While significant research has been performed in predicting winter snowpack behavior, maximums and extent, no efforts focused on predicting large-scale spring snowpack behavior have produced successful results. Increasing sensitivity to snowpack changes in the areas of water supply, energy production, agriculture, transportation, tourism and safety are making seasonal prediction of snowpack particularly important. The known breakdown of the wintertime relationship between tropospheric dynamics and snow characteristics indicates the need to explore new approaches to seasonal snowpack forecasts for the spring melt season. To examine possible new methods, Northern Hemisphere snow water equivalent and snow cover data from 1980-2004 are used in correlation analysis with traditional climate indices as well as newly defined sea surface temperature and sea ice regions. Additionally, large scale continental and latitude divisions are applied to the snow variables and the impact of ENSO is incorporated into the analysis. Results suggest the following: 1) Both sea ice and sea surface temperatures show promise as seasonal predictors for snowpack; 2) ENSO plays a critical role even though it is represented through indirect relationships; 3) Predicting spring snowpack behavior is feasible.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/16132 |
| Date | 10 July 2007 |
| Creators | Jelinek, Mark Thomas |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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