Biotic responses buffer warming‐induced soil organic carbon loss in Arctic tundra

Liang, Junyi; Ji, Xia; Shi, Zheng; Jiang, Lifen; Ma, Shuang; Lu, Xingjie; Mauritz, Marguerite; Natali, Susan M.; Pegoraro, Elaine; Penton, C. Ryan; Plaza, César; Salmon, V. G.; Celis, Gerardo; Cole, James R.; Konstantinidis, Konstantinos T.; Tiedje, James M.; Zhou, Jizhong; Schuur, Edward A. G.; Luo, Yiqi

doi:10.1111/gcb.14325

Cited by 28 publications

(39 citation statements)

References 74 publications

(166 reference statements)

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“…For example, the 9 year warming treatment in a tallgrass prairie in Great Plains of USA decreases allocation of gross primary production to shoot, and turnover rates of both shoot and root carbon pools but increases the turnover rates of litter and fast soil carbon pools (Shi, Xu, et al, ). Experimental warming in Alaska tundra significantly changes three out of the 16 parameters: light use efficiency (LUE), baseline (i.e., environment‐corrected) turnover rates of the fast and slow soil organic carbon (SOC) pools (Liang et al, ). When different sets of parameter values are used in model predictions, predicted carbon sequestration in terrestrial ecosystems is substantially different in response to global change (Liang et al, ; Xu et al, ).…”

Section: Are Parameters Constant or Varying?mentioning

confidence: 99%

“…To represent observations from the warming experiment at the Eight Mile Lake, Alaska, Liang et al () assimilated six data sets into Terrestrial ECOsystem (TECO) model to optimally estimate 16 parameters. The TECO model uses multiple soil layers to track dynamics of thawed soil under different warming treatments.…”

Section: Processes At Resolved Versus Unresolved Scales For a Modelmentioning

confidence: 99%

“…Warming‐induced parameter change over treatment time. (a) Light use efficiency; (b) baseline turnover rate of the fast soil organic carbon (SOC) pool ( k fast ); (c) baseline turnover rate of the slow SOC pool ( k slow ; from Liang et al, )…”

Section: Changing Properties Of Evolving Systemsmentioning

confidence: 99%

“…When different sets of parameter values are used in model predictions, predicted carbon sequestration in terrestrial ecosystems is substantially different in response to global change (Liang et al, 2018;Xu et al, 2006).…”

Section: Are Par Ame Ter S Con S Tant or Varying?mentioning

confidence: 99%

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Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Luo

Schuur

2020

Global Change Biology

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Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.

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Section: Are Parameters Constant or Varying?mentioning

confidence: 99%

Section: Processes At Resolved Versus Unresolved Scales For a Modelmentioning

confidence: 99%

Section: Changing Properties Of Evolving Systemsmentioning

confidence: 99%

Section: Are Par Ame Ter S Con S Tant or Varying?mentioning

confidence: 99%

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Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Luo

Schuur

2020

Global Change Biology

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“…This study provides explicit, robust evidence of the persistence of soil microbial respiratory acclimation to warming-induced rising temperature and reducing moisture over long periods. If this phenomenon holds over larger spatial scales across different ecosystems, soil microbial respiratory acclimation globally may have a greater mitigating impact than expected on climate warming-induced CO 2 losses 32 . If the results from this study are applicable to other grasslands globally 33 , the microbial acclimation could lead to 0.49±0.…”

Section: Discussionmentioning

confidence: 99%

Lower Soil Carbon Loss Due to Persistent Microbial Adaptation to Climate Warming

Guo

Gao

Yuan

et al. 2020

Preprint

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40Soil microbial respiration is an important source of uncertainty in projecting future climate and 41 carbon (C) cycle feedbacks. Despite intensive studies for two decades, the magnitude, direction, 42 and duration of such feedbacks are uncertain, and their underlying microbial mechanisms are still 43 poorly understood. Here we examined the responses of soil respiration and microbial community 44 structure to long-term experimental warming in a temperate grassland ecosystem. Our results 45 indicated that the temperature sensitivity of soil microbial respiration (i.e., Q10) persistently 46 decreased by 12.0±3.7% across 7 years of warming. Integrated metagenomic and functional 47 analyses showed that microbial community adaptation played critical roles in regulating 48 respiratory acclimation. Incorporating microbial functional gene abundance data into a 49 microbially-enabled ecosystem model significantly improved the modeling performance of soil 50 microbial respiration by 5-19%, compared to the traditional non-microbial model. Model 51 parametric uncertainty was also reduced by 55-71% when gene abundances were used. In addition, 52 our modeling analyses suggested that decreased temperature sensitivity could lead to considerably 53 less heterotrophic respiration (11.6±7.5%), and hence less soil C loss. If such microbially mediated 54 dampening effects occur generally across different spatial and temporal scales, the potential 55 positive feedback of soil microbial respiration in response to climate warming may be less than 56 previously predicted. 57 Introduction 58 59Soil stores large quantities of organic carbon (C), about three times more C than the Earth's 60 atmosphere 1,2 . Soil respiration is the largest single source of carbon dioxide (CO2) from terrestrial 61 ecosystems to the atmosphere, whose magnitude is about ten times larger than anthropogenic 62 emissions 3 . Soil total respiration (Rt) includes both autotrophic respiration (Ra) from plant root 63 growth and root biomass maintenance, and heterotrophic respiration (Rh) from microbial 64 decomposition of litter and soil organic matter (SOM). Various short-term experiments show that 65 soil respiration increases exponentially with temperature 4 , which has been used as a general 66 relationship to parameterize ecosystem and Earth System Models (ESMs) 5 . If the near-exponential 67 short-term relationship of soil respiration and temperature holds for the long-term (years to 68 decades), climate warming will trigger a sharp increase in ecosystem respiration. Such an increase 69 could then result in a strong positive feedback to the global C cycle 6 , which is dependent on the 70 responses of Rh and the dynamics of detrital inputs under warming 7 . Therefore, it is particularly 71 important to accurately evaluate soil Rh and its response to climate warming. However, partitioning 72 Rt into Ra and Rh is one of the main challenges in both experiment-and model-based global change 73 research 8 . Consequently, soil respiration is a poorly understood key ...

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Matrix approach to land carbon cycle modeling

Luo¹

2021

Preprint

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Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin-up of land carbon cycle models by tens of times. The accelerated spin-up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever-increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.Plain Language Summary Earth system models (ESMs) are the tools we have to predict future states of climate and ecosystems. However, land carbon cycle models, a critical component of ESMs, are highly diverse in both structures and predictions, hindering our ability to obtain consistent future projections. The latter is needed as part of the evidence base to inform climate change mitigation strategies. This paper describes a matrix approach that unifies land carbon cycle models in one matrix form. The matrix models offer consistency and simplicity in structure that make the models analytically tractable. In addition, the matrix approach provides a theoretical framework for understanding the general behavior of the land carbon cycle. More importantly, the matrix approach solves some key contemporary issues in land carbon cycle modeling, such as pinning down sources of model uncertainty and accelerating spin-up. The accelerated spin-up speeds up land carbon cycle simulations by tens of times, making it feasible to perform parameter sensitivity analysis and data assimilation to constrain models with big data. Overall, the matrix approach represents a step change forward for underst...

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Biotic responses buffer warming‐induced soil organic carbon loss in Arctic tundra

Cited by 28 publications

References 74 publications

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Lower Soil Carbon Loss Due to Persistent Microbial Adaptation to Climate Warming

Matrix approach to land carbon cycle modeling

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