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Speaker:
Murali Haran, Ph.D.
Assistant Professor
Department of Statistics
Penn State University
"Inferring Likelihoods and Climate System Characteristics from Climate Models and Spatio-temporal Tracer Data"
What is the risk of human-induced abrupt climate change? We study the risk of an abrupt change in the North Atlantic Meridional Overturning Circulation (MOC). The MOC is part of the ocean circulation conveyor belt; its collapse would have major impacts on global climate, ecosystems and human societies. To predict the behavior of the MOC, it is critical to have good estimates of the true value of certain climate system parameters. Since these climate parameters are very difficult to measure directly, we have to infer their values based on two sources of information: (i) Spatiotemporal observations of ‘tracers’ that indirectly provide information about these parameters, and (ii) output from complex climate computer models run at several climate parameter settings. These climate models are computationally expensive and can take weeks or months to run at each setting. I will discuss an inferential approach that uses Gaussian processes to emulate the climate models, thereby establishing a connection between the climate parameters and the data. Using a space-time model, it is then possible to carry out statistical inference for the climate parameters, while accounting for various sources of variability and dependence. I will describe how our methods propose to address a few of the many challenges involved in this research, including parameter identifiability issues and computational obstacles posed by the size of the data.
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