HR: 14:00h
AN: GC43B-02    [Abstracts]
TI: A new approach to global climate reconstructions of the last 1000 years
AU: Barsugli, J
EM: Joseph.J.Barsugli@noaa.gov
AF: Climate Diagnostics Center/CIRES/University of Colorado and Physical Sciences Division/ESRL/NOAA, R/PSD1, 325 Broadway, Boulder, CO 80305,
AU: * Sardeshmukh, P D
EM: Prashant.D.Sardeshmukh@noaa.gov
AF: Climate Diagnostics Center/CIRES/University of Colorado and Physical Sciences Division/ESRL/NOAA, R/PSD1, 325 Broadway, Boulder, CO 80305,
AU: Webb, R S
EM: Robert.S.Webb@noaa.gov
AF: Climate Diagnostics Center/CIRES/University of Colorado and Physical Sciences Division/ESRL/NOAA, R/PSD1, 325 Broadway, Boulder, CO 80305,
AU: Shin, S
EM: Sangik.Shin@noaa.gov
AF: Climate Diagnostics Center/CIRES/University of Colorado and Physical Sciences Division/ESRL/NOAA, R/PSD1, 325 Broadway, Boulder, CO 80305,
AU: Kilbourne, H
EM: Hali.Kilbourne@noaa.gov
AF: Climate Diagnostics Center/CIRES/University of Colorado and Physical Sciences Division/ESRL/NOAA, R/PSD1, 325 Broadway, Boulder, CO 80305,
AB: Efforts to reconstruct global gridded climate datasets from sparse paleoclimatic observations face special challenges. Extrapolations to data void regions based on observed present-day linear correlations (i.e. modern analogs) are too simple for this purpose, and are not strictly justified in a different and evolving climate. Modern data assimilation methods used for generating the "Reanalysis" datasets of the past half-century are also inappropriate for several reasons, and would require expensive re-tuning to assimilate the much sparser and temporally lower resolution paleoclimatic observations. Most importantly, such methods would make reconstructions of the key tropical SST fields problematic. In this talk, we will explore a different approach to the assimilation of paleoclimatic observations, in the standard framework of modern "3D-VAR" data assimilation methods but uniquely tailored to the assimilation of pre- industrial observations. The basic idea is to exploit important evidence from recent studies that global atmospheric climate changes (including those ultimately forced by radiative forcing changes) are strongly constrained by global SST changes, and the global response to global SST changes is well approximated by the linear response to tropical SST changes in a relatively low-dimensional dual space of tropical SST and global climate anomaly patterns. This relationship may be formally expressed as y = Gx, where x is the anomalous tropical SST field, y is the global climate response field, and G is a linear operator. We have found that G can be well approximated by retaining as few as 5 terms in its singular vector expansion series, representing contributions from optimal forcing/response singular vector pairs in decreasing order of importance. Our proposed 3D-Var approach to paleoclimatic data assimilation will amount to estimating a mean climate state for each 50-yr period in the last millennium that minimizes deviations from both a "first guess" mean state and the available proxy-based observations for that 50-yr period, taking into account the expected errors in each, while also maintaining the y=Gx relationship. The G operator appropriate to the pre-industrial climate could be estimated by forcing an atmospheric GCM coupled a mixed layer ocean with pre-industrial radiative forcings, and then determining its global response to prescribed localized SST perturbations on a regular tropical grid. The same GCM could also be used to provide a better "first guess" (as well as its expected error) for each 50-yr period than the modern climate as the first guess. Additionally, such a 3D-Var approach would account for errors associated with both the local and proxy nature of the paleoclimatic observations in a flexible and straightforward manner, and also incorporate "forward" multivariate models of those proxy observations that are not related solely to either local temperature or local precipitation. One could thus assimilate all available continental and marine paleoclimatic observations for the last 1000 years to produce a 1000-yr time series of global gridded 3- dimensional atmospheric and SST fields at 50-yr temporal resolution and a spatial resolution consistent with that of our reduced dimensional space. One could also provide error estimates of the reconstructions as a natural by- product of the analysis.
DE: 1620 Climate dynamics (0429, 3309)
DE: 1626 Global climate models (3337, 4928)
DE: 1635 Oceans (1616, 3305, 4215, 4513)
DE: 3305 Climate change and variability (1616, 1635, 3309, 4215, 4513)
DE: 4900 PALEOCEANOGRAPHY (0473, 3344)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting