Model structures amplify uncertainty in predicted soil carbon responses to climate change

Shi, Zheng; Crowell, Sean; Luo, Yiqi; Moore, Berrien

doi:10.1038/s41467-018-04526-9

Cited by 103 publications

(108 citation statements)

References 51 publications

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“…Soil organic carbon (SOC) is the largest terrestrial carbon (C) pool (Ciais et al, 2013), and it contains more than three times as much C as either the atmosphere or terrestrial vegetation. Therefore, a small change (<1%) in the global SOC pool might drastically alter the land-atmosphere C balance (Heimann & Reichstein, 2008;Shi, Crowell, Luo, Moore, 2018). SOC is also closely related to soil fertility, structure, water holding capacity and ecosystem biogeochemical cycles (Campbell & Paustian, 2015;Six, Bossuyt, Degryze, & Denef, 2004).…”

Section: Introductionmentioning

confidence: 99%

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

Zhang

Goll

Wang

et al. 2020

Global Change Biology

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First‐order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial‐MIneral Carbon Stabilization model), which we compared to the widely used first‐order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.

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Section: Introductionmentioning

confidence: 99%

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

Zhang

Goll

Wang

et al. 2020

Global Change Biology

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“…The observed SOC data for parameter calibration comes from the International Geosphere Biosphere Programme -Data and Information System (IGBP-DIS) dataset (Global Soil Data Task Group, 2000). The IGBP-DIS dataset includes a 1 km resolution global land carbon data set that has been widely used in many studies to evaluate and improve models (Zhou et al, 2009;Smith et al, 2013).…”

Section: Data and Cost Functionmentioning

confidence: 99%

“…As a consequence, the matrix approach makes parameter estimation and calibration possible. The matrix approach has been successfully used for parameter calibration to constrain SOC turnover and microbial process using the Bayesian Markov chain Monte Carlo (MCMC) algorithm (Harauk et al, 2014(Harauk et al, , 2015Shi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Even using highperformance computers, complex land models, like the latest version of Community Land Model (CLM5.0), require a very long spin-up time for carbon cycle simulation, leading to several hours or days for one simulation. Although the matrix approach has been developed to enable data assimilation of global land carbon cycle models (Harauk et al, 2014(Harauk et al, , 2015Shi et al, 2018), Bayesian MCMC is still very computationally expensive for calibrating global land models. More effective and efficient parameter calibration algorithms are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

“…With the help of the surrogate model, many unnecessary simulations with bad parameter values, which lead to high prediction errors, are avoided. SBO has been shown to find the near-optimal parameter values within only a few hundred simulations for different problems (Aleman et al, 2009;Giunta et al, 1997;Regis, 2011;Simpson et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Parameter calibration in global soil carbon models using surrogate-based optimization

Zhang

Luo

et al. 2018

Geosci. Model Dev.

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Abstract. Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie-Ames-Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12 % for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

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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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Model structures amplify uncertainty in predicted soil carbon responses to climate change

Cited by 103 publications

References 51 publications

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

Parameter calibration in global soil carbon models using surrogate-based optimization

Matrix approach to land carbon cycle modeling

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