Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

Lu, Xingjie; Du, Zhenggang; Huang, Yuanyuan; Lawrence, David M.; Kluzek, Erik; Collier, Nathan; Lombardozzi, Danica; Sobhani, Negin; Schuur, Edward A. G.; Luo, Yiqi

doi:10.1029/2020ms002105

“…In this study, we quantified the effects of N deposition fluxes reduction on terrestrial carbon and nitrogen dynamics over China using a widely used model, the Community Land Model version 5 (CLM5) 60 , 61 . The CLM simulates land biogeophysical, biogeochemical, and landscape processes, including surface energy, water, carbon, nitrogen, momentum, and radiative fluxes over different land types and ecosystems 62 .…”

Section: Methodsmentioning

confidence: 99%

Unexpected response of nitrogen deposition to nitrogen oxide controls and implications for land carbon sink

Liu

¹

,

Shang

²

,

Lu

³

et al. 2022

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Terrestrial ecosystems in China receive the world’s largest amount of reactive nitrogen (N) deposition. Recent controls on nitrogen oxides (NOx = NO + NO2) emissions in China to tackle air pollution are expected to decrease N deposition, yet the observed N deposition fluxes remain almost stagnant. Here we show that the effectiveness of NOx emission controls for reducing oxidized N (NOy = NOx + its oxidation products) deposition is unforeseen in Eastern China, with one-unit reduction in NOx emission leading to only 55‒76% reductions in NOy-N deposition, as opposed to the high effectiveness (around 100%) in both Southern China and the United States. Using an atmospheric chemical transport model, we demonstrate that this unexpected weakened response of N deposition is attributable to the enhanced atmospheric oxidizing capacity by NOx emissions reductions. The decline in N deposition could bear a penalty on terrestrial carbon sinks and should be taken into account when developing pathways for China’s carbon neutrality.

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“…The CLM5 matrix model was developed by reorganizing hundreds of carbon and nitrogen equations from six modules into four matrix equations representing carbon and nitrogen cycles of vegetation and soil. This matrix version of CLM5 operates as a replica in parallel with the original version online or offline within the Community Earth System Model (CESM; Lu et al., 2020). Matrix models were reconstructed from numerical outputs of carbon stocks and fluxes among pools for LPJ‐GUESS, ORCHIDEE, and ELM‐ECA (Ahlström et al., 2015; Metzler et al., 2020; Wu et al., 2020).…”

Section: Unifying Land Carbon Cycle Modelsmentioning

confidence: 99%

“…For example, CLM5 with a vertically resolved soil carbon module includes 18 plant pools for each of the 17 vegetation types and 140 soil pools (i.e., 7 pools per layer over 20 layers) in X ( t ) (Lawrence et al., 2019). Thus, CLM5 assigns 158 elements (i.e., 18 for plant + 140 for soil) to X ( t ) for one grid cell if it is occupied by one vegetation type and 194 elements (i.e., 3 × 18 + 140) if the grid is occupied by three vegetation types (Lu et al., 2020). In contrast, the CABLE model has nine pools, therefore X ( t ) has nine elements (Wang et al., 2010).…”

Section: Unifying Land Carbon Cycle Modelsmentioning

confidence: 99%

“…To date, dozens of existing models have been converted into the matrix form, including CLM3.5, CLM4, CLM4.5, CLM5, ORCHIDEE, CABLE, LPJ‐GUESS, and nonlinear microbial models (Ahlström et al., 2015; Huang, Lu, et al., 2018; Huang, Zhu, et al., 2018; Lu et al., 2020; Sierra & Müller, 2015; Wu et al., 2020; Xia et al., 2012). Four coupled carbon‐nitrogen cycle models, CABLE, CLM5, TECO, and GECO, were converted into the matrix form (Lu et al., 2020; Shi et al., 2016; Wang et al., 2022; Xia et al., 2012). A fully coupled carbon‐nitrogen‐phosphorus model, TECO‐CNP, was developed in the matrix form to study effects of nutrient limitations on subtropical forests (Du et al., 2021).…”

Section: Unifying Land Carbon Cycle Modelsmentioning

confidence: 99%

“…Huang, Zhu, et al (2018) converted a version of the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model to the matrix form to facilitate a variance-based sensitivity analysis. Lu et al (2020) fully implemented a matrix approach to represent the interconnected carbon and nitrogen cycle system of CLM5. Meanwhile, the matrix equation has been used for theoretical studies, for example, categorizing decomposition models (Sierra & Müller, 2015), and diagnosing the predictability and dynamic disequilibrium of the land carbon cycle (Luo et al, 2015(Luo et al, , 2017.…”

Section: Introductionmentioning

confidence: 99%

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Matrix Approach to Land Carbon Cycle Modeling

Luo

¹

,

Huang

²

,

Sierra

³

et al. 2022

J Adv Model Earth Syst

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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.

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Matrix approach to land carbon cycle modeling

Luo¹

2021

Preprint

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0

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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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Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

Cited by 14 publications

References 59 publications

Unexpected response of nitrogen deposition to nitrogen oxide controls and implications for land carbon sink

Unexpected response of nitrogen deposition to nitrogen oxide controls and implications for land carbon sink

Matrix Approach to Land Carbon Cycle Modeling

Matrix approach to land carbon cycle modeling

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