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Article

Assessment of Terrestrial Carbon Sinks in China Simulated by Multiple Vegetation Models

by
Weiyi Xu
1,
Jing Liu
1,*,
Longgao Chen
1 and
Suchen Ying
2
1
School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China
2
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(6), 1246; https://doi.org/10.3390/land14061246
Submission received: 5 May 2025 / Revised: 4 June 2025 / Accepted: 6 June 2025 / Published: 10 June 2025
(This article belongs to the Special Issue Land-Based Greenhouse Gas Mitigation for Carbon Neutrality (2nd Edition))
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Abstract

China plays an important role in the global terrestrial carbon cycle. While China is included in global assessments of the carbon cycle, such as the global carbon budget, the performance of dynamic global vegetation models (DGVMs) over China has rarely been evaluated. This knowledge gap constrains both model applicability and region-specific parameter optimization within China. To address this gap, our study assesses the performance of terrestrial carbon stocks and sinks simulated by 12 DGVMs in China from 1970 to 2018. The results indicate that (1) there is significant variation in the numerical magnitudes of terrestrial carbon stocks as simulated by various models, with mean vegetation carbon at 38.3 PgC and mean soil carbon at 115.3 PgC. Nevertheless, their spatial distribution demonstrates a remarkable degree of congruence. Notably, the simulated carbon stocks are generally in excess of existing estimates. (2) Despite the good consistency in the spatial distribution of terrestrial carbon sinks across different models, there is considerable fluctuation in the numerical values, with a mean carbon sink of 0.02 PgC yr−1, a value lower than pre-existing estimations. (3) The responses of terrestrial carbon stocks and sinks to CO2 fertilization, climate change, and land use change exhibit pronounced heterogeneity. CO2 fertilization has a positive effect, whereas land use change has a negative one. The impact of climate change is variable, and the carbon sink effect engendered by CO2 fertilization is negated by the adverse influence of land use change. This comprehensive evaluation of the simulation performance of DGVMs in China is anticipated to serve as an important reference for the functional analysis and parameter optimization of DGVMs within China.

