During the 1997/98 El Niño event, a Northern Illinois University (NIU) faculty member and a group of undergraduate meteorology students interacted with the university's heating plant manager to determine whether climate information and forecast tools could assist him with NIU's natural gas purchase decisions each fall. Based on the El Niño-driven temperature forecasts and information developed by the faculty-directed student group, which indicated that northern Illinois would experience a warmer than average winter (December through March), the manager chose the option to ride the market on a continuous basis, buying incrementally to reduce total natural gas expenditures, rather than lock into a fixed price.

To aid this annual decision process, winter El Niño–Southern Oscillation (ENSO) classifications, based on sea surface temperature (SST) data measured in the Niño-3 region, were analyzed to determine whether relationships existed between local mean winter temperature and the ENSO phenomena during the 1951–97 period. An SST ENSO model, which uses the past winter's ENSO state along with the SST trends from April through September, was developed to predict the upcoming winter's temperatures (above, near, or below average). The model predicted an 83% chance of a winter experiencing average to below-average temperatures following an El Niño winter, regardless of trend. Those winters following a non-ENSO winter with steady or increasing SST trends experienced average or above-average temperatures 79% of the time. These results supported the manager's natural gas decision, which in turn saved NIU approximately $500,000 and aided in the university's decision to hire a full-time applied meteorologist to provide advice on a continuing basis.

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