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| Titel |
A multi-model assessment of last interglacial temperatures |
| VerfasserIn |
D. J. Lunt, A. Abe-Ouchi, P. Bakker, A. Berger, P. Braconnot, S. Charbit, N. Fischer, N. Herold, J. H. Jungclaus, V. C. Khon, U. Krebs-Kanzow, P. M. Langebroek, G. Lohmann, K. H. Nisancioglu, B. L. Otto-Bliesner, W. Park, M. Pfeiffer, S. J. Phipps, M. Prange, R. Rachmayani, H. Renssen, N. Rosenbloom, B. Schneider, E. J. Stone, K. Takahashi, W. Wei, Q. Yin, Z. S. Zhang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1814-9324
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| Digitales Dokument |
URL |
| Erschienen |
In: Climate of the Past ; 9, no. 2 ; Nr. 9, no. 2 (2013-03-14), S.699-717 |
| Datensatznummer |
250018015
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| Publikation (Nr.) |
 copernicus.org/cp-9-699-2013.pdf |
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| Zusammenfassung |
| The last interglaciation (~130 to 116 ka) is a time period with a
strong astronomically induced seasonal forcing of insolation compared to
the present. Proxy records indicate a significantly different climate to that of
the modern, in particular Arctic summer warming and higher eustatic sea
level. Because the forcings are relatively well constrained, it provides an
opportunity to test numerical models which are used for future climate
prediction. In this paper we compile a set of climate model simulations of
the early last interglaciation (130 to 125 ka), encompassing a range of
model complexities. We compare the simulations to each other and to a
recently published compilation of last interglacial temperature estimates. We
show that the annual mean response of the models is rather small, with no
clear signal in many regions. However, the seasonal response is more robust,
and there is significant agreement amongst models as to the regions of
warming vs cooling. However, the quantitative agreement of the model
simulations with data is poor, with the models in general underestimating the
magnitude of response seen in the proxies. Taking possible seasonal biases in
the proxies into account improves the agreement, but only marginally.
However, a lack of uncertainty estimates in the data does not allow us to
draw firm conclusions. Instead, this paper points to several ways in which
both modelling and data could be improved, to allow a more robust model–data
comparison. |
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