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Once there is some trust in a model, how well can the climate be predicted? How are models used to generate predictions and projections? The description and quantification of uncertainty is an element of scientific research. To be useful, predictions and projections require a description and estimate of uncertainty. Key uncertainties in model predictions and projections are discussed. Then methods of computational experimentation to understand uncertainty are described. Attention is given to ensembles of multiple simulations and multiple models. As an example and application of these ideas, the development of scenarios for future greenhouse gas emissions is examined.
The basic understanding of what will happen to global temperature has not changed much (between 1 and 4 °C global temperature change for doubling CO 2 concentrations in 30 years; see Charney, J. G. (1979). Carbon Dioxide and Climate: A Scientific Assessment. Washington, DC: National Academies Press; and Houghton, J. T., Meira Filho, L. G., Callander, B. A., Harris, N., Kattenberg, A., & K. Maskell, eds. (1996). Climate Change 1995: The Science of Climate Change. Cambridge, UK: Cambridge University Press.
For an example of a forecast nearly 30 years old, see Hansen, J., Fung, I., Lacis, A., Rind, D., Lebedeff, S., Ruedy, R., et al. (1988). “Global Climate Changes as Forecast by Goddard Institute for Space Studies Three‐Dimensional Model.” Journal of Geophysical Research: Atmospheres, 93(D8): 9341–9364.
For an overview of the earth’s energy budget, see Chap. 2 of Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2009). “Earth’s Global Energy Budget.” Bulletin of the American Meteorological Society, 90(3): 311–323.
For a good popular treatment of the signal and noise in statistics and in climate modeling, see Silver, N. (2014). The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t. New York: Penguin. Chapter 12 is all about climate science and climate prediction.
The definitions follow Hawkins, E., & Sutton, R. (2009). “The Potential to Narrow Uncertainty in Regional Climate Predictions.” Bulletin of the American Meteorological Society, 90(8): 1095–1107.
The tropical Pacific temperatures are due to El Niño; see Chap. 8.
CFC-12 data from McCulloch, A., Midgley, P. M., & Ashford, P. (2003). “Releases of Refrigerant Gases (CFC-12, HCFC-22 and HFC-134a) to the Atmosphere.” Atmospheric Environment, 37(7): 889–902.
For a background on scenario development, consult Nakicenovic, N., & Swart, R., eds. (2000). Special Report on Emissions Scenarios. Cambridge, UK: Cambridge University Press. See also https://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf.
A full description of the RCP scenarios is at Van Vuuren, D. P., et al. (2011). “The Representative Concentration Pathways: An Overview.” Climatic Change, 109: 5–31.
Trenberth et al., “Earth’s Global Energy Budget.”.
Tebaldi, C., & Knutti, R. (2007). “The Use of the Multi-Model Ensemble in Probabilistic Climate Projections.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857): 2053–2075.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A. (2010). “Challenges in Combining Projections From Multiple Climate Models.” Journal of Climate, 23(10): 2739–2758.
Solomon, S., ed. (2007). Climate Change 2007: The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC, Vol. 4. Cambridge, UK: Cambridge University Press.
Knutti, Reto. (2010). “The End of Model Democracy?.” Climatic Change, 102(34): 395–404.
O’Neill, B. C., et al. (2014). “A New Scenario Framework for Climate Change Research: The Concept of Shared Socioeconomic Pathways.” Climatic Change, 122(3): 387–400.
Richard B. Rood
- Springer Berlin Heidelberg
- Chapter 10