The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks
Open accessAbstract
In the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations. When forced by RCP8.5 CO2 emissions, models simulate a large spread in atmospheric CO2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m−2) when driven by CO2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO2, with the present-day biases reasonably well correlated with future atmospheric concentrations’ departure from the prescribed concentration. The uncertainty in CO2 projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO2 concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO2 emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks.
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Citations
Cites
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Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
The next generation of scenarios for climate change research and assessment
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., et al. (2010) · Nature · doi:10.1038/nature08823 · Cited by 6,467
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
Self-citationThe use of the multi-model ensemble in probabilistic climate projections
Tebaldi, C., & Knutti, R. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2076 · Cited by 1,862
Self-citationRobustness and uncertainties in the new CMIP5 climate model projections
Knutti, R., & Sedláček, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1716 · Cited by 1,569
Self-citationHURTT, G. C., FROLKING, S., FEARON, M. G., MOORE, B., SHEVLIAKOVA, E., MALYSHEV, S., PACALA, S. W., & HOUGHTON, R. A. (2006) · Global Change Biology · doi:10.1111/j.1365-2486.2006.01150.x · Cited by 531
Cited by
Deep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673
The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., et al. (2020) · Geoscientific Model Development · doi:10.5194/gmd-13-3571-2020 · Cited by 1,815
“The calibrated carbon cycle of MAGICC has previously been shown to reflect the CMIP5 ESM response range well (Friedlingstein et al., 2014).”
MethodSelf-citationMultiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., et al. (2013) · Biogeosciences · doi:10.5194/bg-10-6225-2013 · Cited by 1,649
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R., McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., et al. (2017) · Water Resources Research · doi:10.1002/2016wr020175 · Cited by 991
Microbial carbon use efficiency promotes global soil carbon storage
Tao, F., Huang, Y., Hungate, B. A., Manzoni, S., Frey, S. D., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Jiang, L., et al. (2023) · Nature · doi:10.1038/s41586-023-06042-3 · Cited by 938
Metadata
- Authors
- Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., & Knutti, R.
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2014-01-15
- Volume
- 27
- Issue
- 2
- Pages
- 511-526
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2048217351
- Cited by
- 1,064
- References
- 6
- FWCI
- 44.57
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., & Knutti, R. (2014). Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. Journal of Climate. https://doi.org/10.1175/jcli-d-12-00579.1
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