Large-scale features of Pliocene climate: results from the Pliocene Model Intercomparison Project

Haywood, Alan, Hill, Daniel, Dolan, Aisling, Otto-Bliesner, Bette, Bragg, Frances, Chan, Wing-Le, Chandler, Mark, Contoux, Camille, Dowsett, Harry, Jost, Anne, Kamae, Youichi, Lohmann, Gerrit, Lunt, Daniel, Abe-Ouchi, Ayako, Pickering, Steven, Ramstein, Gilles, Rosenbloom, Nan, Salzmann, Ulrich, Sohl, Linda, Stepanek, Christian, Ueda, Hiroaki, Yan, Qing and Zhang, Zhongshi (2013) Large-scale features of Pliocene climate: results from the Pliocene Model Intercomparison Project. Climate of the Past, 9 (1). pp. 191-209. ISSN 1814-9332

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Abstract

Climate and environments of the mid-Pliocene warm period (3.264 to 3.025 Ma) have been extensively studied. Whilst numerical models have shed light on the nature of climate at the time, uncertainties in their predictions have not been systematically examined. The Pliocene Model Intercomparison Project quantifies uncertainties in model outputs through a coordinated multi-model and multi-model/data intercomparison. Whilst commonalities in model outputs for the Pliocene are clearly evident, we show substantial variation in the sensitivity of models to the implementation of Pliocene boundary conditions. Models appear able to reproduce many regional changes in temperature reconstructed from geological proxies. However, data/model comparison highlights that models potentially underestimate polar amplification. To assert this conclusion with greater confidence, limitations in the time-averaged proxy data currently available must be addressed. Furthermore, sensitivity tests exploring the known unknowns in modelling Pliocene climate specifically relevant to the high latitudes are essential (e.g. palaeogeography, gateways, orbital forcing and trace gasses). Estimates of longer-term sensitivity to CO2 (also known as Earth System Sensitivity; ESS), support previous work suggesting that ESS is greater than Climate Sensitivity (CS), and suggest that the ratio of ESS to CS is between 1 and 2, with a "best" estimate of 1.5.

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