Explicitly representing soil microbial processes in Earth system models

Wieder, William R.; Allison, Steven D.; Davidson, Eric A.; Georgiou, Katerina; Hararuk, Oleksandra; He, Yujie; Hopkins, F. M.; Luo, Yiqi; Smith, Matthew J.; Sulman, Benjamin N.; Todd-Brown, Katherine; Wang, Ying‐Ping; Xia, Jianyang; Xu, Xiaofeng

doi:10.1002/2015gb005188

Cited by 340 publications

(329 citation statements)

References 186 publications

(325 reference statements)

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“…The values of those parameters may be quite different under field conditions. Evaluation of their applicability under a wide range of field conditions will require an integrated approach, such as applications of model-data fusion using a range of field experiments (Wieder et al, 2016). This will eventually lead to a better understanding of the significance of microbial activity on soil carbon decomposition and more accurate predictions of carbon-climate interactions.…”

Section: Discussionmentioning

confidence: 99%

“…For example, conventional linear soil carbon models predict that soil carbon will decrease with increased temperature, all else being equal (Jenkinson et al, 1991), whereas the nonlinear models predict that the soil carbon can decrease or increase, depending on the temperature sensitivity of microbial growth efficiency and turnover rates (Frey et al, 2013;Hagerty et al, 2014;Li et al, 2014). However, the nonlinear models have yet to be validated against field measurements as extensively as the conventional linear soil carbon models (Wieder et al, 2016). They also have some undesirable features, particularly the presence of strong oscillations or bifurcations (Manzoni and Porporato, 2007;Wang et al, 2014) in their dynamics that are not observed in real-world systems.…”

Section: Introductionmentioning

confidence: 99%

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Responses of two nonlinear microbial models to warming and increased carbon input

et al. 2016

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Abstract.A number of nonlinear microbial models of soil carbon decomposition have been developed. Some of them have been applied globally but have yet to be shown to realistically represent soil carbon dynamics in the field. A thorough analysis of their key differences is needed to inform future model developments. Here we compare two nonlinear microbial models of soil carbon decomposition: one based on reverse Michaelis-Menten kinetics (model A) and the other on regular Michaelis-Menten kinetics (model B). Using analytic approximations and numerical solutions, we find that the oscillatory responses of carbon pools to a small perturbation in their initial pool sizes dampen faster in model A than in model B. Soil warming always decreases carbon storage in model A, but in model B it predominantly decreases carbon storage in cool regions and increases carbon storage in warm regions. For both models, the CO 2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO 2 efflux (F max ) decreases with an increase in soil temperature in both models. However, the sensitivity of F max to the increased amount of carbon input increases with soil temperature in model A but decreases monotonically with an increase in soil temperature in model B. These differences in the responses to soil warming and carbon input between the two nonlinear models can be used to discern which model is more realistic when compared to results from field or laboratory experiments. These insights will contribute to an improved understanding of the significance of soil microbial processes in soil carbon responses to future climate change.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Responses of two nonlinear microbial models to warming and increased carbon input

et al. 2016

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“…Hence, use of a more mechanistic module of Rh in ecosystem models and ESMs is warranted to address important environmental controls that modulate the apparent temperature sensitivity of Rh across different systems and among seasons within a system (Davidson and Janssens, 2006;von Lützow and Kögel-Knabner, 2009;Wieder et al, 2015;Luo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA

Sihi

Davidson

Chen

et al. 2018

Agricultural and Forest Meteorology

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A B S T R A C THeterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2 ), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synopticscale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q 10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.

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“…In contrast, growth respiration is only due when substrate for growth is available. Because of the explicit and mechanistic link between microbial activity and soil organic matter degradation, inclusion of microbial models in Earth system models may have the potential to ultimately reduce uncertainty in climate-carbon feedback in the face of climate change because of the explicit link between microbial activity and soil organic matter degradation (Todd-Brown et al, 2012Wieder et al, 2015a).…”

Section: Sihi Et Al: Comparing Models Of Microbial-substrate Intementioning

confidence: 99%

Comparing models of microbial–substrate interactions and their response to warming

et al. 2016

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Abstract. Recent developments in modelling soil organic carbon decomposition include the explicit incorporation of enzyme and microbial dynamics. A characteristic of these models is a positive feedback between substrate and consumers, which is absent in traditional first-order decay models. With sufficiently large substrate, this feedback allows an unconstrained growth of microbial biomass. We explore mechanisms that curb unrestricted microbial growth by including finite potential sites where enzymes can bind and by allowing microbial scavenging for enzymes. We further developed a model where enzyme synthesis is not scaled to microbial biomass but associated with a respiratory cost and microbial population adjusts enzyme production in order to optimise their growth. We then tested short-and long-term responses of these models to a step increase in temperature and find that these models differ in the long-term when shortterm responses are harmonised. We show that several mechanisms, including substrate limitation, variable production of microbial enzymes, and microbes feeding on extracellular enzymes eliminate oscillations arising from a positive feedback between microbial biomass and depolymerisation. The model where enzyme production is optimised to yield maximum microbial growth shows the strongest reduction in soil organic carbon in response to warming, and the trajectory of soil carbon largely follows that of a first-order decomposition model. Modifications to separate growth and maintenance respiration generally yield short-term differences, but results converge over time because microbial biomass approaches a quasi-equilibrium with the new conditions of carbon supply and temperature.

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Explicitly representing soil microbial processes in Earth system models

Cited by 340 publications

References 186 publications

Responses of two nonlinear microbial models to warming and increased carbon input

Responses of two nonlinear microbial models to warming and increased carbon input

Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA

Comparing models of microbial–substrate interactions and their response to warming

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