Dendroclimatic Reconstruction with Time Varying Predictor Subsets of Tree Indices

Abstract

Tree-ring site chronologies, the predictors for most dendroclimatic reconstructions, are essentially mean-value functions with a time varying sample size (number of trees) and sample composition. Because reconstruction models are calibrated and verified on the most recent, best-replicated part of the chronologies, regression and verification statistics can be misleading as indicators of long-term reconstruction accuracy. A new reconstruction method is described that circumvents the use of site chronologies and instead derives predictor variables from indices of individual trees. Separate regression models are estimated and cross validated for various time segments of the tree-ring record, depending on the trees available at the time. This approach allows the reconstruction to extend to the first year covered by any tree in the network and yields direct evaluation of the change in reconstruction accuracy with tree-ring sample composition. The method includes two regression stages. The first is to separately deconvolve the local climate signal for individual trees, and the second is to weight the deconvolved signals into estimates of the climatic variable to be reconstructed. The method is illustrated in an application of precipitation and tree-ring data for the San Pedro River Basin in southeastern Arizona. Extensions to larger-scale problems and spatial reconstruction are suggested.