Simulating Miocene Warmth: Insights From an Opportunistic Multi-Model Ensemble (MioMIP1)

被引:94
作者
Burls, N. J. [1 ]
Bradshaw, C. D. [2 ,3 ]
De Boer, A. M. [4 ]
Herold, N. [5 ]
Huber, M. [6 ]
Pound, M. [7 ]
Donnadieu, Y. [8 ]
Farnsworth, A. [9 ]
Frigola, A. [10 ]
Gasson, E. [9 ]
von der Heydt, A. S. [11 ]
Hutchinson, D. K. [4 ]
Knorr, G. [12 ]
Lawrence, K. T. [13 ]
Lear, C. H. [14 ]
Li, X. [15 ]
Lohmann, G. [12 ]
Lunt, D. J. [9 ]
Marzocchi, A. [16 ]
Prange, M. [10 ]
Riihimaki, C. A. [17 ]
Sarr, A-C [8 ]
Siler, N. [18 ]
Zhang, Z. [15 ,19 ]
机构
[1] George Mason Univ, Dept Atmospher Ocean & Earth Sci, Ctr Ocean Land Atmosphere Studies, Fairfax, VA 22030 USA
[2] Met Off Hadley Ctr, Exeter, Devon, England
[3] Univ Exeter, Global Sys Inst, Exeter, Devon, England
[4] Stockholm Univ, Dept Geol Sci, Bolin Ctr Climate Res, Stockholm, Sweden
[5] NSW Dept Planning Ind & Environm Climate & Atmosp, Sci Div, Parramatta, NSW, Australia
[6] Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN USA
[7] Northumbria Univ, Dept Geogr & Environm Sci, Newcastle Upon Tyne, Tyne & Wear, England
[8] Aix Marseille Univ, CNRS, IRD, Coll France,INRA,CEREGE, Marseille, France
[9] Univ Bristol, Sch Geog Sci, Bristol, Avon, England
[10] Univ Bremen, MARUM Ctr Marine Environm Sci, Bremen, Germany
[11] Univ Utrecht, Fac Sci, Inst Marine & Atmospher Res, Dept Phys, Utrecht, Netherlands
[12] Alfred Wegener Inst Helmholtz Ctr Polar & Marine, Bremerhaven, Germany
[13] Lafayette Coll, Dept Geol & Environm Geosci, Easton, PA USA
[14] Cardiff Univ, Sch Earth & Environm Sci, Cardiff, Wales
[15] China Univ Geosci, Dept Atmospher Sci, Sch Environm Studies, Wuhan, Peoples R China
[16] Natl Oceanog Ctr, Southampton, Hants, England
[17] Princeton Univ, Council Sci & Technol, Princeton, NJ USA
[18] Oregon State Univ, Coll Earth Ocean & Atmospher Sci, Corvallis, OR USA
[19] Bjerknes Ctr Climate Res, NORCE Norwegian Res Ctr, Bergen, Norway
基金
瑞典研究理事会; 英国自然环境研究理事会; 美国国家科学基金会;
关键词
Miocene; Miocene surface temperature synthesis; model intercomparison; paleoclimate; polar amplification; ANTARCTIC ICE-SHEET; MERIDIONAL OVERTURNING CIRCULATION; EARTH SYSTEM MODEL; TERRESTRIAL CLIMATE EVOLUTION; COMMUNITY ATMOSPHERE MODEL; SEA-SURFACE TEMPERATURES; MIDDLE MIOCENE; NORTH-ATLANTIC; BOUNDARY-CONDITIONS; EQUATORIAL PACIFIC;
D O I
10.1029/2020PA004054
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The Miocene epoch, spanning 23.03-5.33 Ma, was a dynamic climate of sustained, polar amplified warmth. Miocene atmospheric CO2 concentrations are typically reconstructed between 300 and 600 ppm and were potentially higher during the Miocene Climatic Optimum (16.75-14.5 Ma). With surface temperature reconstructions pointing to substantial midlatitude and polar warmth, it is unclear what processes maintained the much weaker-than-modern equator-to-pole temperature difference. Here, we synthesize several Miocene climate modeling efforts together with available terrestrial and ocean surface temperature reconstructions. We evaluate the range of model-data agreement, highlight robust mechanisms operating across Miocene modeling efforts and regions where differences across experiments result in a large spread in warming responses. Prescribed CO2 is the primary factor controlling global warming across the ensemble. On average, elements other than CO2, such as Miocene paleogeography and ice sheets, raise global mean temperature by similar to 2 degrees C, with the spread in warming under a given CO2 concentration (due to a combination of the spread in imposed boundary conditions and climate feedback strengths) equivalent to similar to 1.2 times a CO2 doubling. This study uses an ensemble of opportunity: models, boundary conditions, and reference data sets represent the state-of-art for the Miocene, but are inhomogeneous and not ideal for a formal intermodel comparison effort. Acknowledging this caveat, this study is nevertheless the first Miocene multi-model, multi-proxy comparison attempted so far. This study serves to take stock of the current progress toward simulating Miocene warmth while isolating remaining challenges that may be well served by community-led efforts to coordinate modeling and data activities within a common analytical framework.
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