Showing papers by "David J. Erickson published in 2005"
TL;DR: In this article, a quantitative statistical clustering technique called Multivariate Spatio-Temporal Clustering (MSTC) was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM).
Abstract: Changes in Earth's climate in response to atmospheric green- house gas buildup impact the health of terrestrial ecosystems and the hydro- logic cycle. The environmental conditions influential to plant and animal life are often mapped as ecoregions, which are land areas having similar combi- nations of environmental characteristics. This idea is extended to establish regions of similarity with respect to climatic characteristics that evolve through time using a quantitative statistical clustering technique called Multivariate Spatio-Temporal Clustering (MSTC). MSTC was applied to the monthly time series output from a fully coupled general circulation model (GCM) called the Parallel Climate Model (PCM). Results from an ensemble of five 99-yr Busi-
63 citations
21 Aug 2005
TL;DR: The multivariate spatio-temporal dependence between extreme values and anomalies in highly nonlinear or stochastic systems is an emerging research area in theoretical statistics, with limited development in application areas and for massive or disparate space-time data as discussed by the authors.
Abstract: This paper discusses multivariate spatio-temporal dependence between extremes or abrupt change and unusual values or anomalies in the context of climate dynamics and climate change In climate, as in many other applications, anomalies (or extremes) in one variable like sea surface temperature may be a precursor for extremes (or abrupt change) in another variable like regional precipitation In addition, this multivariate dependence may be spatially or temporally lagged, owing to climate “teleconnections” However, the anomalies may not be easily detectable and their dependence with extremes and rapid change may be difficult to quantify This paper provides a brief review of the literature, which is followed by a description of critical gaps, both in the data or computational sciences as well as in the climate sciences The quantification and visualization of multivariate dependence among extreme values and anomalies in highly nonlinear or stochastic systems is an emerging research area in theoretical statistics, with limited development in application areas and/or for massive or disparate space-time data Further development is needed in these areas for multiple domains ranging from climate sciences and geography to sensor networks and national security
3 citations