Preprints
https://doi.org/10.5194/bg-2021-108
https://doi.org/10.5194/bg-2021-108

  19 May 2021

19 May 2021

Review status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

Theoretical insights from upscaling Michaelis-Menten microbial dynamics in biogeochemical models: a dimensionless approach

Chris H. Wilson1 and Stefan Gerber2 Chris H. Wilson and Stefan Gerber
  • 1Agronomy Department, University of Florida, Gainesville, FL, USA 32611
  • 2Soil and Water Sciences Department, University of Florida, Gainesville, FL, USA 32611

Abstract. Leading an effective response to the accelerating crisis of anthropogenic climate change will require improved understanding of global carbon cycling. A critical source of uncertainty in Earth Systems Models (ESMs) is the role of microbes in mediating both the formation and decomposition of soil organic matter, and hence in determining patterns of CO2 efflux. Traditionally, ESMs model carbon turnover as a first order process impacted primarily by abiotic factors, whereas contemporary biogeochemical models often explicitly represent the microbial biomass and enzyme pools as the active agents of decomposition. However, the combination of non-linear microbial kinetics and ecological heterogeneity across space guarantees that upscaled dyamics will violate mean-field assumptions via Jensen’s Inequality. Violations of mean-field assumptions mean that parameter estimates from models fit to upscaled data (e.g. eddy covariance towers) are likely systematically biased. Here we present a generic mathematical analysis of upscaling michaelis-menten kinetics under heterogeneity, and provide solutions in dimensionless form. We illustrate how our dimensionless form facilitates qualitative insight into the significance of this scale transition, and argue that it will facilitate cross site intercomparisons of flux data. We also identify the critical terms that need to be constrained in order to unbias parameter estimates.

Chris H. Wilson and Stefan Gerber

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-108', Anonymous Referee #1, 14 Jun 2021
    • AC1: 'Reply on RC1', Chris Wilson, 19 Jul 2021
  • RC2: 'Comment on bg-2021-108', William Wieder, 28 Jun 2021
    • AC2: 'Reply on RC2', Chris Wilson, 19 Jul 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-108', Anonymous Referee #1, 14 Jun 2021
    • AC1: 'Reply on RC1', Chris Wilson, 19 Jul 2021
  • RC2: 'Comment on bg-2021-108', William Wieder, 28 Jun 2021
    • AC2: 'Reply on RC2', Chris Wilson, 19 Jul 2021

Chris H. Wilson and Stefan Gerber

Chris H. Wilson and Stefan Gerber

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Short summary
To better mitigate against climate change, it is imperative that ecosystem scientists understand how microbes decompose organic carbon in the soil, and thereby release it as carbon dioxide into the atmosphere. A major challenge is the high variability across ecosystems in microbial biomass, and in the environmental factors like temperature that drive their activity. In this paper, we use math to better understand this how this variability impacts carbon dioxide release over large scales.
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