Assessing confidence in Pliocene sea surface temperatures to evaluate predictive models

Abstract

In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.3–3.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history. This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

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Figure 1: Confidence estimates and temperature anomalies from PRISM dataset.
Figure 2: Data and model mean annual temperature profiles.
Figure 3: Data–model comparison.

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Acknowledgements

H.J.D., M.M.R., D.K.S. and K.M.F. acknowledge the continued support of the US Geological Survey Climate and Land Use Change Research and Development Program; C.R.R. thanks the US Geological Survey Mendenhall Postdoctoral Fellowship Program; H.J.D., M.M.R. and M.A.C. thank the US Geological Survey John Wesley Powell Center for Analysis and Synthesis; M.A.C. acknowledges support from the NASA Climate Modelling Program. A.M.H. acknowledges financial support from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 278636. D.J.H. undertook his contribution as part of a Leverhulme Early Career Fellowship (ECF-2011-205), co-financially supported by the British Geological Survey and National Centre for Atmospheric Science; D.J.L. and F.J.B. acknowledge the Natural Environment Research Council grant NE/H006273/1. B.L.O-B. and N.A.R. recognize the National Center for Atmospheric Research is sponsored by the US National Science Foundation and computing resources were provided by the Climate Simulation Laboratory at the National Center for Atmospheric Research’s Computational and Information Systems Laboratory sponsored by the National Science Foundation and other agencies. This research used samples and/or data provided by the Integrated Ocean Drilling Program. This is a product of the PRISM Project and the Pliocene Model Intercomparison Project.

Author information

H.J.D., M.M.R., K.M.F. and D.K.S. developed the mid-Piacenzian SST-verification data set and carried out the data–model comparison. H.J.D., M.M.R. and C.R.R. designed and completed the confidence assessments. D.J.L., F.J.B., A.M.H., M.A.C., W-L.C., A.A-O., B.L.O-B. and N.A.R. carried out the general circulation model simulations and analysis. H.J.D., M.M.R., A.M.H. and B.L.O-B. were involved in the study design. A.M.H., D.J.H. and A.M.D. carried out the comparison of model performance for the pre-industrial versus Pliocene. All authors discussed the results and commented on the manuscript.

Correspondence to Harry J. Dowsett.

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Dowsett, H., Robinson, M., Haywood, A. et al. Assessing confidence in Pliocene sea surface temperatures to evaluate predictive models. Nature Clim Change 2, 365–371 (2012). https://doi.org/10.1038/nclimate1455

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