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Multiple lines of existing evidence suggest that increasing CO

Nitrogen (N) availability is one of the critical limiting factors regulating plant growth, microbial activity, and the interactions between plants and soil microorganisms. The competition between plants and microorganisms, represented by the ratio of microbial N immobilization and plant N uptake (measured as Nim: PNU ratio), generally reflects the degree of N limitation in a terrestrial ecosystem. However, the key factors driving the pattern of Nim: PNU ratio across global ecosystems remain little understood. Here, using a global data set of 1022 observations from 184 studies, we examined the relative importance of mycorrhizal associations, N availability, climate, plant, and soil properties on the Nim: PNU ratio. Our results show that mycorrhizal fungi type (arbuscular mycorrhizal (AM) vs. ectomycorrhizal (EM) fungi) in combination with soil N availability explain the variation in the Nim: PNU across terrestrial ecosystems. In AM fungi-associated ecosystems, the relation between Nim and PNU displayed a weaker negative correlation (r =-0.06, p < 0.001), whereas there was a stronger positive correlation (r = 0.25, p < 0.001) in EM fungi-associated ecosystems. Those results indicated that the AM-associated plants display a weak interaction with soil microorganisms for N absorption, while EM-associated plants cooperate with soil microorganisms. Further, we found that the Nim: PNU ratio for both AM- and EM-associated ecosystems gradually converge at a stable value (14.7 vs. 13.5, p > 0.05) with greater N availability. Our study thus highlights that plant-microbial interaction for N absorption both equalize and stabilize at increased N supply, and both these mechanisms primarily depend on the mycorrhizal association of plants in terrestrial ecosystems.

12 páginas.- 7 figuras.- 1 tabla.- referencias.- Supplementary Material Supplementary material is available at Journal of Plant Ecology online, Tropical forests are among the most productive and vulnerable ecosystems in the planet. Several global forestation programs are aiming to plant millions of trees in tropical regions in the future decade. Mycorrhizal associations are known to largely influence forest soil carbon (C) stocks. However, to date, little is known on whether and how different tree mycorrhizal types affect soil respiration (Rs) and C stocks in tropical forests. In this study, we used a three-decade tropical common garden experiment, with three arbuscular mycorrhizal (AM) and three ectomycorrhizal (EM) monocultures, to investigate the impacts of tree mycorrhizal type on Rs and soil C stocks. Associating biotic (e.g. root biomass, litter dynamic, soil microbes) and abiotic factors (e.g. microclimate) were also measured. Our results showed that AM stands supported significantly higher Rs and soil C stock, litter turnover rate and fine root biomass than EM stands. Further statistical analysis displayed that tree mycorrhizal type was the most important factor in regulating Rs and soil C stock compared with other biotic or abiotic factors. Moreover, we found that mycorrhizal type directly and indirectly affected Rs and soil C stocks via fine root biomass and litter dynamic, i.e. litter production, litter standing crop and litter turnover rate. Our findings highlight important effects of tree mycorrhizal type on forest C cycle, suggesting that planting AM tree species could contribute to promotion of soil C stock in tropical ecosystems., This research was financially supported by the National Natural Science Foundation of China (31930072, 31770559, 31600387, 31370489) and the Postdoctoral Innovation Talents Program of China (BX20200133). M.D-B. is supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I)

