Reduced El Niño variability in the mid-Pliocene according to the PlioMIP2 ensemble
Oldeman, Arthur; Baatsen, Michiel; von der Heydt, Anna; Dijkstra, Henk
(2021) Climate of the Past, volume 17, issue 6, pp. 2427 - 2450
(Article)
Abstract
The mid-Pliocene warm period (3.264-3.025Ma) is the most recent geological period during which atmospheric CO2 levels were similar to recent historical values (1/4400ppm). Several proxy reconstructions for the mid-Pliocene show highly reduced zonal sea surface temperature (SST) gradients in the tropical Pacific Ocean, indicating an El Nino-like mean state. However,
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past modelling studies do not show these highly reduced gradients. Efforts to understand mid-Pliocene climate dynamics have led to the Pliocene Model Intercomparison Project (PlioMIP). Results from the first phase (PlioMIP1) showed clear El Nino variability (albeit significantly reduced) and did not show the greatly reduced time-mean zonal SST gradient suggested by some of the proxies. In this work, we study El Nino-Southern Oscillation (ENSO) variability in the PlioMIP2 ensemble, which consists of additional global coupled climate models and updated boundary conditions compared to PlioMIP1. We quantify ENSO amplitude, period, spatial structure and "flavour", as well as the tropical Pacific annual mean state in mid-Pliocene and pre-industrial simulations. Results show a reduced ENSO amplitude in the model-ensemble mean (-24%) with respect to the pre-industrial, with 15 out of 17 individual models showing such a reduction. Furthermore, the spectral power of this variability considerably decreases in the 3-4-year band. The spatial structure of the dominant empirical orthogonal function shows no particular change in the patterns of tropical Pacific variability in the model-ensemble mean, compared to the pre-industrial. Although the time-mean zonal SST gradient in the equatorial Pacific decreases for 14 out of 17 models (0.2 C reduction in the ensemble mean), there does not seem to be a correlation with the decrease in ENSO amplitude. The models showing the most "El Nino-like"mean state changes show a similar ENSO amplitude to that in the pre-industrial reference, while models showing more "La Nina-like"mean state changes generally show a large reduction in ENSO variability. The PlioMIP2 results show a reasonable agreement with both time-mean proxies indicating a reduced zonal SST gradient and reconstructions indicating a reduced, or similar, ENSO variability.
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Keywords: Global and Planetary Change, Stratigraphy, Palaeontology
ISSN: 1814-9324
Publisher: European Geosciences Union
Note: Funding Information: Qiong Zhang acknowledges support from the Swedish Research Council (2013-06476 and 2017-04232). Simulations with EC-Earth were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC). Funding Information: Acknowledgements. The work by Arthur M. Oldeman, Anna S. von der Heydt, Michiel L. J. Baatsen and Henk A. Dijkstra was carried out under the program of the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture and Science (OCW grant no. 024.002.001). Simulations with CCSM4-Utr were performed at the SURFsara Dutch national computing facilities and were sponsored by NWO-EW (Netherlands Organisation for Scientific Research, Exact Sciences) (project no. 17189). Funding Information: Christian Stepanek acknowledges funding from the Helmholtz Climate Initiative REKLIM. Christian Stepanek and Gerrit Lohmann acknowledge funding via the Alfred Wegener Institute’s research programme Marine, Coastal and Polar Systems. Funding Information: Wing-Le Chan and Ayako Abe-Ouchi acknowledge funding from JSPS (KAKENHI grant no. 17H06104 and MEXT KAKENHI grant no. 17H06323). Their simulations with MIROC4m were performed on the Earth Simulator at JAMSTEC, Yokohama, Japan. Funding Information: Alan M. Haywood, Julia C. Tindall and Stephen J. Hunter acknowledge the FP7 Ideas programme from the European Research Council (grant no. PLIO-ESS, 278636), the Past Earth Network (EPSRC grant no. EP/M008.363/1) and the University of Leeds Advanced Research Computing service. Julia C. Tindall was also supported through the Centre for Environmental Modelling and Computation (CEMAC), University of Leeds. Funding Information: W. Richard Peltier and Deepak Chandan wish to acknowledge that data they have contributed from the CCSM4-UoT model was produced with the support of Canadian NSERC Discovery Grant A9627 t WRP, and they wish to acknowledge the support of the SciNet HPC Consortium for providing computing facilities. SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada, the Government of Ontario, the Ontario Research Fund – Research Excellence and the University of Toronto. Funding Information: Gabriel M. Pontes and Ilana Wainer acknowledge the São Paulo Research Foundation (FAPESP 2016/23670-0). Funding Information: Financial support. This research has been supported by the Netherlands Earth System Science Centre (OCW (grant no. 024.002.001)). Funding Information: Bette L. Otto-Bliesner, Esther C. Brady and Ran Feng acknowledge that material for their participation is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) (cooperative agreement no. 1852977 and NSF OPP grant no. 1418411). Ran Feng is also supported by NSF grant no. 1903650. The CESM project is supported primarily by the National Science Foundation. 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. NCAR is sponsored by the National Science Foundation. Funding Information: Charles J. R. Williams and Dan Lunt are thankful for NERC grant NE/P01903X/1 and the NEXCS High Performance Computing facility funded by the Natural Environment Research Council and delivered by the Met Office. Funding Information: Zhongshi Zhang and Xiangyu Li acknowledge financial support from the National Natural Science Foundation of China (grant no. 42005042), the China Scholarship Council (201804910023) and the China Postdoctoral Science Foundation (project no. 2015M581154). The NorESM simulations benefitted from resources provided by UNINETT Sigma2 – the National Infrastructure for High Performance Computing and Data Storage in Norway. Publisher Copyright: © 2021 Arthur M. Oldeman et al.
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