1. Introduction

Since the 19th century, rising atmospheric CO2 concentrations have been mainly ascribed to the consumption of fossil fuels and alterations in land use patterns during industrialization. These CO2-related greenhouse gas emissions have exerted a profound influence on global climate change [1,2,3,4,5]. Over the past 50 years, approximately 60% of the anthropogenic CO2 emissions have been offset by these natural sinks. Notably, the terrestrial ecosystem carbon sinks (hereinafter referred to as the terrestrial carbon sinks) constitute 31% of the total ecosystem carbon sinks [1,6]. Research findings reveal that the terrestrial carbon sinks underwent a transition from being a source (−0.2 ± 0.9) PgC yr−1 (1 Pg = 109 t) in the 1960s to a sink (1.9 ± 1.1) PgC yr−1 in the 2010s, with an annual net carbon uptake ranging from 2.0 to 3.4 PgC [7]. Evidently, terrestrial ecosystems assume a crucial role in regulating atmospheric CO2 levels and combating climate change by functioning as substantial carbon sinks and sources.
Since the 1990s, global research on the terrestrial ecosystem carbon balance has advanced methodologies for the estimation and understanding of carbon sink/source dynamics [8,9]. Current approaches for quantifying the regional terrestrial ecosystem carbon balance are generally categorized into two categories: “bottom-up” and “top-down” [10]. Bottom-up estimation approaches extrapolate ground-based observations and model simulation outputs from plot-scale measurements to broader spatial extents. Prominent techniques employed in this category include the inventory method, eddy correlation method, and ecosystem process model simulation method. Meanwhile, top-down estimation approaches primarily rely on atmospheric inversion techniques to quantify terrestrial carbon sinks by analyzing spatiotemporal variations in atmospheric CO2 concentrations. Process-based ecosystem models estimate gridded carbon sinks through mechanistic simulations of biogeochemical processes. These models serve as indispensable tools for assessing terrestrial carbon dynamics within initiatives such as the Global Carbon Project, offering a unique capability to quantify the relative contributions of multiple environmental drivers to observed changes in carbon storage [1]. They also project future carbon sink dynamics under changing environmental conditions. Dynamic global vegetation models (DGVMs) represent a prototypical class of process-based models that simulate the responses of terrestrial ecosystems to climate variability through explicit representations of plant physiological processes, vegetation phenology, and biogeochemical cycling. These models have been widely applied in quantifying carbon cycle dynamics [11,12]. Notably, the Global Carbon Project launched the “Trends and drivers of the regional-scale sources and sinks of carbon dioxide” (TRENDY) initiative to systematically assess and intercompare global-scale terrestrial carbon–climate interactions. As a multi-model ensemble of DGVM, TRENDY currently constitutes the widely cited international platform for evaluating ecosystem model performance. This initiative provides key data for annual global carbon budget assessments and forms the foundation for advancing mechanistic understanding of terrestrial carbon cycle feedback [13,14].
As a crucial component of the global ecosystem, China’s terrestrial ecosystems play a critical role in regulating the global carbon cycle. China contributes 10–31% of the global land carbon sink, with approximately 6.5% of the world’s land area [10]. Meanwhile, concurrent studies have demonstrated that the continuous growth of forest carbon sinks in China directly underpins the overall enhancement of global temperate forest carbon sinks. Moreover, land use change in China has transitioned into a significant carbon sink system, whose carbon sequestration efficiency has long been underestimated in global assessment frameworks [3]. While these ecosystems have been incorporated into global carbon budget assessments and DGVM simulations, the performance of DGVMs across China’s diverse ecosystems remains inadequately evaluated. Given the centrality in contemporary carbon cycle dynamics, systematic validation of DGVMs within China is of utmost scientific and practical significance. Such evaluations not only provide critical insights into model discrepancies but also facilitate the refinement of parameterization schemes.
Therefore, this study employs an ensemble of 12 DGVMs to investigate the spatiotemporal dynamics of terrestrial carbon stocks and carbon sinks across China from 1970 to 2018. Our analysis systematically compares inter-model discrepancies while comparing these findings with the existing studies. Through multi-scenario simulation analyses, this study further elucidates the differential responses of terrestrial carbon stocks and sinks to three key drivers: CO2 fertilization effects, climate change, and land use change. Our study highlights the comparison of simulation results from different DGVMs over China and their discrepancies with the existing consensus on China’s carbon cycle, thereby providing a basis for optimizing regional model parameters to improve model applicability in China. More specifically, this study focuses on three critical dimensions: inter-model variance analysis, process attribution assessment, and comparative validation against existing studies.

2. Materials and Methods

2.1. Data Sources

As a prominent ecosystem model intercomparison project, TRENDY exerts a substantial influence on global carbon cycle research. This study employs simulation data from 12 DGVMs included in TRENDY v8 (https://doi.org/10.18160/gcp-2019 (accessed on 4 May 2025)) (Table 1), covering the period of 1970–2018 under the S0–S3 scenarios. The selected variables included vegetation carbon stocks (Cveg), soil carbon stocks (Csoil), and net biome productivity (NBP). Critically, NBP serves as a quantitative metric for terrestrial carbon sink/source dynamics in global change studies, where NBP > 0 denotes a carbon sink, whereas NBP < 0 indicates a carbon source. In this study, NBP was used to characterize the magnitude of terrestrial carbon sinks. Table 1 provides a detailed overview of the 12 DGVMs included in this analysis.
DGVMs features four simulations: S0—the baseline scenario, with a fixed pre-industrial atmospheric CO2, climate, and land use. S1—transient atmospheric CO2, with a fixed pre-industrial climate and land use, which characterizes the impacts of the CO2 fertilization effect. S2—transient atmospheric CO2, with a transient climate and fixed pre-industrial land use, which is used to characterize the combined impacts of the CO2 fertilization effect and climate change. S3—transient atmospheric CO2, a transient climate, and transient industrial land use, which represents a simulation of actual environmental conditions. For the underlying forcing data and protocol, refer to Friedlingstein et al. [12]. All runs were performed within the TRENDY v8 efforts for the GCB2019.