Elevated atmospheric CO

Climate change has increased the frequency and intensity of droughts, with potential impacts on carbon (C) release from soil (i.e. soil respiration, Rs). Although numerous studies have investigated drought-induced changes in Rs, how roots and the soil microbial community regulate responses of Rs to drought remains unclear. We conducted a 4-year field experiment (2014–2017) with three treatments (i.e. 70% rainfall reduction, control and ambient) in a subtropical forest to examine effects of drought on Rs and its components [i.e. autotrophic (Ra) and heterotrophic respiration (Rh)] and explore the mechanisms underlying these effects. Drought significantly decreased Rs by 17% averaged over the 4 years, but it had no significant effect in the first experimental year. The decrease in Rs was mediated by soil fungi and fine root biomass. Fine root biomass was correlated negatively with Ra and Rs under drought, but positively in the control treatment. Furthermore, drought treatments increased physiological stress in the bacterial community. Structural equation model (SEM) analysis suggested that under drought conditions, microclimate affected Rs via its impact on fine root biomass and fungal biomass. Our results highlight the complex interactions between microclimate, roots and soil microbes in regulating Rs under drought in subtropical forest ecosystems. Incorporating these interactions into land surface models may improve predictions of climate change impacts on forest ecosystems. A free Plain Language Summary can be found within the Supporting Information of this article.

Plants and microbes are the primary drivers in affecting the formation and accrual of soil organic carbon (SOC) for natural ecosystems. However, experimental evidence elucidating their underlying mechanisms for SOC accumulation remains elusive. Here, we quantified plant and microbial contributions to SOC accrual in successional subtropical forests by measuring leaf-, root-, and microbial biomarkers, root and leaf litter inputs, and microbial C decomposition. The long-term monitoring results showed that SOC accumulated rapidly at the early-successional stage, but changed little at the mid- and late-successional stages. SOC accrual rate was positively correlated with fine-root production and microbial C turnover, but negatively with annual litterfall. Biomarker data exhibited that the rapid SOC accumulation was jointly driven by root- and microbe-derived C inputs from the early- to mid-successional stages. In contrast, aboveground litterfall considerably contributed to soil C accrual from the mid- to late-successional stages compared to belowground processes, although SOC accumulation is low. Our study revealed the importance of root production and microbial anabolism in SOC accrual at the early stages of forest succession. Incorporating these effects of belowground C inputs on SOC formation and accumulation into earth system models might improve model performance and projection of long-term soil C dynamics.

Biomass allocation in plants is fundamental for understanding and predicting terrestrial carbon storage. Recent studies suggest that climate warming can differentially affect root and shoot biomass, and subsequently alter root: shoot ratio. However, warming effects on root: shoot ratio and their underlying drivers at a global scale remain unclear. Using a global synthesis of >300 studies, we here show that warming significantly increases biomass allocation to roots (by 13.1%), and two factors drive this response: mean annual precipitation of the site, and the type of mycorrhizal fungi associated with a plant. Warming-induced allocation to roots is greater in relatively drier habitats compared to shoots (by 15.1%), but lower in wetter sites (by 4.9%), especially for plants associated with arbuscular mycorrhizal fungi compared to ectomycorrhizal fungi. Root-biomass responses to warming predominantly determine the biomass allocation in terrestrial plants suggesting that warming can reinforce the importance of belowground resource uptake. Our study highlights that the wetness or dryness of a site and plants’ mycorrhizal associations strongly regulate terrestrial carbon cycle by altering biomass allocation strategies in a warmer world.

Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.

Global land models are now routinely incorporating the nitrogen (N) cycle into simulations, but the identification of its benchmarks has lagged behind. An important variable in these models is the soil inorganic N (SIN) which is the resultant of different input and output N processes. However, whether and how the SIN pool and its spatiotemporal variation can be used as benchmarks for models remains unclear. Here we first constructed a database of measured SIN at 756 sites from 1980 to 2010 across China, one of the regions that has been experiencing the highest external N input. Although there was great spatial variability of the measured SIN pool, no significant changes were detected across China during 1980–2010 based on a bootstrapping approach. The medians of the measured SIN across China were 63, 70, and 65 mg/kg during the 1980s, 1990s, and 2000s, respectively. Then, we used the regional SIN database to evaluate two versions of the Community Land Model (i.e., CLM4.5 and CLM5.0). In comparison with the observation (median 75 mg/kg) at grid‐cell scale, both CLM4.5 (median 0.70 mg/kg) and CLM5.0 (median 0.79 mg/kg) underestimated the SIN pools across China. Although the drivers of such modeling biases are difficult to identify at the current stage, improved representations of both input and output processes of the SIN pool in the models are highly recommended. These findings suggest that a decadal stabilization of the SIN pool in terrestrial ecosystems and the spatial distribution of the SIN pool may be a useful benchmark for global biogeochemical models.

Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin‐up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin‐up.

Soil respiration, the major pathway for ecosystem carbon (C) loss, has the potential to enter a positive feedback loop with the atmospheric CO

Accurately quantifying ecosystem respiration (ER) is crucial for understanding the feedback between the terrestrial carbon (C) cycle and climate change. However, the current estimation of ER by partitioning net ecosystem exchange (NEE) meets a great challenge due to the response difference of daytime and nighttime respiration to temperature and the effects of air humidity. In this study, we modified daytime flux partitioning methods to estimate ER by integrating the different daytime and nighttime temperature sensitivities of ER (E0, day and E0, night, referred to as DT-E0) and then relative humidity (DT-RH) based on eddy covariance and isotopic data at three sites. Our results showed that diurnal and seasonal patterns of daytime ER derived from DT-E0 and DT-RH agreed well with isotopic data, but were considerably different from previous widely used methods (e.g., nighttime (NT) and daytime (DT) methods). The daily mean ER with DT-E0 was 6%∼18% less than those with NT and DT, suggesting that the difference between E0, night and E0, day greatly affects ER estimates. By adding RH, the difference in mean ER estimated by DT-RH and DT-E0 was largest in the dry period at CN-Qia, suggesting that the change in RH at dawn and dusk may significantly influence ER and E0 in the water-limited season. In addition, the magnitude of inhibition by light in the daytime ER from DT was less than that from DT-RH, especially in forest ecosystems, indicating the previous underestimation of light inhibition for ER. Our study highlights the crucial importance of diurnal variations in E0 and RH in accurately estimating ER, which may need to be incorporated into regional and global models to improve the assessment of global C budgets.

Nitrogen (N) and phosphorus (P) have been demonstrated to limit terrestrial carbon (C) storage in terrestrial ecosystems. However, the reliable indicator to infer N and P limitation are still lacking, especially in subtropical forests. Here we used a terrestrial ecosystem (TECO) model framework in combination with a Bayesian approach to evaluate effects of nutrient limitation from added N/P processes and data sets on C storage capacities in two subtropical forests (Tiantong and Qianyanzhou [QYZ]). Three of the six simulation experiments were developed with assimilating data (TECO C model with C data [C-C], TECO C-N coupling model with C and N data [CN-CN], and TECO C-N-P model with C, N, and P data [CNP-CNP]), and the other three ones were simulated without assimilating data (C-only, CN-only, and CNP-only). We found that P dominantly constrained C storage capacities in Tiantong (42%) whereas N limitation decreased C storage projections in QYZ (44%). Our analysis indicated that the stoichiometry of wood biomass and soil microbe (e.g., N:P ratio) were more sensitive indicators of N or P limitation than that of other pools. Furthermore, effects of P-induced limitation were mainly on root biomass by additional P data and on both metabolic litter and soil organic carbon (SOC) by added P processes. N-induced effects were mainly from added N data that limited plant non-photosynthetic tissues (e.g., woody biomass and litter). The different effects of N and P modules on C storage projections reflected the diverse nutrient acquisition strategies associated with stand ages and plant species under nutrient stressed environment. These findings suggest that the interaction between plants and microorganisms regulate effects of nutrient availability on ecosystem C storage, and stoichiometric flexibility of N and P in plant and soil C pools could improve the representation of N and P limitation in terrestrial ecosystem models.

