The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Uncertainty in weather and climate prediction
Open accessAbstract
Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation.
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Citations
Cites
Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP Model Intercomparison
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V., Cadule, P., Doney, S., Eby, M., Fung, I., et al. (2006) · Journal of Climate · doi:10.1175/jcli3800.1 · Cited by 2,902
Quantification of modelling uncertainties in a large ensemble of climate change simulations.
Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M., & Stainforth, D. A. (2004) · Nature · doi:10.1038/nature02771 · Cited by 1,655
The role of the basic state in the ENSO–monsoon relationship and implications for predictability
Turner, A. G., Inness, P. M., & Slingo, J. M. (2005) · Quarterly Journal of the Royal Meteorological Society · doi:10.1256/qj.04.70 · Cited by 139
Self-citation
Cited by
The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A., & Brunet, G. (2015) · Nature · doi:10.1038/nature14956 · Cited by 2,729
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
A signal-to-noise paradox in climate science
Scaife, A. A., & Smith, D. (2018) · npj Climate and Atmospheric Science · doi:10.1038/s41612-018-0038-4 · Cited by 411
Addressing agricultural nitrogen losses in a changing climate
Bowles, T. M., Atallah, S. S., Campbell, E. E., Gaudin, A. C. M., Wieder, W. R., & Grandy, A. S. (2018) · Nature Sustainability · doi:10.1038/s41893-018-0106-0 · Cited by 336
Machine learning for the physics of climate
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00776-3 · Cited by 79
Metadata
- Authors
- Slingo, J., & Palmer, T.
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2011-12-13
- Volume
- 369
- Issue
- 1956
- Pages
- 4751-4767
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- OpenAlex
- W2121593555
- Cited by
- 344
- References
- 3
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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