Model of Early Diagenesis in the Upper Sediment with Adaptable complexity - MEDUSA (v. 2): A time-dependent biogeochemical sediment module for Earth system models, process analysis and teaching

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Abstract

MEDUSA is a time-dependent one-dimensional numerical model of coupled early diagenetic processes in the surface sea-floor sediment. In the vertical, the sediment is subdivided into two different zones. Solids (biogenic, mineral, etc.) raining down from the surface of the ocean are collected by the reactive mixed layer at the top. This is where chemical reactions take place. Solids are transported by bioturbation and advection, and solutes are transported by diffusion and bioirrigation. The classical coupled time-dependent early diagenesis equations (advection-diffusion reaction equations) are used to describe the evolutions of the solid and solute components here. Solids that get transported deeper than the bottom boundary of the reactive mixed layer enter the second zone underneath, where reactions and mixing are neglected. Gradually, as solid material gets transferred here from the overlying reactive layer, it is buried and preserved in a stack of layers that make up a synthetic sediment core. MEDUSA has been extensively modified since its first release from 2007. The composition of the two phases, the processes (chemical reactions) and chemical equilibria between solutes are not fixed any more, but get assembled from a set of XML-based description files that are processed by a code generator to produce the required Fortran code. 1D, 2D and 2D×2D interfaces have been introduced to facilitate the coupling to common grid configurations and material compositions used in biogeochemical models. MEDUSA can also be run in parallel computing environments using the Message Passing Interface (MPI).

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Munhoven, G. (2021). Model of Early Diagenesis in the Upper Sediment with Adaptable complexity - MEDUSA (v. 2): A time-dependent biogeochemical sediment module for Earth system models, process analysis and teaching. Geoscientific Model Development, 14(6), 3603–3631. https://doi.org/10.5194/gmd-14-3603-2021

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