As a climate change mitigation strategy, biochar application to soil has been demonstrated to increase soil carbon (C) sequestration and reduce greenhouse gas (GHG) emission. Although numerous manipulative studies have been conducted, it is still not fully understood how biochar application affects soil respiration ( Rs ) and its components (i.e., autotrophic [ Ra ] and heterotrophic respiration [ Rh ]) in forest ecosystems, especially in subtropical forests. In this study, we performed a meta-analysis of forest ecosystems and a field experiment with biochar amendments of 0, 10, and 30 t ha −1 in a subtropical forest in Zhejiang, China to examine the effects of biochar application on Rs and its components. Our results showed that biochar application significantly increased Rs by 20.92% at the global scale with an increase of 20.25% in temperate forests and a nonsignificant effect in subtropical forests. Responses of Rs to biochar application varied with experimental methods and soil textures. Similarly, our field experiment showed that biochar amendment did not significantly affect Ra, Rh , and Rs in a subtropical forest in Eastern China. Specifically, the average Rs under biochar amendments of 0, 10, and 30 t ha −1 were 2.37, 2.06 and 2.15 μmol m −2 s −1 , respectively ( P > 0.05). Both Rs and Rh were positively correlated with microbial biomass C (MBC) and negatively with dissolved organic C (DOC). Both apparent temperature sensitivity ( Q 10 ) of Rh and Rs were significantly higher under biochar treatments than in the control. Our findings indicate the importance of the differential effects of biochar application on Rs in different forest types for C sequestration, which may inform ecosystem and regional models to improve prediction of biochar effects on forest C dynamics and climate-biosphere feedbacks.

Soil microbial carbon-use efficiency (CUE), which is defined as the ratio of growth over C uptake, is commonly assumed as a constant or estimated by a temperature-dependent function in current microbial-explicit soil carbon (C) models. The temperature-dependent function (i.e., CUE = CUE0 + m × (T − 20)) simulates the dynamic CUE based on the specific CUE at a given reference temperature (i.e., CUE0) and a temperature response coefficient (i.e., m). Here, based on 780 observations from 98 sites, we showed a divergent spatial distribution of the soil microbial CUE (0.5 ± 0.25; mean ± SD) at the global scale. Then, the key parameters CUE0 and m in the above equation were estimated as 0.475 and −0.016, respectively, based on the observations with the Markov chain Monte Carlo technique. We also found a strong dependence of microbial CUE on the type of C substrate. The multiple regression analysis showed that glucose influences the variation of measured CUE associated with the environmental factors. Overall, this study confirms the global divergence of soil microbial CUE and calls for the incorporation of C substrate beside temperature in estimating the microbial CUE in different biomes.

Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO-C-C (carbon (C)-only model), TECO-CN-C (TECO-CN coupled model with only C measurements as assimilating data), and TECO-CN-CN (TECO-CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long-term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long-term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process-induced disequilibrium in the initial value of C pools. However, in the long-term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C-N coupling in ecosystem models and highlight critical observational needs for future studies.

Terrestrial ecosystem models have been extensively used in global change research. When a model calibrated with site-specific parameters is applied to another site, how and why the parameters have to be adjusted again in order to fit data well are pervasive yet underexplored issues. In this exploratory study, we examined how and why model parameters of a Flux-Based Ecosystem Model (FBEM) varied across different sites. Parameters were estimated from data at 12 eddy-covariance towers in the conterminous USA using the conditional inversion method. Results showed that optimized values of these parameters varied across sites. For example, the estimated coefficients in the Leuning model, g l and D 0 , exhibited high cross-site variation, but the ratio of internal to air CO2 concentration ( f C i ) and canopy light extinction coefficient ( k n ) varied little among these sites. Parameters greatly varied with ecosystem types at adjacent sites where climate conditions were similar. Five parameters (activation energy of carboxylation, E K c ; activation energy of oxygenation, E V m ; ecosystem respiration, R e c o 0 ; temperature sensitivity of respiration, Q 10 ; and stomatal conductance coefficient, D 0 ) were highly correlated with mean annual temperature and precipitation across sites, which were distributed in different climate regions of conterminous US. Our results indicate that individual parameters vary to different degrees across sites and parameter variation can be related to different biological factors (e.g., ecosystem types) and environmental conditions (e.g., temperature and precipitation). It is essential to further examine magnitudes of and mechanisms underlying the parameter variation in ecosystem models so as to improve model prediction.

