- 1Group of Applied Physics, University of Geneva, Rue de l’Ecole de Médecine 20, 1205 Geneva, Switzerland (laure.moinat@unige.ch)
- 2Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, CH-1205 Geneva, Switzerland
- 3Section of Earth and Environmental Sciences, University of Geneva, 13 Rue des Maraîchers, Geneva, 1205, Switzerland
- 4School of GeoSciences, University of Edinburgh, Edinburgh, UK
Exploring the dynamical structure of complex systems like Earth’s climate generally requires run- ning simulations over long time scales and for a wide range of initial conditions [1] following a ‘bio- geodynamical approach’. This means that the simulations need to include interactions among the climatic components (in particular, dynamical atmosphere and ocean as in general circulation models, as well as representations of vegetation, sea and continental ice) under different plate tectonic config- urations for deep time modelling. This is hardly achieved using CMIP-like models, because of their high computational costs.
Here, we describe a recently developed biogeodynamical modelling tool that allows for running simulations over multi-millennial time scales within a reasonable amount of CPU-time. Starting from the MITgcm coupled atmosphere-ocean-sea ice setup, we have developed a global ice-sheet model based on the shallow-ice approximation, where in a first step the surface mass balance is computed as in [2]. In a second step, we will adapt the MITgcm land/snow model to properly compute the surface energy balance. The runoff map is obtained by the hydrological model pysheds [3] and takes into account the ice-sheet isostatic correction. These three components are further coupled with the well- known vegetation model BIOME4 [4] and the paleogeographical reconstruction model PANALESIS [5].
Such a coupled setup permits to investigate nonlinear interactions among the climatic components at the global scale. These interactions evolve and balance differently along Earth’s history under the effect of various types of forcing, leading to a wide range of climatic steady states for different paleogeographical reconstruction times, and potentially revealing the presence of tipping mechanisms. Here, we show a present-day validation of this coupled setup against observations and CMIP6-model results, and how we are planning to apply it to selected time frames in deep time.
References
[1] Brunetti and Ragon, Physical Review E 107, 054214 (2023)
[2] Tsai & Ruan, Journal of Glaciology 64,246 (2018)
[3] Bartos, Matt., pysheds: simple and fast watershed delineation in python. (2020)
[4] Kaplan et al., Journal of Geophysical Research 108, 8171 (2003)
[5] Vérard., Geological Magazine 156, 2 (2019)
How to cite: Moinat, L., Franziskakis, F., Vérard, C., Goldberg, D., and Brunetti, M.: Development of a biogeodynamical tool for exploratory paleoclimate modelling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2266, https://doi.org/10.5194/egusphere-egu25-2266, 2025.
