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What might we learn from climate forecasts?
Open accessAbstract
Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them “better,” estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as “chaos” prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system.
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Cited by
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ARAUJO, M., & NEW, M. (2007) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2006.09.010 · Cited by 3,640
Constraints on future changes in climate and the hydrologic cycle
Allen, M. R., & Ingram, W. J. (2002) · Nature · doi:10.1038/nature01092 · Cited by 3,009
Future climate risk from compound events
Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., et al. (2018) · Nature Climate Change · doi:10.1038/s41558-018-0156-3 · Cited by 2,527
Challenges in combining projections from multiple climate models.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A. (2010) · Journal of Climate · doi:10.1175/2009jcli3361.1 · Cited by 1,270
Confidence, uncertainty and decision-support relevance in climate predictions.
Stainforth, D. A., Allen, M. R., Tredger, E. R., & Smith, L. A. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2074 · Cited by 464
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Metadata
- Authors
- Smith, L. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2002-02-19
- Volume
- 99
- Issue
- suppl_1
- Pages
- 2487-2492
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2013290333
- Cited by
- 208
- References
- 0
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- green
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