Biochar application to soils may increase carbon (C) sequestration due to the inputs of recalcitrant organic C. However, the effects of biochar application on the soil greenhouse gases (GHGs) fluxes appear variable among many case studies; therefore the efficacy of biochar as a carbon sequestration agent for climate change mitigation remains uncertain. We performed a meta-analysis of 91 published papers with 552 paired comparisons to obtain a central tendency of three main GHG fluxes (i.e., CO2, CH4, and N2O) in response to biochar application. Our results showed that biochar application significantly increased soil CO2 fluxes by 22.14%, but decreased N2O fluxes by 30.92% and did not affect CH4 fluxes. As a consequence, biochar application may significantly contribute to increased global warming potential (GWP) of total soil GHG fluxes due to the large stimulation of CO2 fluxes. However, soil CO2 fluxes were suppressed when biochar was added to fertilized soils, indicating that biochar application is unlikely to stimulate CO2 fluxes in the agriculture sector, in which N fertilizer inputs are common. Responses of soil GHG fluxes mainly varied with biochar feedstock source and soil texture, and the pyrolysis temperature of biochar. Soil and biochar pH, biochar applied rate and latitude also influence soil GHG fluxes, but to a more limited extent.Our findings provide a scientific basis for developing more rational strategies towards widespread adoption of biochar as a soil amendment for climate change mitigation;

Global climate models predict that future precipitation regimes will largely change across the globe due to the intensification of the global water cycle under climate warming, which may generate considerable impacts on ecosystem carbon (C) dynamics. Although substantial manipulative experiments have been conducted to probe the responses of ecosystem C processes to altered precipitation, how soil C storage responds to both drought and irrigation is still unclear across biomes and the globe. A meta-analysis of 179 published studies was carried out to examine responses of soil C storage and associated C fluxes and pools to drought and irrigation. Our results showed that, on average across all biomes, drought and irrigation similarly induced minor increases in soil C pool (SCP) by 1.45% and 1.27%, respectively. However, drought and irrigation oppositely affected both C fluxes and plant C pools as well as in agroecosystems (e.g., croplands and grasslands). The drought-induced increases in root: shoot ratio and decreases in heterotrophic respiration and soil C turnover rate mostly contributed to minor increase in SCP, while an increase in newly fixed C inputs in soil was more important under irrigation. In addition, the relative changes in precipitation intensity in manipulative experiments were positively correlated with response ratios of plant C pool (PCP), net primary production (NPP), microbial biomass C, ecosystem, soil and heterotrophic respiration. The drought-induced responses of SCP exhibited a positive correlation with experimental duration but not under irrigation and for other C pools and fluxes. These results indicate that more attention should be paid to the responses of C allocation and turnover rate to drought and irrigation, which should be incorporated into land surface models to better project effects of altered precipitation on ecosystem C cycling in terrestrial ecosystems.