2.2. Methods

All simulation outputs from DGVMs were stored in a NetCDF file. To facilitate subsequent processing and analysis, these NC files were converted to the TIFF format using R software (R 4.4.2). Although the DGVMs provide global coverage, substantial disparities exist in spatial and temporal resolutions across different models. To address these inconsistencies, further data processing was implemented. Focusing on the study region, the R program was used to apply a spatial mask to the global datasets and extract domain-specific data for China. To harmonize the heterogeneous resolutions, this study remapped all model outputs to a common 0.5° grid using conservative regridding and aggregated monthly data to annual intervals. This preprocessing step established a robust foundation for subsequent research and analytical procedures.
Furthermore, an analysis of the four simulation scenarios enabled the differentiation of the driving impacts of the CO2 fertilization effect, climate change, and land use change on terrestrial carbon stocks and sinks. Specifically, the impact of the CO2 fertilization effect was defined as the difference between S1 and S0 (S1 minus S0), climate change was represented by the difference between S2 and S1 (S2 minus S1), and land use change was quantified as the difference between S3 and S2 (S3 minus S2). These pairwise differences allowed for a quantitative comparison of the individual contributions of each factor to terrestrial carbon stocks and sinks.

3. Results

3.1. Spatiotemporal Evolution of Terrestrial Carbon Stocks

Based on model-derived results from DGVMs under the S3 scenario, the analysis of terrestrial carbon stock dynamics from 1970 to 2018 revealed pronounced temporal variability in simulated Cveg (Figure 1a). Across models, Cveg exhibited a consistent trend of initial decline followed by increase (Figure 1b), and the simulated trends of Cveg across different models were generally consistent, with Kendall’s W coefficient reaching 0.98 (p < 0.01), albeit with substantial inter-model discrepancies in mean carbon stocks (Figure 1c). Specifically, Cveg estimates ranged from 18.8 PgC (LPJ-GUESS) to 67.71 PgC (CLASS-CTEM), representing a nearly threefold difference between the lowest and highest model outputs, and the DGVMs’ ensemble mean was 38.3 PgC. Meanwhile, the coefficient of variation (CV) in the estimates was 40%, indicating a large degree of dispersion. Similarly, Csoil displayed marked inter-model variability (Figure 1d), with the simulated trends of Csoil across different models being generally consistent, as evidenced by a Kendall’s W coefficient of 0.97 (p < 0.01), characterized by an overall upward trend in temporal profiles accompanied by substantial value fluctuations (Figure 1e). Csoil estimates spanned from 40.94 PgC (JSBACH) to 212.20 PgC (JULES-ES) (Figure 1f), reflecting a fivefold difference between the lowest and highest model estimates, and the DGVMs’ ensemble mean was 115.3 PgC. The coefficient of variation (CV) in these estimates was 40%, the same as that of Cveg estimates, indicating a similarly large degree of dispersion.
As illustrated in Supplementary Materials Figures S1 and S2, the spatial distribution patterns of carbon stocks simulated by DGVMs under the S3 scenario were presented. Overall, the DGVMs exhibited a strong consistency in depicting the spatial distribution of carbon stocks. Notably, regions with high Cveg were primarily concentrated in Northeast and South China (see Figure S1), areas characterized by a dense vegetation coverage [27]. In contrast, Csoil hotspots were predominantly located in Northeast and Southwest China, whereas relatively lower values were observed in the eastern regions (see Figure S2). Overall, the spatial distribution of terrestrial carbon stocks in China simulated by DGVMs showed a high degree of consistency with previous findings [3,7].

