Abstract
The Miocene (23.03–5.33 Ma) is recognized as a period with close to modern-day paleogeography, yet a much warmer climate. With large uncertainties in future hydroclimate projections, Miocene conditions illustrate a potential future analog for the Earth system. A recent opportunistic Miocene Model Intercomparison Project 1 (MioMIP1) focused on synthesizing published Miocene climate simulations and comparing them with available temperature reconstructions. Here, we build on this effort by analyzing the hydrological cycle response to Miocene forcings across early-to-middle (E2MMIO; 20.03–11.6 Ma) and middle-to-late Miocene (M2LMIO; 11.5–5.33 Ma) simulations with CO2 concentrations ranging from 200 to 850 ppm and providing a model-data comparison against available precipitation reconstructions. We find global precipitation increases by ∼2.1 and 2.3% per degree of warming for E2MMIO and M2LMIO simulations, respectively. Models generally agree on a wetter than modern-day tropics; mid and high-latitude, however, do not agree on the sign of subtropical precipitation changes with warming. Global monsoon analysis suggests most monsoon regions, except the North American Monsoon, experience higher precipitation rates under warmer conditions. Model-data comparison shows that mean annual precipitation is underestimated by the models regardless of CO2 concentration, particularly in the mid- to high-latitudes. This suggests that the models may not be (a) resolving key processes driving the hydrological cycle response to Miocene boundary conditions and/or (b) other boundary conditions or processes not considered here are critical to reproducing Miocene hydroclimate. This study highlights the challenges in modeling and reconstructing the Miocene hydrological cycle and serves as a baseline for future coordinated MioMIP efforts.
| Original language | English |
|---|---|
| Article number | e2023PA004726 |
| Number of pages | 29 |
| Journal | Paleoceanography and Paleoclimatology |
| Volume | 39 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2024 |
Bibliographical note
Publisher Copyright:© 2023. The Authors.
Funding
R. P. Acosta acknowledges support from AGS‐1844380 and EAR‐2303417. N. J. Burls acknowledges support from the NSF AGS‐1844380. M. J. Pound acknowledges support from NERC Grant NE/V01501X/1 and Royal Society IECR2202086. C. D. Bradshaw acknowledges NERC Grant NE/I006281/1 and a NERC PhD studentship. M. Huber and X. Liu acknowledge NSF OCN‐2217530. A. C. Sarr and Y. Donnadieu were granted access to HPC resources of TGCC under allocations 2020‐A0090107601 and 2022‐A0090102212 made by GENCI. A. C. Sarr is supported by a grant from Labex OSUG (Investissements d'avenir—ANR10 LABX56). Y. Donnadieu acknowledges support from ANR AMOR (ANR‐16‐CE31‐0020). D. J. Lunt acknowledges support from NERC Grant NE/P01903X/1. M. Prange is supported by the Cluster of Excellence “The Ocean Floor – Earth's Uncharted Interface.” A. Frigola acknowledges support from the Norddeutscher Verbund für Hoch‐ und Höchstleistungsrechnen (HLRN) HPC cluster. G. Knorr and G. Lohmann acknowledge institutional funds at AWI for this work via the Helmholtz research program “Changing Earth—Sustaining our Future.” D. K. Hutchinson is supported by the Australian Research Council Grant DE220100279. We appreciate NCAR CISL Data Support section and Glade (NSFEAR114504) for maintaining and facilitating interactive computing on Casper, to which we used postprocessed data. We thank the reviewers and editor for their insight and thoughtful feedback, which helped improve the manuscript. R. P. Acosta acknowledges support from AGS-1844380 and EAR-2303417. N. J. Burls acknowledges support from the NSF AGS-1844380. M. J. Pound acknowledges support from NERC Grant NE/V01501X/1 and Royal Society IECR2202086. C. D. Bradshaw acknowledges NERC Grant NE/I006281/1 and a NERC PhD studentship. M. Huber and X. Liu acknowledge NSF OCN-2217530. A. C. Sarr and Y. Donnadieu were granted access to HPC resources of TGCC under allocations 2020-A0090107601 and 2022-A0090102212 made by GENCI. A. C. Sarr is supported by a grant from Labex OSUG (Investissements d'avenir—ANR10 LABX56). Y. Donnadieu acknowledges support from ANR AMOR (ANR-16-CE31-0020). D. J. Lunt acknowledges support from NERC Grant NE/P01903X/1. M. Prange is supported by the Cluster of Excellence “The Ocean Floor – Earth's Uncharted Interface.” A. Frigola acknowledges support from the Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen (HLRN) HPC cluster. G. Knorr and G. Lohmann acknowledge institutional funds at AWI for this work via the Helmholtz research program “Changing Earth—Sustaining our Future.” D. K. Hutchinson is supported by the Australian Research Council Grant DE220100279. We appreciate NCAR CISL Data Support section and Glade (NSFEAR114504) for maintaining and facilitating interactive computing on Casper, to which we used postprocessed data. We thank the reviewers and editor for their insight and thoughtful feedback, which helped improve the manuscript.
| Funders | Funder number |
|---|---|
| ANR AMOR | NE/P01903X/1, ANR‐16‐CE31‐0020 |
| ANR-16-CE31-0020 | |
| Norddeutscher Verbund für Hoch | |
| National Science Foundation | AGS‐1844380 |
| Animal Welfare Institute | |
| Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen | |
| UK Natural Environment Research Council | NE/V01501X/1 |
| Royal Historical Society | IECR2202086, ANR10 LABX56, NE/I006281/1 |
| Australian Research Council | DE220100279, NSFEAR114504 |
| Grand Équipement National De Calcul Intensif | |
| Exzellenzcluster Ozean der Zukunft |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- hydroclimate
- Miocene
- modeling
- paleoclimate
- precipitation
- proxies
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