HR: 14:20h
AN: GC43B-03    [Abstracts]
TI: Reconstructing Spatial Patterns of Climate Change During the Last Millennium: The Challenges of Method and Data
AU: * Smerdon, J E
EM: jsmerdon@ldeo.columbia.edu
AF: Lamont-Doherty Earth Observatory of Columbia University, 61 Route 9W P.O. Box 1000, Palisades, NY 10964, United States
AU: Kaplan, A
EM: alexeyk@ldeo.columbia.edu
AF: Lamont-Doherty Earth Observatory of Columbia University, 61 Route 9W P.O. Box 1000, Palisades, NY 10964, United States
AU: Chang, D
EM: dc2199@barnard.edu
AF: Barnard College, 3008 Broadway, New York, NY 10027, United States
AU: Evans, M N
EM: mevans@ltrr.arizona.edu
AF: University of Arizona, 105 W. Stadium, Tucson, AZ 85721, United States
AB: Understanding synoptic patterns of climate change during the last millennium is fundamentally tied to our ability to faithfully reconstruct such patterns from sparse data networks and regression-based climate field reconstruction (CFR) techniques. Here we demonstrate several challenges associated with CFR methods and the consequences for estimates of reconstructed spatial patterns. We focus on two widely applied CFR techniques that have been used to reconstruct temperature and hydrologic variables during the last millennium: regularized expectation maximization (RegEM) and canonical correlation analysis (CCA). These methods are tested using a pseudo-proxy framework that reflects real-world proxy observing sites and is derived from General Circulation Model simulations of the last millennium. We demonstrate that that the skill of reconstructions varies spatially and can be quite poor over important regions such as the equatorial Pacific Ocean. These regions of reduced skill appear most notably associated with the distribution of the proxy network. Widespread variance losses are also noted, resulting in significant underestimates of variability in many regions of the reconstructed field. Furthermore, the spectral fidelity of the reconstructed climatic fields is shown to have altered ratios of high (annual to decadal) and low (multi-decadal and lower) frequency variability. Collectively, these characteristics of CFRs should be a fundamental consideration of work that seeks to interpret reconstructed spatial patterns of climate change during the last millennium.
DE: 1600 GLOBAL CHANGE
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1626 Global climate models (3337, 4928)
DE: 1637 Regional climate change
DE: 3252 Spatial analysis (0500)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting