2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)

Paper No. 264-1
Presentation Time: 8:00 AM


BADR, Hamada S., DELUCA, Nicole M., LEVIN, Naomi E. and ZAITCHIK, Benjamin F., Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218, badr@jhu.edu

Many studies have documented dramatic climatic and environmental changes that have affected Africa over historical and geologic timescales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from paleorecords or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, we present an application of the recently released Hierarchical Climate Regionalization (HiClimR) package in R to regionalize Africa using global climate model (GCM) simulations performed under the Paleoclimate Model Intercomparison Project (PMIP). We apply HiClimR to regionalize Africa based on precipitation variability using CCSM4 data at three different periods: the mid-Pliocene (3.3-3.0 Ma), historical (1850-2005), and future (2006-2100). We discuss the changes in spatial patterns of interannual precipitation variability (represented as shifts in regions) between the different time periods. Climate regionalization studies for Africa have the potential to expand the utility of paleoclimate records because they make it possible to explore hypotheses about why records from different parts of Africa may be in or out of synch with each other. Moreover, this work can be useful for both i) teleconnections analysis, by building statistical models for selected regions as a function of large-scale drivers of variability and ii) the assessment of GCMs for paleoclimate studies, either by comparing the regions generated from GCM simulations with observation-based regions or by identifying regions that match our understanding of modern-day regions of coherent variability). While this presentation focuses on regionalization of GCM output, HiClimR is just as easily applied to observational data; any set of overlapping time series records can be regionalized into zones of coherent variability.