3.2. Spatiotemporal Evolution of Terrestrial Carbon Sinks

Based on annual NBP from 1970 to 2018 simulated by DGVMs, this study characterized the temporal dynamics of terrestrial carbon sinks in China, as illustrated in Figure 2. Notably, the interannual variability of China’s terrestrial carbon sinks were substantial, with annual value fluctuating between −0.34 and 0.26 PgC yr−1 and averaging 0.02 PgC yr−1 over the study period. Such interannual variability contributed significantly to the uncertainty in cumulative carbon sinks estimated across model simulations. The range of cumulative terrestrial carbon sinks derived from different models spanned from −5.47 PgC (SDGVM) to 5.95 PgC (CABLE-POP), with an ensemble mean of 0.75 PgC. However, the simulated trends of carbon sinks across different models showed a relatively consistent pattern, with a Kendall’s W of 0.83 (p < 0.01). Most models showed that carbon sinks exhibited a trend of first decreasing and then increasing over time. Furthermore, four models (CLASS-CTEM, CLM5.0, ORCHIDEE-CNP, and SDGVM) projected China’s land as a net carbon source over the period (Figure 2b), while the remaining models represented a net carbon sink.
Supplementary Materials Figure S3 depicts the spatial patterns of terrestrial carbon sinks across China. Overall, the carbon sinks exhibited pronounced regional concentration, with dominant distributions in northeastern and southwestern China. In contrast, carbon source regions were primarily identified in the northeastern region, the North China Plain, and southeastern hilly areas. These observations were broadly consistent with the findings from previous research [27]. Notable discrepancies emerged, however, when comparing spatial distributions simulated by different models. While a consensus existed regarding the prevalence of carbon sinks in northeastern and southwestern China, significant model-based variations were evident in carbon source patterns. For example, OCN and SDGVM explicitly identified carbon source signals in northwestern China, which were less pronounced in other model projections.

3.3. Responses of Terrestrial Carbon Stocks and Sinks to Different Drivers

Analysis of model simulations for terrestrial carbon stocks and sinks under multiple scenarios highlights the significant roles of CO2 fertilization, climate change, and land use change in driving carbon stock and sink dynamics. These findings are visualized in Figure 3. Generally speaking, the influences of these drivers on carbon cycling exhibited notable variability. The CO2 fertilization effect exerted a universally positive influence on Cveg across all models (Figure 3a), though with model-dependent magnitudes. Estimated increases in Cveg driven by CO2 fertilization ranged from 3.86 PgC (JULES-ES) to 7.70 PgC (ORCHIDEE-CNP), with a mean value of 5.78 PgC (Figure 3d). In contrast, model projections of climate change impacts on Cveg showed substantial discrepancies in both direction and magnitude. In the analyzed models, eight (CABLE-POP, CLASS-CTEM, DLEM, JSBACH, JULES-ES, LPJ-GUESS, SDGVM, and VISIT) indicated positive climate-driven carbon gains, whereas three models (CLM5.0, ORCHIDEE, and ORCHIDEE-CNP) projected opposing effects. In terms of impact magnitude, climate change exerted a substantially smaller influence compared to the CO2 fertilization effect. Land use change, conversely, induced consistent declines in Cveg, with model-specific losses ranging from 5.13 PgC (JULES-ES) to 13.81 PgC (JSBACH), with a mean value of 8.22 PgC (Figure 3d). These results indicated that the majority of vegetation carbon sinks generated by CO2 fertilization were offset by carbon emissions from land use change.
The CO2 fertilization effect emerged as a prominent positive determinant of Csoil change (Figure 3b), mirroring its influence on Cveg. Nevertheless, the magnitude of this effect on Csoil exhibited variability across different models. The increases in Csoil attributable to the CO2 fertilization effect spanned from 2.25 PgC (LPJ-GUESS) to 6.55 PgC (CABLE-POP), with a mean value of 3.90 PgC (Figure 3d). In contrast, with the exception of SDGVM, all other models indicated a negative impact of climate change on Csoil. This negative influence led to a decline in Csoil, with the decrease ranging from 0.01 PgC (VISIT) to 1.07 PgC (LPJ-GUESS). When it comes to land use change, the majority of models (including CLASS-CTEM, DLEM, JSBACH, JULES-ES, LPJ-GUESS, ORCHIDEE, ORCHIDEE-CNP, SDGVM, and VISIT) showed a negative response of Csoil to land use change. This response resulted in a carbon loss that ranged from 0.37 PgC (LPJ-GUESS) to 5.20 PgC (CLASS-CTEM). Significantly, CABLE-POP and CLM5.0 deviated from this trend, showing carbon increases of 0.33 PgC and 1.12 PgC, respectively. The overall change in Csoil driven by land use change resulted in a carbon loss of 1.9 PgC (Figure 3d).
Consistency among DGVMs was evident in the primary role of the CO2 fertilization effect and land use change on terrestrial carbon sinks. However, substantial discrepancies existed in projections of climate change impacts. Specifically, the CO2 fertilization effect exerted a positive influence on terrestrial carbon sinks, promoting carbon accumulation with model-derived increases ranging from 7.63 PgC (SDGVM) to 14.47 PgC (JULES-ES), with a mean value of 11.61 PgC (Figure 3d). In contrast, land use change consistently demonstrated detrimental effects, though the magnitude of sink depletion varied significantly across models, from a net loss of 15.50 PgC (JSBACH) to a comparatively smaller reduction of 4.64 PgC (ORCHIDEE), with a mean value of 10.96 PgC (Figure 3d). These results indicated that CO2 fertilization-driven carbon sequestration benefits were largely offset by carbon emissions from land use change. Furthermore, climate change impacts on terrestrial carbon sinks exhibited notable variability in both direction and magnitude. Among the models, five (CABLE-POP, CLM5.0, LPJ-GUESS, ORCHIDEE, and ORCHIDEE-CNP) projected adverse effects, with climate-induced changes ranging from a net carbon loss of 4.25 PgC (ORCHIDEE-CNP) to a marginal gain of 3.10 PgC (JSBACH).

