The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2

Xin Ren; Daniel J. Lunt; Erica Hendy; Anna Von Der Heydt; Ayako Abe-Ouchi; Bette Otto-Bliesner; Charles J.R. Williams; Christian Stepanek; Chuncheng Guo; Deepak Chandan; Gerrit Lohmann; Julia C. Tindall; Linda E. Sohl; Mark A. Chandler; Masa Kageyama; Michiel L.J. Baatsen; Ning Tan; Qiong Zhang; Ran Feng; Stephen Hunter; Wing Le Chan; W. Richard Peltier; Xiangyu Li; Youichi Kamae; Zhongshi Zhang; Alan M. Haywood

doi:10.5194/cp-19-2053-2023

The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2

Xin Ren^*, Daniel J. Lunt, Erica Hendy, Anna Von Der Heydt, Ayako Abe-Ouchi, Bette Otto-Bliesner, Charles J.R. Williams, Christian Stepanek, Chuncheng Guo, Deepak Chandan, Gerrit Lohmann, Julia C. Tindall, Linda E. Sohl, Mark A. Chandler, Masa Kageyama, Michiel L.J. Baatsen, Ning Tan, Qiong Zhang, Ran Feng, Stephen HunterWing Le Chan, W. Richard Peltier, Xiangyu Li, Youichi Kamae, Zhongshi Zhang, Alan M. Haywood

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land-sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial. In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.

Original language	English
Pages (from-to)	2053-2077
Number of pages	25
Journal	Climate of the Past
Volume	19
Issue number	10
DOIs	https://doi.org/10.5194/cp-19-2053-2023
Publication status	Published - 26 Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 Xin Ren et al.

Funding

Xin Ren, Daniel Lunt and Erica Hendy acknowledge the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (grant no. 813360 4DREEF). This research used data provided by the Ocean Drilling Program (ODP). Wing-Le Chan and Ayako Abe-Ouchi acknowledge funding from JSPS (KAKENHI grant no. 17H06104 and MEXT KAKENHI grant no. 17H06323) and computational resources from the Earth Simulator at JAMSTEC, Yokohama, Japan. The development of GISS-E2.1 was supported by the NASA Modeling, Analysis, and Prediction (MAP) program. CMIP6 simulations with GISS-E2.1 were made possible by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. Charles J. R. Williams and Daniel J. Lunt acknowledge the financial support of the UK Natural Environment Research Council (NERC)-funded SWEET project (grant no. NE/P01903X/1) and funding from the European Research Council (under the European Union’s Seventh Framework Programme (FP/2007-868 2013; ERC grant no. 340923; TGRES). Gerrit Lohmann and Christian Stepanek acknowledge institutional funding from the Alfred Wegener Institute (AWI) via the research programme PACES-II of the Helmholtz Association. Christian Stepanek acknowledges funding from the Helmholtz Climate Initiative REKLIM. Michiel L. J. Baatsen and Anna von der Heydt acknowledge support by the programme of the Netherlands Earth System Science Centre (NESSC), which is financially supported by the Ministry of Education, Culture and Science (OCW). Xiangyu Li acknowledges funding from National Natural Science Foundation of China (grant nos. 42005042 and 42275047). All IPSL simulations have been run on Très Grand Centre de Calcul du CEA (TGCC) through GENCI (Grand Équipement National de Calcul Intensif) allocation (grant no. gen2212). We acknowledge the work of GENCI and TGCC for making our simulations available for the present work. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6, and the Palaeoclimate Modelling Intercomparison Project (PMIP) for coordinating its fourth phase, including the mPWP simulations analysed here, with CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the multiple funding agencies who support CMIP6 and ESGF. Xin Ren, Daniel Lunt and Erica Hendy acknowledge the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (grant no. 813360 4DREEF). This research used data provided by the Ocean Drilling Program (ODP). Wing-Le Chan and Ayako Abe-Ouchi acknowledge funding from JSPS (KAKENHI grant no. 17H06104 and MEXT KAKENHI grant no. 17H06323) and computational resources from the Earth Simulator at JAMSTEC, Yokohama, Japan. The development of GISS-E2.1 was supported by the NASA Modeling, Analysis, and Prediction (MAP) program. CMIP6 simulations with GISS-E2.1 were made possible by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. Charles J. R. Williams and Daniel J. Lunt acknowledge the financial support of the UK Natural Environment Research Council (NERC)-funded SWEET project (grant no. NE/P01903X/1) and funding from the European Research Council (under the European Union’s Seventh Framework Programme (FP/2007-868 2013; ERC grant no. 340923; TGRES). Gerrit Lohmann and Christian Stepanek acknowledge institutional funding from the Alfred Wegener Institute (AWI) via the research programme PACES-II of the Helmholtz Association. Christian Stepanek acknowledges funding from the Helmholtz Climate Initiative REKLIM. Michiel L. J. Baatsen and Anna von der Heydt acknowledge support by the programme of the Netherlands Earth System Science Centre (NESSC), which is financially supported by the Ministry of Education, Culture and Science (OCW). Xiangyu Li acknowledges funding from National Natural Science Foundation of China (grant nos. 42005042 and 42275047). All IPSL simulations have been run on Très Grand Centre de Calcul du CEA (TGCC) through GENCI (Grand Équipement National de Calcul Intensif) allocation (grant no. gen2212). We acknowledge the work of GENCI and TGCC for making our simulations available for the present work. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6, and the Palaeoclimate Modelling Intercomparison Project (PMIP) for coordinating its fourth phase, including the mPWP simulations analysed here, with CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the multiple funding agencies who support CMIP6 and ESGF. This research has been funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions (grant no. 813360 4DREEF).

