Submitted as: model description paper 23 Apr 2021

Submitted as: model description paper | 23 Apr 2021

Review status: this preprint is currently under review for the journal GMD.

A model for marine sedimentary carbonate diagenesis and paleoclimate proxy signal tracking: IMP v0.9

Yoshiki Kanzaki1,a, Dominik Hülse1, Sandra Kirtland Turner1, and Andy Ridgwell1 Yoshiki Kanzaki et al.
  • 1Department of Earth and Planetary Sciences, University of California – Riverside, Riverside, CA 92521, USA
  • acurrent affiliation: School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA

Abstract. The preservation of calcium carbonate in marine sediments is central to controlling the alkalinity balance of the ocean and hence the ocean-atmosphere partitioning of CO2. To successfully address carbon cycle-climate dynamics on geologic (>> 1 kyr) time-scales, Earth system models then require an appropriate representation of the primary controls on CaCO3 preservation. At the same time, marine sedimentary carbonates represent a major archive of Earth history, as they have the potential to preserve how seawater chemistry, and isotopic composition, and even properties of planktic and benthic ecosystems, change with time. However, changes in preservation and even chemical erosion of previously deposited CaCO3, together with the biogenic reworking of upper portions of sediments whereby sediment particles are translocated both locally and non-locally between different depths in the sediments, all act to distort the recorded signal. Numerical models can aid in recovering what the “true” environmental changes might have been, but only if they appropriately account for these processes.

Building on a classical 1-D reaction-transport framework, we present a new diagenetic model – IMP – that simulates biogeochemical transformations in carbonate-hosted proxy signals by allowing for populations of solid carbonate particles to possess different physicochemical characteristics such as isotopic value, solubility, and particle size. The model also utilizes a variable transition matrix to implement different styles of bioturbation. We illustrate the utility of the model for deciphering past environmental changes using several hypothesized transitions of seawater proxies obscured by sediment mixing and chemical erosion. To facilitate the use of IMP, we provide the model in FORTRAN, MATLAB, and Python versions. We described IMP with integration into Earth system models in mind, and present the description of this coupling of IMP with the “cGENIE.muffin” model in a subsequent paper.

Yoshiki Kanzaki et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2020-405', David Archer, 11 May 2021
  • RC2: 'Comment on gmd-2020-405', Anonymous Referee #2, 07 Jun 2021
  • RC3: 'Comment on gmd-2020-405', Guy Munhoven, 15 Jun 2021

Yoshiki Kanzaki et al.

Yoshiki Kanzaki et al.


Total article views: 455 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
363 83 9 455 33 1 1
  • HTML: 363
  • PDF: 83
  • XML: 9
  • Total: 455
  • Supplement: 33
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 23 Apr 2021)
Cumulative views and downloads (calculated since 23 Apr 2021)

Viewed (geographical distribution)

Total article views: 396 (including HTML, PDF, and XML) Thereof 396 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 31 Jul 2021
Short summary
Sedimentary carbonate plays a central role to regulation of Earth’s carbon cycle and climate, and also serves as an archive of paleo environments hosting various elements/isotopes. To help obtain “true” environmental changes from carbonate records over diagenetic distortion, a new model IMP has been developed. Signal tracking is enabled by simulating diagenesis of multiple carbonate particles and different styles of particle mixing by benthos are also reflected with a variable transition matrix.