3.4. Comparative Analysis of This Study and Existing Studies

China’s carbon sinks account for 10–31% of total terrestrial carbon sinks [10]. With the implementation of ecological restoration projects and land use management in China, carbon sinks in China’s terrestrial ecosystems are expected to be further enhanced [3]. Thus, accurate assessment of the carbon sequestration capacity in China’s terrestrial ecosystems represents a key component in unraveling the global carbon budget balance. Over the past two decades, researchers have utilized diverse methodologies to estimate the terrestrial carbon sinks in China. However, estimates from these approaches exhibited substantial discrepancies [7,10,28,29]. To assess the performance of DGVMs, this study compared the model simulation results with those obtained from alternative estimation methods (Figure 4). DGVMs’ ensemble projected the Cveg of 38.3 PgC for China (Figure 4a), contrasting sharply with earlier estimates. For example, Fang et al. [30] reported 6.1 PgC based on historical statistical data and literature synthesis, while Ni et al. [31] derived 35.2 PgC using carbon density datasets from published studies. Xu et al. [32] combined literature data with plot-level measurements to estimate vegetation carbon at 14.6 PgC for the period of 2004–2014, a value closely matching Tang et al.’s 14.3 PgC estimate [27], which relied on intensive sampling from the “Priority Program of Carbon Budget” of the Chinese Academy of Sciences in 2011. More recently, studies using terrestrial biosphere models reported initial vegetation carbon stocks ranging from 13.3 to 14.0 PgC [33,34,35]. Excluding the outlying values from early-stage research, the consensus estimated for China’s vegetation carbon stocks converged to 13–15 PgC, with a relative error of 155–195% compared to model simulations. Notably, DGVM simulations consistently yield higher estimates than the consensus estimate, potentially reflecting differences in model structures, input datasets, or process representations.
According to DGVMs, the estimated Csoil in China was 115.3 PgC (Figure 4b), which was significantly lower than values derived from prior survey-based estimates. Early research by Fang et al. [30] reported a national soil carbon stock of 185.7 PgC, based on data from 745 soil profiles across China. This estimate was later revised to 119.8 PgC by Ni et al. [31], who integrated carbon density data from the published literature. However, subsequent studies including those utilizing data from the Second National Soil Census have consistently reported soil carbon stocks in the range of 69–92 PgC [36,37,38,39,40,41,42,43]. The “Priority Program of Carbon Budget” further narrowed this range to 75–86.5 PgC [27,44], with estimates from terrestrial biosphere models [33,35] aligning closely with plot-based observations at 83 PgC. After excluding anomalous values from earlier investigations, the most robust estimates of China’s soil carbon stocks converged within 69–92 PgC, with a relative error of 25–67% compared to model simulations. In contrast, DGVM simulations systematically overestimated this parameter relative to existing estimates.
Figure 4c illustrates a comparison of terrestrial carbon sink estimates derived from different methodologies. DGVMs simulated a mean terrestrial carbon sink of 0.02 PgC yr−1 for China, indicating that China’s terrestrial ecosystem acts as a carbon sink, which was consistent with previous research [10]. Notable discrepancies existed, however, in the magnitude of these estimates. Inventory-based approaches reported terrestrial carbon sink ranging from 0.16 to 0.20 PgC yr−1 [45,46,47], while terrestrial biosphere model simulations yielded values between 0.07 and 0.18 PgC yr−1 [48,49,50]. Atmospheric inversion methods produced substantially larger estimates, spanning 0.28–0.35 PgC yr−1, which surpass those from both inventory-based approaches and biosphere models [51,52,53]. A special example was Wang et al. [52], utilizing data from six greenhouse gas observatories and international atmospheric CO2 monitoring networks, who reported a terrestrial carbon sink of 1.11 PgC yr−1 for China via atmospheric inversion, a value significantly higher than earlier projections. Flux measurement-based calculations exhibited the broadest range, from 1.18 to 1.91 PgC yr−1 [54,55]. Collectively, these results highlighted that DGVM-derived estimates of China’s terrestrial carbon sinks were notably lower than those obtained through alternative methodologies.
