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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-300
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-300
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 28 Nov 2019

Submitted as: model description paper | 28 Nov 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Robust Ecosystem Demography (RED): a parsimonious approach to modelling vegetation dynamics in Earth System Models

Arthur P. K. Argles1, Jonathan R. Moore1, Chris Huntingford2, Andrew J. Wiltshire3, Chris D. Jones3, and Peter M. Cox1 Arthur P. K. Argles et al.
  • 1College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
  • 2Centre for Ecology and Hydrology, Wallingford OX10 8BB, UK
  • 3Met Office Hadley Centre, Fitzroy Road, Exeter EX1 3PB, UK

Abstract. A significant proportion of the uncertainty in climate projections arises from uncertainty in the representation of land carbon uptake. Dynamic Global Vegetation Models (DGVMs) vary in their representations of regrowth and competition for resources, which results in differing responses to changes in atmospheric CO2 and climate. More advanced cohort-based patch models are now becoming established in the latest DGVMs. These typically attempt to simulate the size-distribution of trees as a function of both tree-size (mass or trunk diameter) and age (time since disturbance). This approach can capture the overall impact of stochastic disturbance events on the forest structure and biomass, but at the cost of needing to update a probability density function in two-dimensions. Here we present the Robust Ecosystem Demography (RED), in which the pdf is collapsed on to the single dimension of tree mass. RED is designed to retain the ability of more complex cohort DGVMs to represent forest demography, while also being parameter sparse and analytically soluble. The population of each Plant Functional Type (PFT) is partitioned into mass classes with a fixed baseline mortality along with an assumed power-law scaling of growth-rate with mass . The analytical equilibrium solutions of RED allow the model to be calibrated against observed forest cover using a single parameter – the ratio of mortality to growth for a tree of a reference mass (μ0). We show that RED can thus be calibrated to the ESA LC_CCI (European Space Agency Land Cover Climate Change Initiative) coverage dataset for nine PFTs. Using Net Primary Productivity and litter outputs from the UK Earth System Model (UKESM), we are able to diagnose the spatially varying disturbance rates consistent with this observed vegetation map. The analytical form for RED circumnavigates the need to spin-up the numerical model, making it attractive for application in Earth System Models (ESMs). This is especially so given that the model is also highly parameter-sparse.

Arthur P. K. Argles et al.
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Arthur P. K. Argles et al.
Model code and software

RED DGVM A. Argles, J. Moore, and P. Cox https://doi.org/10.5281/zenodo.3548678

Arthur P. K. Argles et al.
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Short summary
The Robust Ecosystem Demography (RED) model simulates cohorts of vegetation through mass classes. RED establishes a framework for representing demographic changes through competition, growth, and mortality across the size distribution of a forest. The steady-state of the model can be solved analytically, enabling initialization. When driven by mean growth rates from a land-surface model, RED is able to fit the observed global vegetation map, giving a map of implicit mortality rates.
The Robust Ecosystem Demography (RED) model simulates cohorts of vegetation through mass...
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