The muddle of ages, turnover, transit, and residence times in the carbon cycle

Sierra, Carlos A.; Müller, Markus M.; Metzler, Holger; Manzoni, Stefano; Trumbore, Susan E.

doi:10.1111/gcb.13556

Cited by 134 publications

(166 citation statements)

References 40 publications

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“…These differences can be explained by the heterogeneity of PyOC material, but also by the different methods used to assess its presence in soils. While being a limited tool to assess the persistence of SOC (Sierra et al, 2017), mean residence time (MRT) is widely used to compare the different turnovers of organic materials. Assessing the MRT of PyOC is complex, because of the large time span that should be considered.…”

Section: Introductionmentioning

confidence: 99%

Pyrogenic Carbon Lacks Long-Term Persistence in Temperate Arable Soils

et al. 2017

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Pyrogenic organic carbon (PyOC) derived from incomplete burning of biomass is considered the most persistent fraction of soil organic carbon (SOC), being expected to remain in soil for centuries. However, PyOC persistence has seldom been evaluated under field conditions. Based on a unique set of soils from five European long-term bare fallows, i.e. vegetation-free field experiments, we provide the first direct comparison between PyOC and SOC persistence in temperate arable soils. We found that soil PyOC contents decreased more rapidly than expected from current concepts, the mean residence time of native PyOC being just 1.6 times longer than that of SOC. At the oldest experimental site, 55% of the initial PyOC remained after 80 years of bare fallow. Our results suggest that while the potential for long-term C storage exists, the persistence of PyOC in soil may currently be overestimated

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Section: Introductionmentioning

confidence: 99%

Pyrogenic Carbon Lacks Long-Term Persistence in Temperate Arable Soils

et al. 2017

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“…In the C allocation schemes used, changing biomass with time can be expressed as Eq. (4), which are models that behave as a linear autonomous system (Sierra et al, 2017). This implies the models, when forced with equivalent meteorology and physical soil properties, will eventually converge to a steady-state independent of the starting values of the state variables, although in the case of CLM this may take many tens of thousands of years.…”

Section: Allocation Scheme: Implications For C Poolsmentioning

confidence: 99%

“…Some of the aforementioned processes are already partially incorporated in LSMs, in particular land-use and land-cover change, but the lack of a mechanistic representation of the remaining processes is therefore indirectly represented in stem turnover rates. The processes controlling turnover times influence C storage capacity, but turnover is not well constrained in models (Friend et al, 2014;Sierra et al 2017).…”

Section: Allocation Scheme: Implications For C Poolsmentioning

confidence: 99%

“…Once C is taken up by the plant, the carbon is allocated either to short-lived leaf or fine-root tissues, or to longer-lived woody tissues. Here we use turnover time of C in a plant pool as the total carbon pool divided by the total flux into or out of the pool (Sierra et al, 2017). Plants that allocate a greater proportion of C to tissues with long turnover times (e.g., stem) have a higher standing biomass than the plants that allocate a greater proportion of C to tissues with short turnover times (e.g., leaf).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

et al. 2017

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Abstract. How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes:

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“…From these equations it is evident that age and transit time calculations mainly depend on the schemes of C partitioning (β) 10 and cycling (B) within a vegetation model. Therefore, if we want to understand processes such as the mixing of old and newly fixed NSC using ecosystem models, it is critical to model proper carbon allocation (CA) strategies.…”

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confidence: 99%

Ages and transit times as important diagnostics of model performance for predicting carbon dynamics in terrestrial vegetation models

Ceballos-Núñez¹,

Richardson²,

Sierra³

2017

Preprint

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Abstract. The global carbon cycle is strongly controlled by the source/sink strength of vegetation as well as the capacity of terrestrial ecosystems to retain this carbon. These dynamics, as well as processes such as the mixing of old and newly fixed carbon, have been studied using ecosystem models, but different assumptions regarding the carbon allocation strategies and other model structures may result in highly divergent model predictions. We modeled three systems of vegetation compartments and assessed their performance by calculating the age of the carbon in vegetation system and within each compartment, and 5 the overall transit time of C in the system. We used these diagnostics to assess the influence of three different carbon allocation schemes on the rates of C cycling in vegetation. First, we used published measurements of ecosystem C compartments from the Harvard Forest Environmental Measurement Site to find the best set of parameters for the different model structures. Second, we calculated C stocks, release fluxes, radiocarbon values based on the bomb spike, ages, and transit times. We found a good fit of the three model structures to the available data, but the time series of C in foliage and wood need to be complemented 10 with other ecosystem compartments in order to reduce the high parameter collinearity that we observed, and reduce model equifinality. Differences in model structures had a small impact on predicting C stocks in ecosystem compartments, but overall they resulted in very different predictions of age and transit time distributions. In particular, the inclusion of two storage compartments resulted in the prediction of a system mean age that was 10-20 years older than in the models with one or no storage compartments. The age of carbon in the wood compartment of this model was also distributed towards older ages, 15 whereas fast cycling compartments had an age distribution that did not exceed 5 years. As expected, models with C distributed towards older ages also had longer transit times. These results suggest that ages and transit times, which can be indirectly measured using isotope tracers, serve as important diagnostics of model structure and could largely help to reduce uncertainties in model predictions. Furthermore, by considering age and transit times of C in vegetation compartments as distributions, not only their mean values, we obtain additional insights on the temporal dynamics of carbon use, storage, and allocation to plant 20 parts, which not only depends on the rate at which this C is transferred in and out of the compartments, but also on the stochastic nature of the process itself. 1Biogeosciences Discuss., https://doi

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The muddle of ages, turnover, transit, and residence times in the carbon cycle

Cited by 134 publications

References 40 publications

Pyrogenic Carbon Lacks Long-Term Persistence in Temperate Arable Soils

Pyrogenic Carbon Lacks Long-Term Persistence in Temperate Arable Soils

Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

Ages and transit times as important diagnostics of model performance for predicting carbon dynamics in terrestrial vegetation models

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