A Pseudoproxy Evaluation of the CCA and RegEM Methods for Reconstructing Climate Fields of the Last Millennium
- A Pseudoproxy Evaluation of the CCA and RegEM Methods for Reconstructing Climate Fields of the Last Millennium
- Smerdon, Jason E.
Evans, Michael N.
- Lamont-Doherty Earth Observatory
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- Book/Journal Title:
- Journal of Climate
- Canonical correlation analysis (CCA) is evaluated for paleoclimate field reconstructions in the context of pseudoproxy experiments assembled from the millennial integration (850–1999 c.e.) of the National Center for Atmospheric Research Community Climate System Model, version 1.4. A parsimonious method for selecting the order of the CCA model is presented. Results suggest that the method is capable of resolving multiple (3–13) climatic patterns given the estimated proxy observational network and the amount of observational uncertainty. CCA reconstructions are compared to those derived from the regularized expectation maximization method using ridge regression regularization (RegEM-Ridge). CCA and RegEM-Ridge yield similar skill patterns that are characterized by high correlation regions collocated with dense pseudoproxy sampling areas in North America and Europe. Both methods also produce reconstructions characterized by spatially variable warm biases and variance losses, particularly at high pseudoproxy noise levels. RegEM-Ridge in particular is subject to significantly larger variance losses than CCA, even though the spatial correlation patterns of the two methods are comparable. Results collectively indicate the importance of evaluating the field performance of methods that target spatial climate patterns during the last several millennia and indicate that the results of currently available climate field reconstructions should be interpreted carefully.
- Paleoclimate science
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- Suggested Citation:
- Jason E. Smerdon, Alexey Kaplan, Diana Chang, Michael N. Evans, 2010, A Pseudoproxy Evaluation of the CCA and RegEM Methods for Reconstructing Climate Fields of the Last Millennium, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:10872.