Land 14 01246 g004
Figure 4. Comparison of vegetation carbon stock (Cveg (a)), soil carbon stock (Csoil (b)), and terrestrial carbon sink (NBP (c)) estimates between this study and previous studies [27,29,30,31,32,33,34,35,36,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55].
Figure 4. Comparison of vegetation carbon stock (Cveg (a)), soil carbon stock (Csoil (b)), and terrestrial carbon sink (NBP (c)) estimates between this study and previous studies [27,29,30,31,32,33,34,35,36,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55].
Land 14 01246 g004

4. Discussion

The comparison of DGVM simulations with existing studies underscores the need for continuous improvement in model structures and parameterizations. Due to the differences in specific time frames focused on by previous studies, the DGVM simulation results may exhibit certain discrepancies from the terrestrial carbon stocks and sinks in different time periods, though their magnitudes should remain largely consistent. So, overall, DGVM simulations of China’s terrestrial carbon stocks generally exceed values reported in the existing literature, whereas estimated carbon sink magnitudes are significantly lower than previously documented results (see Figure 4). These discrepancies could arise from various sources, including the inherent limitations of DGVMs in representing complex ecological processes, differences in input datasets, and spatial and temporal scales of observations. Furthermore, considering the unique regional specificities of China, this study hereby presents preliminary reflections on the potential sources of such discrepancies. Firstly, systematic differences in methodological frameworks, such as variations in carbon cycle parameterization schemes and data sources (including remote sensing inversions, ground-based observations, and model-derived outputs) across estimation approaches, can lead to cumulative errors in carbon flux partitioning. Secondly, China’s unique land use change dynamics, shaped by policy-driven initiatives like the “Grain for Green Program” and large-scale afforestation, profoundly influence the terrestrial carbon balance. Related studies have shown that land use change in China, characterized by a rapid forest expansion since 1980, contributed to nearly 44% of the national terrestrial carbon sink [56]. However, these high-intensity, policy-mediated human activities are not fully integrated into current DGVM simulations.
To address the above issue, future improvement directions should focus on incorporating key processes such as high-intensity, policy-driven human activities specific to China into DGVMs [28,56,57]. Firstly, they should enhance the quantitative characterization of policy-driven factors, such as establishing dynamic parameter sets covering policy interventions like afforestation, farmland management, and ecological engineering and constructing high-resolution human activity datasets by integrating remote sensing with ground-based survey data. Secondly, they should embed policy response modules into the model structure, simulating the cascading impacts of human activities under different policy scenarios on vegetation physiology, community composition, and carbon–nitrogen cycling by coupling policy implementation intensity with land use change processes [3]. Thirdly, they should develop a multi-scale validation framework that integrates ground observations, remote-sensing inversion, and social survey data to carry out model parameter optimization and uncertainty analysis for typical regions with policy implementation. The aforementioned measures are expected to refine the model structure, optimize model parameters, and thereby improve the simulation accuracy of DGVMs.