Subtropical forests are considerable carbon (C) sinks in Asia, yet are facing the threat of drought with increased frequency and prolonged duration. Drought may directly and indirectly impact soil C cycling, potentially affecting the fate of the soil organic carbon (SOC) storage. In a subtropical evergreen broad-leaved forest of eastern China, five years of rainfall reduction experiment resulted in an average of 52.6% decrease in soil water content. In this study, the responses of SOC composition, soil aggregate stability, microbial extracellular enzymatic activities, fungal and bacterial community structures under long-term drought were assessed. Our results showed that drought resulted in loss of a third of large macroaggregates, and doubled the proportion of microaggregates. The non-hydrolyzed carbon (NHC) content decreased by over 50% in large macroaggregates, leading to increased sensitivity of SOC to decomposition. Compared with fungi, bacteria were more sensitive to drought. The majority of the affected taxa showed reduced abundances, while that of Actinobacteria, a group commonly associated with recalcitrant C degradation, significantly increased. Drought also increased the overall peroxidase activity typically involved in recalcitrant C turnover, although it reduced hydrolytic enzyme activities in macroaggregates. These findings revealed that drought not only decreased SOC stability through macroaggregate disintegration and changing its chemical characteristics, but also shifted microbial communities in both composition and activities toward enhanced abilities of recalcitrant C conversion. This study highlights the importance of understanding microbially-mediated C turnover processes to better predict the fate of SOC storage in response to long-term drought.

The interaction between terrestrial carbon (C) and nitrogen (N) cycles has been incorporated into more and more land surface models. However, the scheme of C-N coupling differs greatly among models, and how these diverse representations of C-N interactions will affect C-cycle modeling remains unclear. In this study, we explored how the simulated ecosystem C storage capacity in the terrestrial ecosystem (TECO) model varies with three different commonly-used schemes of C-N coupling. The three schemes (SM1, SM2, and SM3) have been used in three different coupled C-N models (i.e., TECO-CN 2.0, CLM 4.5, and O-CN, respectively). They differ mainly in the stoichiometry of C and N in vegetation and soils, plant N uptake strategies, pathways of N import, and the competition between plants and microbes for soil mineral N. We incorporated them into the C-only version of TECO model, and evaluated their impacts on the C cycle with a traceability framework. Our results showed that all of the three C-N schemes resulted in significant reductions in steady-state C storage capacity compared with the C-only version, but the magnitude varied with −23 %, −30 % and −54 % for SM1, SM2, SM3, respectively. The reduced C storage capacity is the combination of decreases in net primary productivity (NPP) by −29 %, −15 % and −45 % with changes of mean C residence time (MRT) by 9 %, −17 % and −17 % for SM1, SM2, and SM3, respectively. The divergent NPP are mainly attributed to the different assumptions on plant N uptake, plant tissue C:N ratio, down-regulation photosynthesis, and biological N fixation. In comparison, the alternative representations of the plant and microbe competition strategy and the plant N uptake, combining with the flexible C:N ratio in vegetation and soils, led to a notable spread MRT. These results highlight that the diverse assumptions on N process representation among different C-N coupled models could cause additional uncertainty to land surface models. Understanding their difference can help us to improve the capability of models to predict future biogeochemical cycles on land.

© 2017 John Wiley & Sons Ltd The terrestrial carbon (C) cycle has been commonly represented by a series of C balance equations to track C influxes into and effluxes out of individual pools in earth system models (ESMs). This representation matches our understanding of C cycle processes well but makes it difficult to track model behaviors. It is also computationally expensive, limiting the ability to conduct comprehensive parametric sensitivity analyses. To overcome these challenges, we have developed a matrix approach, which reorganizes the C balance equations in the original ESM into one matrix equation without changing any modeled C cycle processes and mechanisms. We applied the matrix approach to the Community Land Model (CLM4.5) with vertically-resolved biogeochemistry. The matrix equation exactly reproduces litter and soil organic carbon (SOC) dynamics of the standard CLM4.5 across different spatial-temporal scales. The matrix approach enables effective diagnosis of system properties such as C residence time and attribution of global change impacts to relevant processes. We illustrated, for example, the impacts of CO2fertilization on litter and SOC dynamics can be easily decomposed into the relative contributions from C input, allocation of external C into different C pools, nitrogen regulation, altered soil environmental conditions, and vertical mixing along the soil profile. In addition, the matrix tool can accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data assimilation, and facilitate tracking and benchmarking of model behaviors. Overall, the matrix approach can make a broad range of future modeling activities more efficient and effective.