A technique for generating regional climate scenarios using a nearest-neighbor algorithm

Abstract

A K-nearest neighbor (K-nn) resampling scheme is presented that simulates daily weather variables, and consequently seasonal climate and spatial and temporal dependencies, at multiple stations in a given region. A strategy is introduced that uses the K-nn algorithm to produce alternative climate data sets conditioned upon hypothetical climate scenarios, e.g., warmer-drier springs, warmer-wetter winters, and so on. This technique allows for the creation of ensembles of climate scenarios that can be used in integrated assessment and water resource management models for addressing the potential impacts of climate change and climate variability. This K-nn algorithm makes use of the Mahalanobis distance as the metric for neighbor selection, as opposed to a Euclidian distance. The advantage of the Mahalanobis distance is that the variables do not have to be standardized nor is there a requirement to preassign weights to variables. The model is applied to two sets of station data in climatologically diverse areas of the United States, including the Rocky Mountains and the north central United States and is shown to reproduce synthetic series that largely preserve important cross correlations and autocorrelations. Likewise, the adapted K-nn algorithm is used to generate alternative climate scenarios based upon prescribed conditioning criteria.