2170 JOURNAL OF CLIMATE VOLUME 17 The 100 000-Yr Cycle in Tropical SST, Greenhouse Forcing, and Climate Sensitivity
The key scientific uncertainty in the global warming debate is the equilibrium climate sensitivity. Coupled atmosphere–ocean general circulation models predict a wide range of equilibrium climate sensitivities, with a consequently large spread of societal implications. Comparison of models with inst...
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Format: | Text |
Language: | English |
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2003
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Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.420.1554 http://www.seas.harvard.edu/climate/seminars/pdfs/Lea_JofClimate2004.pdf |
Summary: | The key scientific uncertainty in the global warming debate is the equilibrium climate sensitivity. Coupled atmosphere–ocean general circulation models predict a wide range of equilibrium climate sensitivities, with a consequently large spread of societal implications. Comparison of models with instrumental data has not been able to reduce the uncertainty in climate sensitivity. An alternative way to gauge equilibrium climate sensitivity is to use paleoclimatic data. Two recent advances, the development and application of proxy recorders of tropical sea surface temperature (SST) and the synchronization of the deep-sea and Antarctic ice-core time scales, make it possible to directly relate past changes in tropical SST to atmospheric carbon dioxide (CO 2) levels. The strong correspondence of a proxy SST record from the eastern equatorial Pacific and the Vostok CO 2 record suggests that varying atmospheric carbon dioxide is the dominant control on tropical climate on orbital time scales. This effect is especially pronounced at the 100 000-yr cycle. Calibration of the CO 2 influence via tropical SST variability indicates a tropical climate sensitivity of 4.4�–5.6�C (errors estimated at � 1.0�C) for a doubling of atmospheric CO2 concentration. This result suggests that the equilibrium response of tropical climate to atmospheric CO2 changes is likely to be similar to the upper end of available global predictions from coupled models. 1. |
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