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The Phanerozoic climate has been interrupted by two long "icehouse" intervals, including the current icehouse of the last ~34 million years. While these cool intervals correspond to lower atmospheric CO2, it is unclear why CO2 levels fell, with hypotheses suggesting changes in CO2 degassing rates or modification of silicate weathering through changing continental lithology or paleogeography. Here, we construct an Earth System Model that integrates these proposed cooling mechanisms in detail. The model can reproduce the broad geologic record of ice cap expansion, allowing us to infer the primary drivers of long-term climate change. Our results indicate that recent icehouse climates required a combination of different cooling mechanisms acting simultaneously and were not driven by a single known process, potentially explaining why icehouses have been rarer than greenhouses over Earth history. [ABSTRACT FROM AUTHOR]

Solid Earth CO2 outgassing, driven by plate tectonic processes, is a key driver of carbon cycle models. However, the magnitudes and variations in outgassing are poorly constrained in deep‐time. We assess plate tectonic carbon emissions and sequestration by coupling a plate tectonic model with reconstructions of oceanic plate carbon reservoirs and a thermodynamic model to quantify outfluxes from slabs and continental arcs over 1 billion years. In the early Neoproterozoic, our model predicts a peak in crustal production and net outgassing from 840 to 780 Ma that corresponds to a contemporaneous pulse in large igneous province eruptions. The Sturtian and Marinoan glaciations (717–635 Ma) correspond to a low in mid‐ocean ridge outgassing, while the following Ediacaran global warming coincides with a rise in net atmospheric carbon influx, driven by an increase in plate boundary and rift length. The Cambrian, Silurian/Devonian and Triassic Jurassic hothouse climates are synchronous with a reduction in carbon sequestration flux into oceanic plates, increasing net outgassing. In contrast, the Early Cretaceous hothouse climate is accompanied by a pronounced increase in mid‐ocean ridge outgassing. Both the Early Ordovician cooling and the late Paleozoic ice ages coincide with a significant decrease in net atmospheric outgassing, driven by an increase in carbon sequestration. The late Cenozoic glaciation is associated with a long‐term decrease in mid‐ocean ridge and rift degassing, and a pronounced increase in carbon flux into pelagic carbonate sediments. Our tectono‐thermodynamic carbon cycle model provides a new foundation for future long‐term climate and geochemical cycling models. Plain Language Summary: Solid Earth carbon outgassing is thought to be a driver of long‐term fluctuations in climate and surface environments, but is difficult to constrain for the geological past. We have built upon the recent development of deep‐time plate tectonic models with evolving boundaries to compute the flux of carbon into plates, the mantle and the atmosphere in the Neoproterozoic and Phanerozoic eons. We use a novel tectono‐thermodynamic modeling approach to compute the mobilization of carbon from subducting slabs and overriding plates along active margins. Our analysis reveals how plate tectonics drives fluctuations between hothouse and icehouse climates by modulating the balance between solid Earth carbon outgassing and storage. Earth's youngest glaciation in the Cenozoic Era is exceptional in that the biological evolution of calcifying oceanic phytoplankton has played an important role in driving an enormous growth of the reservoir of deep‐sea carbonate sediments creating a new sink for atmospheric CO2. Key Points: We combine plate tectonic and thermodynamic models to reconstruct plate tectonic solid Earth carbon outgassing and sequestration since 1 GaCarbon outgassing and plate storage flux play equally important roles in driving long‐term climate fluctuationsCryogenian glaciations correlate with low carbon degassing, while changes in plate carbon influx rise in importance during the Phanerozoic [ABSTRACT FROM AUTHOR]

Databases of 3D paleoclimate model simulations are increasingly used within global biogeochemical models for the Phanerozoic Eon. This improves the accuracy of the surface processes within the biogeochemical models, but the approach is limited by the availability of large numbers of paleoclimate simulations at different p CO 2 levels and for different continental configurations. In this paper we apply the Frame Interpolation for Large Motion (FILM) deep learning method to a set of Phanerozoic paleoclimate model simulations to upscale their time resolution from one model run every ∼25 million years to one model run every 1 million years (Myr). Testing the method on a 5 Myr time-resolution set of continental configurations and paleoclimates confirms the accuracy of our approach when reconstructing intermediate frames from configurations separated by up to 40 Myr. We then apply the method to upscale the paleoclimate data structure in the SCION climate-biogeochemical model. The interpolated surface temperature and runoff are reasonable and present a logical progression between the original key frames. When updated to use the high-time-resolution climate data structure, the SCION model predicts climate shifts that were not present in the original model outputs due to its previous use of widely spaced datasets and simple linear interpolation. We conclude that a time resolution of ∼10 Myr in Phanerozoic paleoclimate simulations is likely sufficient for investigating the long-term carbon cycle and that deep learning methods may be critical in attaining this time resolution at reasonable computational expense, as well as for developing new fully continuous methods in which 3D continental processes are able to translate over a moving continental surface in deep time. However, the efficacy of deep learning methods in interpolating runoff data, compared to that of paleogeography and temperature, is diminished by the heterogeneous distribution of runoff. Consequently, interpolated climates must be confirmed by running a paleoclimate model if scientific conclusions are to be based directly on them. [ABSTRACT FROM AUTHOR]

Databases of 3D paleoclimate model simulations are increasingly used within global biogeochemical models for the Phanerozoic Eon. This improves the accuracy of the surface processes within the biogeochemical models, but the approach is limited by the availability of large numbers of paleoclimate simulations at different pCO2 levels and for different continental configurations. In this paper we apply the Frame Interpolation for Large Motion (FILM) Deep Learning method to a set of paleoclimate model simulations to upscale their time resolution from one model run every ~25 million years to one model run every 1 million year (Myr). Testing the method on a 5 Myr time-resolution set of continental configurations confirms the accuracy of our approach when reconstructing intermediate frames from configurations separated by up to 40 Myrs. We then apply the method to upscale the paleoclimate datastructure in the SCION climate-biogeochemical model and demonstrate that upscaled outputs for global distributions of surface temperature and runoff follow a logical progression between the original keyframes. When updated to use the high-time-resolution climate datastructure, the SCION model predicts climate shifts that were not present in the original model outputs due to its previous use of wide-spaced datasets and simple linear interpolation. We conclude that a time resolution of ~10 Myr in paleoclimate simulations is likely sufficient for investigating the long-term carbon cycle, and that Deep Learning methods may be critical in attaining this time-resolution at a reasonable computational expense, as well as for developing new fully-continuous methods in which 3D continental processes--such as species distribution--are able to translate over a moving continental surface in deep time. Nonetheless, the efficacy of Deep Learning methods in interpolating runoff data, compared to that of paleogeography and temperature, is diminished by the heterogeneous distribution of runoff. Consequently, interpolated climates should be confirmed by running a paleoclimate model for any sound scientific conclusions. [ABSTRACT FROM AUTHOR]