Articles | Volume 7, issue 1
Clim. Past, 7, 151–159, 2011
https://doi.org/10.5194/cp-7-151-2011
Clim. Past, 7, 151–159, 2011
https://doi.org/10.5194/cp-7-151-2011

Research article 02 Mar 2011

Research article | 02 Mar 2011

Can oceanic paleothermometers reconstruct the Atlantic Multidecadal Oscillation?

D. Heslop1,* and A. Paul2 D. Heslop and A. Paul
  • 1Department of Geosciences, University of Bremen, 28334 Bremen, Germany
  • 2MARUM – Center for Marine Environmental Sciences, University Bremen, 28334 Bremen, Germany
  • *now at: Research School of Earth Sciences, The Australian National University, Canberra ACT 0200, Australia

Abstract. Instrumental records of the North Atlantic sea surface temperature reveal a large-scale low frequency mode of variability that has become known as the Atlantic Multidecadal Oscillation (AMO). Proxy and modelling studies have demonstrated the important consequences of the AMO on other components of the climate system both within and outside the Atlantic region. Over longer time scales, the past behavior of the AMO is predominantly constrained by terrestrial proxies and only a limited number of records are available from the marine realm itself. Here we use an Earth System-Climate Model of intermediate complexity to simulate AMO-type behavior in the Atlantic with a specific focus placed on the ability of ocean paleothermometers to capture the associated surface and subsurface temperature variability. Given their lower prediction errors and annual resolution, coral-based proxies of sea surface temperature appear to be capable of reconstructing the temperature variations associated with the past AMO with an adequate signal-to-noise ratio. In contrast, the relatively high prediction error and low temporal resolution of sediment-based proxies, such as the composition of foraminiferal calcite, limits their ability to produce interpretable records of past temperature anomalies corresponding to AMO activity. Whilst the presented results will inevitably be model-dependent to some degree, the statistical framework is model-independent and can be applied to a wide variety of scenarios.