Funders	Funder number
Earth System Grid Federation	CMIP6
Helmholtz Climate Initiative REKLIM
NASA Center for Climate Simulation
Palaeoclimate Modelling Intercomparison Project
TGCC
National Aeronautics and Space Administration
Horizon 2020 Framework Programme
H2020 Marie Skłodowska-Curie Actions	813360 4DREEF
Seventh Framework Programme	FP/2007-868 2013
Animal Welfare Institute
UK Natural Environment Research Council	NE/P01903X/1
Japan Society for the Promotion of Science London	17H06104
European Research Council	340923
Ministry of Education, Culture, Sports, Science and Technology	17H06323
National Natural Science Foundation of China	42275047, 42005042
Ministerie van onderwijs, cultuur en wetenschap
Japan Agency for Marine-Earth Science and Technology
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Horizon 2020
Max Delbrück Center for Molecular Medicine in the Helmholtz Association
Grand Équipement National De Calcul Intensif	gen2212
Netherlands Earth System Science Centre
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung

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Ren, X., Lunt, D. J., Hendy, E., Von Der Heydt, A., Abe-Ouchi, A., Otto-Bliesner, B., Williams, C. J. R., Stepanek, C., Guo, C., Chandan, D., Lohmann, G., Tindall, J. C., Sohl, L. E., Chandler, M. A., Kageyama, M., Baatsen, M. L. J., Tan, N., Zhang, Q., Feng, R., ... Haywood, A. M. (2023). The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2. Climate of the Past, 19(10), 2053-2077. https://doi.org/10.5194/cp-19-2053-2023

@article{5ec0a33d47b141f290470f6a6bb3e83e,

title = "The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2",

abstract = "The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land-sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial. In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.",

author = "Xin Ren and Lunt, \{Daniel J.\} and Erica Hendy and \{Von Der Heydt\}, Anna and Ayako Abe-Ouchi and Bette Otto-Bliesner and Williams, \{Charles J.R.\} and Christian Stepanek and Chuncheng Guo and Deepak Chandan and Gerrit Lohmann and Tindall, \{Julia C.\} and Sohl, \{Linda E.\} and Chandler, \{Mark A.\} and Masa Kageyama and Baatsen, \{Michiel L.J.\} and Ning Tan and Qiong Zhang and Ran Feng and Stephen Hunter and Chan, \{Wing Le\} and Peltier, \{W. Richard\} and Xiangyu Li and Youichi Kamae and Zhongshi Zhang and Haywood, \{Alan M.\}",