5. Conclusions

DGVMs serve as core tools for simulating and analyzing the global carbon cycle, playing a pivotal role in global change research. However, prior to this study, the simulation performance of DGVMs across China’s terrestrial ecosystems, which is critical for accurately quantifying the nation’s terrestrial carbon sink capacity, has remained systematically unevaluated. To address this research gap, we conducted a comprehensive multi-model intercomparison of 12 DGVMs simulating China’s terrestrial carbon stocks and sinks over the period of 1970–2018. Although the spatial patterns of carbon stocks and sinks simulated by different models exhibit consistency, significant discrepancies exist in their quantitative estimates, with the coefficient of variation reaching 40%. Meanwhile, the CO2 fertilization effect exerts a positive influence on both carbon stocks and sinks, whereas land use change exerts a negative impact. Importantly, the carbon sink enhancement from CO2 fertilization is partially offset by the carbon source effect induced by land use change. Comparisons with previous studies reveal that DGVM-simulated terrestrial carbon stocks are generally higher than reported values, while estimated carbon sink magnitudes are lower. The underlying mechanisms for these discrepancies require further investigation in future research. Multi-model intercomparison is critical for advancing model development, as it helps identify structural biases and data limitations. By using China as a laboratory, this work systematically assesses DGVMs’ capabilities in representing the terrestrial carbon cycle, providing essential insights for optimizing regional model parameters and improving the accuracy of China’s terrestrial carbon sink estimates.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/land14061246/s1, Figure S1: Spatial distribution of vegetation carbon stocks (Cveg) averaged over 1970–2018 under the S3 scenario. Panels (a1–a12) show the spatial distribution of Cveg simulated by different models. Panel (b) shows the mean distribution of DGVMs’ ensemble; Figure S2: Spatial distribution of soil carbon stocks (Csoil) averaged over 1970–2018 under the S3 scenario. Panels (a1–a12) show the spatial distribution of Csoil simulated by different models. Panel (b) shows the mean distribution of DGVMs’ ensemble; Figure S3: Spatial distribution of cumulative NBP from 1970 to 2018 under the S3 scenario. Panels (a1–a12) show the spatial distribution of cumulative NBP simulated by different models. Panel (b) shows the mean distribution of cumulative NBP of DGVMs’ ensemble.

Author Contributions

W.X., writing—original draft, methodology, data curation, funding acquisition, formal analysis; J.L., supervision, methodology, funding acquisition; L.C., writing—review and editing, data curation; S.Y., data curation, visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20241666) and the National Natural Science Foundation of China (Grant Nos. 42401316 and 42201269).

Data Availability Statement

The data presented in this study are available on request from the corresponding author, the data are not publicly available due to privacy.

