DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

Daniel J. Lunt1, Fran Bragg1, Wing-Le Chan2, David K. Hutchinson3, Jean-Baptiste Ladant4, Polina Morozova5, Igor Niezgodzki6,7, Sebastian Steinig1, Zhongshi Zhang8,9, Jiang Zhu4, Ayako Abe-Ouchi2, Eleni Anagnostou10, Agatha M. de Boer3, Helen K. Coxall3, Yannick Donnadieu11, Gavin Foster12, Gordon N. Inglis12, Gregor Knorr6, Petra M. Langebroek8, Caroline H. Lear13, Gerrit Lohmann6, Christopher J. Poulsen4, Pierre Sepulchre14, Jessica E. Tierney15, Paul J. Valdes1, Evgeny M. Volodin16, Tom Dunkley Jones17, Christopher J. Hollis18, Matthew Huber19, and Bette L. Otto-Bliesner20

1School of Geographical Sciences, University of Bristol, Bristol, UK
2Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan
3Department of Geological Sciences, Stockholm University, Stockholm, Sweden
4Department of Earth and Environmental Science, University of Michigan, Ann Arbor, USA
5Institute of Geography, Russian Academy of Sciences, Moscow, Russia
6Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
7ING PAN – Institute of Geological Sciences, Polish Academy of Sciences, Research Center in Kraków, Biogeosystem Modelling Group, Kraków, Poland
8NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
9Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, China
10GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
11Aix Marseille Univ, CNRS, IRD, INRA, Coll France, CEREGE, Aix-en-Provence, France
12School of Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UK
13School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK
14Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
15Department of Geosciences, University of Arizona, Tucson, USA
16Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia
17School of Geography, Earth and Environmental Sciences, Birmingham University, Birmingham, UK
18Surface Geosciences, GNS Science, Lower Hutt, New Zealand
19Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, USA
20Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USA

https://doi.org/10.5194/cp-17-203-2021

 

Abstract

We present results from an ensemble of eight climate models, each of which has carried out simulations of the early Eocene climate optimum (EECO, ∼ 50 million years ago). These simulations have been carried out in the framework of the Deep-Time Model Intercomparison Project (DeepMIP; http://www.deepmip.org, last access: 10 January 2021); thus, all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO2 boundary conditions contribute between 3 and 5 ∘C to Eocene warmth. Compared with results from previous studies, the DeepMIP simulations generally show a reduced spread of the global mean surface temperature response across the ensemble for a given atmospheric CO2 concentration as well as an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with the preindustrial period mostly arises from decreases in emissivity due to the elevated CO2 concentration (and associated water vapour and long-wave cloud feedbacks), whereas the reduction in the Eocene in terms of the meridional temperature gradient is primarily due to emissivity and albedo changes owing to the non-CO2 boundary conditions (i.e. the removal of the Antarctic ice sheet and changes in vegetation). Three of the models (the Community Earth System Model, CESM; the Geophysical Fluid Dynamics Laboratory, GFDL, model; and the Norwegian Earth System Model, NorESM) show results that are consistent with the proxies in terms of the global mean temperature, meridional SST gradient, and CO2, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale, the models lack skill. In particular, the modelled anomalies are substantially lower than those indicated by the proxies in the southwest Pacific; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxies or models in this region. Our aim is that the documentation of the large-scale features and model–data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability, and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols.