Nonlinear dynamics and the great salt lake: A predictable indicator of regional climate

Henry D.I. Abarbanel, Upmanu Lall, Young Il Moon, Michael E. Mann, Taiye Sangoyomi

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Using methods from nonlinear dynamics, we examine a long climatological record of measurements of the volume of the Great Salt Lake in Utah. These observations, recorded every 15 days since 1847, provide direct insight into the effect of large-scale atmospheric motions in climatological studies. The lake drains nearly 100,000 km2, and it thus acts as a spatial filter for the finest degrees of freedom for climate. In filtering out a very large number of atmospheric and climatological motions, it reduces its complexity but retains its effectiveness as a climate sensing system. We demonstrate that there are four degrees of freedom active in the Great Salt Lake volume record, that these data reside on a strange attractor of dimension slightly larger than three, and that these data are predictable with a horizon of order a few years. We then show that predictive models based on local properties on the attractor perform remarkably well in reproducing the observations when trained on earlier observations. The ability to predict using earlier observations on the attractor suggests very strongly that over the period of the record, the system has been stationary and that it is a record of the natural variation of the climate. If there is anthropomorphic influence leading to changes in climate, this record suggests it has not made its effect measurable in such large-scale integrating observations.

Original languageEnglish (US)
Pages (from-to)655-665
Number of pages11
Issue number7-8
StatePublished - 1996

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • General Energy
  • Pollution
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering
  • Building and Construction
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment
  • Civil and Structural Engineering
  • Modeling and Simulation


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