Preprints
https://doi.org/10.5194/bg-2022-87
https://doi.org/10.5194/bg-2022-87
12 Apr 2022
 | 12 Apr 2022
Status: this preprint was under review for the journal BG. A final paper is not foreseen.

Modelling the impact of wood density dependent tree mortality on the spatial distribution of Amazonian vegetation carbon

Mathilda Hancock, Stephen Sitch, Fabian Jörg Fischer, Jérôme Chave, Michael O'Sullivan, Dominic Fawcett, and Lina María Mercado

Abstract. Spatially heterogeneous plant mortality rates are an important predictor of the distribution of vegetation carbon in Amazonia. Reproducing the spatial gradients of vegetation carbon in Amazonia and the observed decline in the intact Amazonian carbon sink since 1990 is a challenge faced by dynamic global vegetation models (DGVMs). In this paper, we implement spatially variable mortality rates in TRIFFID, the DGVM currently coupled to the Joint UK Land Environment Simulator (JULES), and compare with the standard model which assumes a homogeneous mortality rate. Spatially variable gridded fields of Amazonian tree mortality are created using a well-known relationship between mortality and wood density, and three independent wood density maps. The diversified mortality scheme substantially improves the representation of vegetation carbon in TRIFFID when compared to observations, with a 90 % reduction in model bias and an increase in the Pearson correlation coefficient with observed biomass. JULES now captures the observed variability of both mortality and vegetation carbon to a greater extent, demonstrating the potential of using easily-measured traits, like wood density, to add spatial and functional diversity into DGVMs. Despite this, the spatial variation of vegetation carbon simulated with the new mortality fields (with standard deviation 15 MgCha-1) is still less than half of the variation in the observed data (standard deviation 35 MgCha-1). Future work should consider the effects of additional processes, like fire, drought and the phosphorus cycle, on the simulated distribution of vegetation carbon in the Amazon.

This preprint has been withdrawn.

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Mathilda Hancock, Stephen Sitch, Fabian Jörg Fischer, Jérôme Chave, Michael O'Sullivan, Dominic Fawcett, and Lina María Mercado

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-87', Izabela Aleixo, 15 May 2022
  • RC2: 'Comment on bg-2022-87', Anonymous Referee #2, 05 Jun 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-87', Izabela Aleixo, 15 May 2022
  • RC2: 'Comment on bg-2022-87', Anonymous Referee #2, 05 Jun 2022
Mathilda Hancock, Stephen Sitch, Fabian Jörg Fischer, Jérôme Chave, Michael O'Sullivan, Dominic Fawcett, and Lina María Mercado

Data sets

Data for: Modelling the impact of wood density dependent tree mortality on the spatial distribution of Amazonian vegetation carbon Mathilda Hancock, Stephen Sitch, Fabian J. Fischer, Jérôme Chave, Michael O'Sullivan, Dominic Fawcett, Lina M. Mercado https://doi.org/10.5281/zenodo.6388019

Mathilda Hancock, Stephen Sitch, Fabian Jörg Fischer, Jérôme Chave, Michael O'Sullivan, Dominic Fawcett, and Lina María Mercado

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Latest update: 22 Jun 2024
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This preprint has been withdrawn.

Short summary
Global vegetation models often underestimate the spatial variability of carbon stored in the Amazon forest. This paper demonstrates that including spatially varying tree mortality rates, as opposed to a homogeneous rate, in one model, significantly improves its simulations of the forest carbon store. To overcome the limited resolution of tree mortality data, this research presents a simple method of calculating mortality rates across Amazonia using a dependence on wood density.
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