note = "Publisher Copyright: {\textcopyright} 2023 Xin Ren et al.",

year = "2023",

month = oct,

day = "26",

doi = "10.5194/cp-19-2053-2023",

language = "English",

volume = "19",

pages = "2053--2077",

journal = "Climate of the Past",

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number = "10",

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Ren, X, Lunt, DJ, Hendy, E, Von Der Heydt, A, Abe-Ouchi, A, Otto-Bliesner, B, Williams, CJR, Stepanek, C, Guo, C, Chandan, D, Lohmann, G, Tindall, JC, Sohl, LE, Chandler, MA, Kageyama, M, Baatsen, MLJ, Tan, N, Zhang, Q, Feng, R, Hunter, S, Chan, WL, Peltier, WR, Li, X, Kamae, Y, Zhang, Z & Haywood, AM 2023, 'The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2', Climate of the Past, vol. 19, no. 10, pp. 2053-2077. https://doi.org/10.5194/cp-19-2053-2023

TY - JOUR

T1 - The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene

T2 - results from PlioMIP2

AU - Ren, Xin

AU - Lunt, Daniel J.

AU - Hendy, Erica

AU - Von Der Heydt, Anna

AU - Abe-Ouchi, Ayako

AU - Otto-Bliesner, Bette

AU - Williams, Charles J.R.

AU - Stepanek, Christian

AU - Guo, Chuncheng

AU - Chandan, Deepak

AU - Lohmann, Gerrit

AU - Tindall, Julia C.

AU - Sohl, Linda E.

AU - Chandler, Mark A.

AU - Kageyama, Masa

AU - Baatsen, Michiel L.J.

AU - Tan, Ning

AU - Zhang, Qiong

AU - Feng, Ran

AU - Hunter, Stephen

AU - Chan, Wing Le

AU - Peltier, W. Richard

AU - Li, Xiangyu

AU - Kamae, Youichi

AU - Zhang, Zhongshi

AU - Haywood, Alan M.

PY - 2023/10/26

Y1 - 2023/10/26

N2 - The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land-sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial. In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.

AB - The Maritime Continent (MC) forms the western boundary of the tropical Pacific Ocean, and relatively small changes in this region can impact the climate locally and remotely. In the mid-Piacenzian warm period of the Pliocene (mPWP; 3.264 to 3.025 Ma) atmospheric CO2 concentrations were ∼ 400 ppm, and the subaerial Sunda and Sahul shelves made the land-sea distribution of the MC different to today. Topographic changes and elevated levels of CO2, combined with other forcings, are therefore expected to have driven a substantial climate signal in the MC region at this time. By using the results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2), we study the mean climatic features of the MC in the mPWP and changes in Indonesian Throughflow (ITF) with respect to the preindustrial. Results show a warmer and wetter mPWP climate of the MC and lower sea surface salinity in the surrounding ocean compared with the preindustrial. Furthermore, we quantify the volume transfer through the ITF; although the ITF may be expected to be hindered by the subaerial shelves, 10 out of 15 models show an increased volume transport compared with the preindustrial. In order to avoid undue influence from closely related models that are present in the PlioMIP2 ensemble, we introduce a new metric, the multi-cluster mean (MCM), which is based on cluster analysis of the individual models. We study the effect that the choice of MCM versus the more traditional analysis of multi-model mean (MMM) and individual models has on the discrepancy between model results and data. We find that models, which reproduce modern MC climate well, are not always good at simulating the mPWP climate anomaly of the MC. By comparing with individual models, the MMM and MCM reproduce the preindustrial sea surface temperature (SST) of the reanalysis better than most individual models and produce less discrepancy with reconstructed sea surface temperature anomalies (SSTA) than most individual models in the MC. In addition, the clusters reveal spatial signals that are not captured by the MMM, so that the MCM provides us with a new way to explore the results from model ensembles that include similar models.

UR - https://www.scopus.com/pages/publications/85177738814

U2 - 10.5194/cp-19-2053-2023

DO - 10.5194/cp-19-2053-2023

M3 - Article

AN - SCOPUS:85177738814

SN - 1814-9324

VL - 19

SP - 2053

EP - 2077

JO - Climate of the Past

JF - Climate of the Past

IS - 10

ER -

The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2

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Bibliographical note

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