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Predictability in the Midst of Chaos: A Scientific Basis for Climate Forecasting
Abstract
The Earth's atmosphere is generally considered to be an example of a chaotic system that is sensitively dependent on initial conditions. It is shown here that certain regions of the atmosphere are an exception. Wind patterns and rainfall in certain regions of the tropics are so strongly determined by the temperature of the underlying sea surface that they do not show sensitive dependence on the initial conditions of the atmosphere. Therefore, it should be possible to predict the large-scale tropical circulation and rainfall for as long as the ocean temperature can be predicted. If changes in tropical Pacific sea-surface temperature are quite large, even the extratropical circulation over some regions, especially over the Pacific–North American sector, is predictable.
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Citations
Cited by
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Palmer, T. N., Alessandri, A., Andersen, U., Cantelaube, P., Davey, M., Délécluse, P., Déqué, M., Díez, E., Doblas-Reyes, F. J., Feddersen, H., et al. (2004) · Bulletin of the American Meteorological Society · doi:10.1175/bams-85-6-853 · Cited by 959
The construction of global warming and the politics of science
Demeritt, D. (2001) · Annals of the Association of American Geographers · doi:10.1111/0004-5608.00245 · Cited by 729
Wang, B., Xiang, B., & Lee, J.-Y. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214626110 · Cited by 658
Seasonal climate predictability and forecasting: status and prospects
Doblas‐Reyes, F. J., García‐Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. L. (2013) · WIREs Climate Change · doi:10.1002/wcc.217 · Cited by 478
Metadata
- Authors
- Shukla, J.
- Venue
- Science
- Published
- 1998-10-23
- Volume
- 282
- Issue
- 5389
- Pages
- 728-731
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1979890053
- Cited by
- 488
- References
- 0
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shukla, J. (1998). Predictability in the Midst of Chaos: A Scientific Basis for Climate Forecasting. Science. https://doi.org/10.1126/science.282.5389.728
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