Acknowledgments

We thank all people and institutions within the “Trends and drivers of the regional-scale sources and sinks of carbon dioxide” (TRENDY) modeling groups who contributed to the data used in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Carbon stored in vegetation (Cveg) and soil (Csoil) for the DGVMs during 1970–2018 under the S3 scenario. Panels (a,d) show the temporal changes in Cveg and Csoil, respectively, summed over China for each model. Panels (b,e) show the temporal changes and means (blue bar) of Cveg and Csoil, respectively, summed over China for DGVMs’ ensemble. Panels (c,f) show the means of Cveg and Csoil for each model, respectively.
Figure 1. Carbon stored in vegetation (Cveg) and soil (Csoil) for the DGVMs during 1970–2018 under the S3 scenario. Panels (a,d) show the temporal changes in Cveg and Csoil, respectively, summed over China for each model. Panels (b,e) show the temporal changes and means (blue bar) of Cveg and Csoil, respectively, summed over China for DGVMs’ ensemble. Panels (c,f) show the means of Cveg and Csoil for each model, respectively.
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Figure 2. Ensemble mean of NBP (a) and cumulative NBP (b) for individual models summed over China during 1970–2018 under the S3 scenario. The solid red line in (a) shows the ensemble mean of annual NBP, the bar chart in (a) shows the mean of annual NBP during the study period, and the shaded red area shows the range across models. The values in (b) represent the cumulative NBP simulated by different models.
Figure 2. Ensemble mean of NBP (a) and cumulative NBP (b) for individual models summed over China during 1970–2018 under the S3 scenario. The solid red line in (a) shows the ensemble mean of annual NBP, the bar chart in (a) shows the mean of annual NBP during the study period, and the shaded red area shows the range across models. The values in (b) represent the cumulative NBP simulated by different models.
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Figure 3. Drivers of vegetation carbon stocks (Cveg (a)), soil carbon stocks (Csoil (b)), terrestrial carbon sink (NBP (c)), and ensemble mean (mean (d)) from CO2 fertilization (CO2), climate change (CLIM), and land use change (LUC) in different models from 1970 to 2018. “max” and “min” in (a–c) show the maximum and minimum values of the driving effect. Note that OCN is not included due to a lack of data.
Figure 3. Drivers of vegetation carbon stocks (Cveg (a)), soil carbon stocks (Csoil (b)), terrestrial carbon sink (NBP (c)), and ensemble mean (mean (d)) from CO2 fertilization (CO2), climate change (CLIM), and land use change (LUC) in different models from 1970 to 2018. “max” and “min” in (a–c) show the maximum and minimum values of the driving effect. Note that OCN is not included due to a lack of data.
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Table 1. Overview of the DGVM outputs provided and used in this study.
Table 1. Overview of the DGVM outputs provided and used in this study.
ModelSpatial ResolutionTemporal ResolutionReferences
CvegCsoilNBP
CABLE-POP 1° × 1°am[15]
CLASS-CTEM2.79° × 2.79°am[16]
CLM5.00.9° × 1.25°am[17]
DLEM0.5° × 0.5°aa[18]
JSBACH1.875° × 1.875°am[19]
JULES-ES1.25° × 1.875°am[20]
OCN1° × 1°am[21]
ORCHIDEE0.5° × 0.5°am[22]
ORCHIDEE-CNP2° × 2°am[23]
SDGVM0.5° × 0.5°am[24]
VISIT0.5° × 0.5°mm[25]
LPJ-GUESS0.5° × 0.5°aa[26]
Note: “a” refers to annually and “m” refers to monthly.
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Xu, W.; Liu, J.; Chen, L.; Ying, S. Assessment of Terrestrial Carbon Sinks in China Simulated by Multiple Vegetation Models. Land 2025, 14, 1246. https://doi.org/10.3390/land14061246

AMA Style

Xu W, Liu J, Chen L, Ying S. Assessment of Terrestrial Carbon Sinks in China Simulated by Multiple Vegetation Models. Land. 2025; 14(6):1246. https://doi.org/10.3390/land14061246

Chicago/Turabian Style

Xu, Weiyi, Jing Liu, Longgao Chen, and Suchen Ying. 2025. "Assessment of Terrestrial Carbon Sinks in China Simulated by Multiple Vegetation Models" Land 14, no. 6: 1246. https://doi.org/10.3390/land14061246

APA Style

Xu, W., Liu, J., Chen, L., & Ying, S. (2025). Assessment of Terrestrial Carbon Sinks in China Simulated by Multiple Vegetation Models. Land, 14(6), 1246. https://doi.org/10.3390/land14061246

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