Exploring Climate Patterns Embedded in Global Climate Change Datasets
Central to the GIS methods being developed is a temporal GIS framework with abilities to represent and analyze geographic dynamics. The research expands upon process-based GIS approach to incorporate representational and analytical needs to characterize spatiotemporal behavior and relationships of pressure and precipitation anomalies. Events of pressure and precipitation anomalies will be modeled as GIS data objects with spatial and temporal dimensions. A set of measures will be developed to quantify shape, growth, movement, internal structure, life cycle, and other spatiotemporal characteristics of these data objects of anomalies. In addition to object characterization, these measures serve as the basis for similarity query and correlation analysis. Similarity query facilitates retrieval of anomaly events that exhibit similar spatiotemporal behaviors, such as similar SO anomalies or similar precipitation anomalies in the three test cases. “An anomaly tree” based on similarity measures can suggest the effects of CO2 emission scenarios on pressure and precipitation distributions, respectively. Complementarily, correlation analysis relates precipitation anomalies and SO pressure anomalies to reveal their spatial and temporal correspondence in answering questions like where, when, and how precipitation anomalies echo SO pressure anomalies. With similarity assessment and correlation analysis, much spatiotemporal information can be revealed from the wealth of CCSM Global Climate Change Datasets.