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Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model.


ABSTRACT: The relative influences of tectonics, continental weathering and seafloor weathering in controlling the geological carbon cycle are unknown. Here we develop a new carbon cycle model that explicitly captures the kinetics of seafloor weathering to investigate carbon fluxes and the evolution of atmospheric CO2 and ocean pH since 100 Myr ago. We compare model outputs to proxy data, and rigorously constrain model parameters using Bayesian inverse methods. Assuming our forward model is an accurate representation of the carbon cycle, to fit proxies the temperature dependence of continental weathering must be weaker than commonly assumed. We find that 15-31?°C (1?) surface warming is required to double the continental weathering flux, versus 3-10?°C in previous work. In addition, continental weatherability has increased 1.7-3.3 times since 100 Myr ago, demanding explanation by uplift and sea-level changes. The average Earth system climate sensitivity is ?K (1?) per CO2 doubling, which is notably higher than fast-feedback estimates. These conclusions are robust to assumptions about outgassing, modern fluxes and seafloor weathering kinetics.

SUBMITTER: Krissansen-Totton J 

PROVIDER: S-EPMC5458154 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model.

Krissansen-Totton Joshua J   Catling David C DC  

Nature communications 20170522


The relative influences of tectonics, continental weathering and seafloor weathering in controlling the geological carbon cycle are unknown. Here we develop a new carbon cycle model that explicitly captures the kinetics of seafloor weathering to investigate carbon fluxes and the evolution of atmospheric CO<sub>2</sub> and ocean pH since 100 Myr ago. We compare model outputs to proxy data, and rigorously constrain model parameters using Bayesian inverse methods. Assuming our forward model is an a  ...[more]

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