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Probabilistic Forecast for Twenty-First-Century Climate Based on Uncertainties in Emissions (Without Policy) and Climate Parameters
Open accessAbstract
The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios. However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine the input climate parameters, the median projected warming at the end of the twenty-first century is only 4.1°C. Nevertheless, all ensembles of the simulations discussed here produce a much smaller probability of warming less than 2.4°C than implied by the lower bound of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) projected likely range for the A1FI scenario, which has forcing very similar to the median projection in this study. The probability distribution for the surface warming produced by this analysis is more symmetric than the distribution assumed by the IPCC because of a different feedback between the climate and the carbon cycle, resulting from the inclusion in this model of the carbon–nitrogen interaction in the terrestrial ecosystem.
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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
Probabilistic climate change projections using neural networks
Knutti, R., Stocker, T. F., Joos, F., & Plattner, G.-K. (2003) · Climate Dynamics · doi:10.1007/s00382-003-0345-1 · Cited by 197
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Spiegelhalter, D., Pearson, M., & Short, I. (2011) · Science · doi:10.1126/science.1191181 · Cited by 682
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Brasseur, G. P., Gupta, M., Anderson, B. E., Balasubramanian, S., Barrett, S., Duda, D., Fleming, G., Forster, P. M., Fuglestvedt, J., Gettelman, A., et al. (2016) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-13-00089.1 · Cited by 139
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Metadata
- Authors
- Sokolov, A. P., Stone, P. H., Forest, C. E., Prinn, R., Sarofim, M. C., Webster, M., Paltsev, S., Schlosser, C. A., Kicklighter, D., Dutkiewicz, S., Reilly, J., Wang, C., Felzer, B., Melillo, J. M., & Jacoby, H. D.
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2009-10-01
- Volume
- 22
- Issue
- 19
- Pages
- 5175-5204
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2167540586
- Cited by
- 247
- References
- 2
- FWCI
- 29.08
- Type
- journal article
- Language
- en
- Open access
- bronze
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