James S. Clark, Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon, Jessica Metcalf, Emily Moran, Luke Pangle, and Mike Wolosin
This article focuses on the use of Bayesian methods in assessing the probability of rare climate events, and more specifically the potential collapse of the meridional overturning circulation (MOC) in the Atlantic Ocean. It first provides an overview of climate models and their use to perform climate simulations, drawing attention to uncertainty in climate simulators and the role of data in climate prediction, before describing an experiment that simulates the evolution of the MOC through the twenty-first century. MOC collapse is predicted by the GENIE-1 (Grid Enabled Integrated Earth system model) for some values of the model inputs, and Bayesian emulation is used for collapse probability analysis. Data comprising a sparse time series of five measurements of the MOC from 1957 to 2004 are analysed. The results demonstrate the utility of Bayesian analysis in dealing with uncertainty in complex models, and in particular in quantifying the risk of extreme outcomes.
Alan Gelfand and Sujit K. Sahu
This article discusses the use of Bayesian analysis and methods to analyse the demography of plant populations, and more specifically to estimate the demographic rates of trees and how they respond to environmental variation. It examines data from individual (tree) measurements over an eighteen-year period, including diameter, crown area, maturation status, and survival, and from seed traps, which provide indirect information on fecundity. The multiple data sets are synthesized with a process model where each individual is represented by a multivariate state-space submodel for both continuous (fecundity potential, growth rate, mortality risk, maturation probability) and discrete states (maturation status). The results from plant population demography analysis demonstrate the utility of hierarchical modelling as a mechanism for the synthesis of complex information and interactions.