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Strong temperature gradients in the ice age North Atlantic Ocean revealed by plankton biogeography
Jonkers, L.; Laepple, T.; Rillo, M.C.; Shi, X.; Dolman, A.M.; Lohmann, G.; Paul, A.; Mix, A.; Kucera, M. (2023). Strong temperature gradients in the ice age North Atlantic Ocean revealed by plankton biogeography. Nature Geoscience 16(12): 1114-1119. https://dx.doi.org/10.1038/s41561-023-01328-7
In: Nature Geoscience. Nature Publishing Group: London. ISSN 1752-0894; e-ISSN 1752-0908
Peer reviewed article  
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  • Jonkers, L.
  • Laepple, T.
  • Rillo, M.C.
  • Shi, X.
  • Dolman, A.M.
  • Lohmann, G.
  • Paul, A.
  • Mix, A.
  • Kucera, M.
Abstract
    The cold Last Glacial Maximum, around 20,000 years ago, provides a useful test case for evaluating whether climate models can simulate climate states distinct from the present. However, because of the indirect and uncertain nature of reconstructions of past environmental variables such as sea surface temperature, such evaluation remains ambiguous. Instead, here we evaluate simulations of Last Glacial Maximum climate by relying on the fundamental macroecological principle of decreasing community similarity with increasing thermal distance. Our analysis of planktonic foraminifera species assemblages from 647 sites reveals that the similarity-decay pattern that we obtain when the simulated ice age seawater temperatures are confronted with species assemblages from that time differs from the modern. This inconsistency between the modern temperature dependence of plankton species turnover and the simulations arises because the simulations show globally rather uniform cooling for the Last Glacial Maximum, whereas the species assemblages indicate stronger cooling in the subpolar North Atlantic. The implied steeper thermal gradient in the North Atlantic is more consistent with climate model simulations with a reduced Atlantic meridional overturning circulation. Our approach demonstrates that macroecology can be used to robustly diagnose simulations of past climate and highlights the challenge of correctly resolving the spatial imprint of global change in climate models.
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