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

Daniel J. Lunt, Fran Bragg, Wing-le Chan, David K. Hutchinson, Jean-baptiste Ladant, Polina Morozova, Igor Niezgodzki, Sebastian Steinig, Zhongshi Zhang, Jiang Zhu, Ayako Abe-ouchi, Eleni Anagnostou, Agatha M. De Boer, Helen K. Coxall, Yannick Donnadieu, Gavin Foster, Gordon N. Inglis, Gregor Knorr, Petra M. Langebroek, Caroline H. LearGerrit Lohmann, Christopher J. Poulsen, Pierre Sepulchre, Jessica E. Tierney, Paul J. Valdes, Evgeny M. Volodin, Tom Dunkley Jones, Christopher J. Hollis, Matthew Huber, Bette L. Otto-bliesner

Research output: Contribution to journalArticle (Academic Journal)peer-review

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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.
Original languageEnglish
Pages (from-to)203-227
Number of pages25
JournalClimate of the Past
Volume17
Issue number1
DOIs
Publication statusPublished - 15 Jan 2021

Bibliographical note

Funding Information:
Acknowledgements. Daniel J. Lunt, Sebastian Steinig, Paul Valdes, and Fran Bragg acknowledge funding from the NERC SWEET grant (grant no. NE/P01903X/1). Daniel J. Lunt also acknowledges funding from NERC DeepMIP grant (grant no. NE/N006828/1) and the ERC (“The greenhouse earth system” grant; T-GRES, project reference no. 340923, awarded to Rich Pancost). Christopher J. Poulsen and Jessica E. Tierney acknowledge funding from the Heising-Simons Foundation (grant no. 2016-015). Jiang Zhu and Christopher J. Poulsen wish to thank Jeff Kiehl, Christine Shields, and Mathew Rothstein for providing the CESM code as well as boundary and initial condition files for the CESM simulations. Wing-Le Chan and Ayako Abe-Ouchi acknowledge funding from JSPS KAKENHI (grant no. 17H06104) and MEXT KAKENHI (grant no. 17H06323), and are grateful to JAMSTEC for use of the Earth Simulator. David K. Hutchinson and Agatha M. de Boer were supported by the Swedish Research Council (project no. 2016-03912) and FORMAS (project no. 2018-01621). Their numerical simulations were performed using resources provided by the Swedish National Infrastructure for Computing (SNIC) at NSC, Linköping. Pierre Sepulchre, Jean-Baptiste Ladant, and Yannick Donnadieu were granted access to the HPC resources of TGCC under the allocation no. 2019- A0050102212 made by GENCI. The HadCM3 simulations were carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol (http://www. bristol.ac.uk/acrc/, last access: 10 January 2021). Gordon N. In-glis acknowledges a Royal Society Dorothy Hodgkin Fellowship. Matthew Huber was funded by the US National Science Foundation (NSF; grant nos. ATM-0902780 and OCE-0902882). Bette L. Otto-Bliesner acknowledges the CESM project, which is primarily supported by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (https://doi.org/10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. Tom Dunkley Jones was supported by NERC (grant no. NE/P013112/1). Polina Morozova was supported by the state assignment project no. 0148-2019-0009.

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