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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 2
Clim. Past, 9, 859–870, 2013
https://doi.org/10.5194/cp-9-859-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Clim. Past, 9, 859–870, 2013
https://doi.org/10.5194/cp-9-859-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Mar 2013

Research article | 22 Mar 2013

Mismatch between the depth habitat of planktonic foraminifera and the calibration depth of SST transfer functions may bias reconstructions

R. J. Telford1,2, C. Li2,3, and M. Kucera4 R. J. Telford et al.
  • 1Department of Biology, University of Bergen, Thormøhlensgate 53A, 5006 Bergen, Norway
  • 2Bjerknes Centre for Climate Research, Allégaten 55, 5007 Bergen, Norway
  • 3Geophysical Institute, University of Bergen, Allégaten 70, 5008 Bergen, Norway
  • 4MARUM & Fachbereich Geowissenschaften, Universität Bremen, Leobener Strasse, 28359 Bremen, Germany

Abstract. We demonstrate that the temperature signal in the planktonic foraminifera assemblage data from the North Atlantic typically does not originate from near-surface waters and argue that this has the potential to bias sea surface temperature reconstructions using transfer functions calibrated against near-surface temperatures if the thermal structure of the upper few hundred metres of ocean changes over time. CMIP5 climate models indicate that ocean thermal structure in the North Atlantic changed between the Last Glacial Maximum (LGM) and the pre-industrial (PI), with some regions, mainly in the tropics, of the LGM ocean lacking good thermal analogues in the PI.

Transfer functions calibrated against different depths reconstruct a marked subsurface cooling in parts of the tropical North Atlantic during the last glacial, in contrast to previous studies that reconstruct only a modest cooling. These possible biases in temperature reconstructions may affect estimates of climate sensitivity based on the difference between LGM and pre-industrial climate. Quantifying these biases has the potential to alter our understanding of LGM climate and improve estimates of climate sensitivity.

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