final_program_abstracts[1]

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11 IMSC Session Program Quantification of the effect of auto correlation and climate trends on record observations Tuesday - Poster Session 10 Albert R. Boehm Retired Climatologist Consider a set of observations Xi i=1 to N, for example daily temperature readings typically taken at one site at fixed time intervals. But in general could be simultaneous measurements taken at N sites along a line in space. Define a record measurement, Xr as exceeding all prior measurements that is, Xr> Xj for all j
11 IMSC Session Program Assimilation of low-resolution paleo-proxies for spatial reconstruction of North American summertime climatology Tuesday - Poster Session 10 Alicia R. Karspeck, Caspar Ammann and Doug Nychka National Center for Atmospheric Research, USA Unlike high-resolution paleo-proxies that might provide information about the annually resolved climate, low-resolution proxies, such as pollen assemblages, provide information on decadal-scale climate variability. Ideally, reconstructions would use both high- and low-resolution proxies to create reconstructions with corresponding multi-scale uncertainty estimates. Toward this goal, we focus here on the question of how best to assimilate pollen ratios representative of 30-yr average summertime temperatures into a spatial reconstruction of North American surface temperatures. Using an 1150 yr integration of the NCAR Community Climate System Model (CCSM), we form a prior distribution of the seasonal climatology (a variable that is not typically a probabilistic quantity). Although point-proxies of 30 yr means can tell us very little about the instantaneous annual state, we explore here what they may be able to provide in terms of adjustments to the temporally evolving seasonal climatology. Second, we form a statistical autocorrelation model that will allow us to predict how innovations at discrete time points impact temporally distant state variables. A realistic network of proxy locations is used to condition the posterior distribution. We operate within a standard Bayesian framework in which we are able to generate both the expected value and uncertainties associated with the reconstruction. Abstracts 116
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