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The development of BHMs for spatially-explicit climate reconstruction has emerged as a powerful new method for this purpose. BHMs have a theoretical advantage over traditional linear subspace-based (EOF) methods of CFR, because the Bayesian posterior distribution of the reconstructed climate, once estimated, can be directly sampled to yield complete uncertainty estimates of the reconstructed climate, along with central tendency, or expected value estimates.
A primary goal of the workshop is to bring together reconstruction experts who currently employ linear-based models for CFR in North America and other regions of the world, and provide an in-depth exposure to the theory and application of BHMs for climate reconstruction.
A second important purpose of the workshop will be to explore these in relation to the BHM methods, to examine the extent to which the traditional methods offer equally or near-equally valid ways to characterize reconstruction uncertainties in practice. This latter goal is important for several reasons, two of which are the computational complexity and time cost of implementing BHM approaches, along with any potential effects that may be caused by relative instability of estimated parameters. A key outcome of the workshop will be a paper addressing these issues for publication in the primary climate and/or statistics literature.
Download the workshop schedule