Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level

Mahecha, Miguel D.; Reichstein, Markus; Carvalhais, Nuno; Lasslop, Gitta; Lange, Holger; Seneviratne, Sonia I.; Vargas, Rodrigo; Ammann, Christof; Arain, M. Altaf; Cescatti, Alessandro; Janssens, Ivan A.; Migliavacca, Mirco; Montagnani, Leonardo; Richardson, Andrew D.

doi:10.1126/science.1189587

Cited by 496 publications

(459 citation statements)

References 36 publications

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“…On the other hand, estimates of net primary production and respiration in Earth system models assume a static response to temperature [e.g., Oleson et al, 2010], although there is evidence that plants can acclimate to higher temperatures on seasonal and interannual timescales [Kattge and Knorr, 2007;Atkin et al, 2008;Sendall et al, 2015]. This is consistent with other evidence that Earth system models may be overly pessimistic about the impact of higher temperatures on land carbon [Frank et al, 2010;Keenan et al, 2013;Mahecha et al, 2010], and studies which include temperature acclimation indicate that including the effects of acclimation would increase the terrestrial carbon in Earth system models [Arneth et al, 2012;Atkin et al, 2008;King et al, 2006]. Thus, the IAM models used to predict future land use conversion may be underestimating the threat to forests from deforestation, while Earth system models may be overestimating the threat to forests from higher temperatures.…”

Section: Discussionsupporting

confidence: 79%

Interactions between land use change and carbon cycle feedbacks

Mahowald

Randerson

Lindsay

et al. 2017

Global Biogeochemical Cycles

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Using the Community Earth System Model, we explore the role of human land use and land cover change (LULCC) in modifying the terrestrial carbon budget in simulations forced by Representative Concentration Pathway 8.5, extended to year 2300. Overall, conversion of land (e.g., from forest to croplands via deforestation) results in a model-estimated, cumulative carbon loss of 490 Pg C between 1850 and 2300, larger than the 230 Pg C loss of carbon caused by climate change over this same interval. The LULCC carbon loss is a combination of a direct loss at the time of conversion and an indirect loss from the reduction of potential terrestrial carbon sinks. Approximately 40% of the carbon loss associated with LULCC in the simulations arises from direct human modification of the land surface; the remaining 60% is an indirect consequence of the loss of potential natural carbon sinks. Because of the multicentury carbon cycle legacy of current land use decisions, a globally averaged amplification factor of 2.6 must be applied to 2015 land use carbon losses to adjust for indirect effects. This estimate is 30% higher when considering the carbon cycle evolution after 2100. Most of the terrestrial uptake of anthropogenic carbon in the model occurs from the influence of rising atmospheric CO 2 on photosynthesis in trees, and thus, model-projected carbon feedbacks are especially sensitive to deforestation.

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Section: Discussionsupporting

confidence: 79%

Interactions between land use change and carbon cycle feedbacks

Mahowald

Randerson

Lindsay

et al. 2017

Global Biogeochemical Cycles

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“…There was no significant relationship between E 0 and any of the mean annual satellite-derived indices related to canopy greenness, water and temperature over either growing season or whole year. The question if the temperature sensitivity of respiration varies over time and among vegetation types is still under debate (Davidson and Janssens, 2006;Mahecha et al, 2010;Chen et al, 2010;Yvon-Durocher et al, 2012;Jägermeyr et al, 2014). Although many evidences showed that E 0 varies among compounds and decomposition of labile SOM has lower temperature sensitivity than decomposition of recalcitrant SOM (Davidson and Janssens, 2006), some researches reported that temperature sensitivities of respiration are remarkably similar among ecosystems (YvonDurocher et al, 2012), and can be even considered as a constant (Mahecha et al, 2010).…”

Section: Characteristics Of the Model Parametersmentioning

confidence: 99%

“…It was reported that the substrates for plant growth respiration (R g ) (Amthor, 2000;Piao et al, 2010;Mahecha et al, 2010;Chapin et al, 2011) and rhizomicrobial respiration (R rhi ) Cheng, 2001, 2004;Dilkes et al, 2004;Heinemeyer et al, 2006Heinemeyer et al, , 2007Moyano et al, 2007Moyano et al, , 2008Gaumont-Guay et al, 2008;Kuzyakov and Gavrichkova, 2010;Mahecha et al, 2010) are both derived from current photosynthate, and their respiratory rates couple closely with GPP. During the process of plant photosynthesis, growth respiration (R g ) consumes some photosynthate to provide energy for satisfying growth demand.…”

Section: Description Of the Rersmmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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“…Arguably, these values are the most relevant for predicting long term changes, since uptake and respiration ultimately depend on C made available by decomposition. Why these values remained especially low and how they may change in the long term remains to be explored, but rather low sensitivities are consistent with some integrative studies at the ecosystem level (Mahecha et al, 2010 behaviour, the answer is not clear and will require further research. Ultimately, the better option may be to abandon such model and develop better validated mechanistic alternatives for prediction purposes.…”

Section: B)mentioning

confidence: 93%

Diffusion based modelling of temperature and moisture interactive effects on carbon fluxes of mineral soils

Moyano

Vasilyeva

Menichetti

2018

Preprint

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Abstract. While CO2 production in soils strongly responds to changes in temperature and moisture, the magnitude of such responses at different time scales remains difficult to predict. In particular, little is known of the mechanisms leading to interactions in the effects of these drivers on soil CO2 emissions, even though such observations are common. Here we compare a number of modelling approaches to test which underlying mechanisms best simulate the interactive responses measured in soils incubated under combined levels of temperature and moisture. We found that two model components were critical for reproducing the observed interactive patterns: 1. Michaelis-Menten reaction kinetics, which strongly improved the model fit when applied to decomposition reactions, and 2. diffusion of dissolved C and enzymes. The latter replaces conventional empirical functions as a mechanism relating moisture content with C fluxes. Indeed, empirical functions failed to capture the main observed interactions. After model calibration we were able to explain 84&#8201;% of the variation in the data. Model simulations resulted in a decoupling of decomposition and respiration C fluxes in the short and mid-term, with interaction effects and general sensitivities to temperature and moisture being more pronounced for respiration. Sensitivity to different model parameters was highest for those affecting diffusion limitations, followed by activation energies, the Michaelis-Menten constant, and carbon use efficiency. Model validation resulted in a high fit against independent data (R2&#8201;=&#8201;0.99). The same underlying model parameters resulted here in different apparent temperature sensitivities compared to the calibration step, demonstrating a strong effects of initial soil conditions. With these results we could demonstrate the importance of model structure and the central role of diffusion and reaction kinetics for simulating complex soil C dynamics related to temperature and moisture interactions. Future studies should further validate this mechanistic approach and extend its use to a larger range of soils.

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Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level

Cited by 496 publications

References 36 publications

Interactions between land use change and carbon cycle feedbacks

Interactions between land use change and carbon cycle feedbacks

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Diffusion based modelling of temperature and moisture interactive effects on carbon fluxes of mineral soils

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