Modeling Global Soil Carbon and Soil Microbial ... - Wiley Online Library

Modeling global soil carbon and soil microbial carbon by integrating microbial processes into the ecosystem process model TRIPLEX-GHG Kefeng Wang1, Changhui Peng2,1* , Qiuan Zhu1 Kerou, Zhang1 , Gangsheng Wang3 1

, Xiaolu Zhou2, Meng Wang1

Center for Ecological Forecasting and Global Change, College of Forestry,

Northwest A&F University, Yangling, Shaanxi, 712100, China 2

Department of Biology Sciences, Institute of Environment Sciences, University of

Quebec at Montreal, C.P. 8888, Succ. Centre-Ville, Montreal H3C 3P8, Canada 3

Environmental Sciences Division & Climate Change Science Institute, Oak Ridge

National Laboratory, Oak Ridge, Tennessee, 37831-6301, USA

*

Corresponding author: [email protected]

Key Points: 

Traditional soil carbon models are lacking in their representation of key microbial processes that control the soil carbon response to global climate change.



A novel ecosystem process model (termed TRIPLEX-MICROBE) offers considerable improvement over a previous version (TRIPLEX-GHG) in simulating soil organic carbon.



Our work is the first step towards a new generation of ecosystem process models that integrate key microbial processes into soil carbon cycles.

Revised Manuscript submitted to Journal of Advances in Modeling Earth Systems May 10, 2017 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/2017MS000920 This article is protected by copyright. All rights reserved.

,

Abstract Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process-based model TRIPLEX-GHG

was

developed

by

coupling

it

with

the

new

MEND

(Microbial-ENzyme-mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX-MICROBE) shows considerable improvement over the previous version (TRIPLEX-GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well-regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. [2014]. We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC) and mineral-associated organic carbon (MOC). However, our work represents the first step towards a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles.

Keywords: Climate change; Global terrestrial ecosystems; Microbial decomposition; Soil organic carbon; Terrestrial ecosystem model

1. Introduction Soil organic carbon (SOC) is the largest carbon pool in the terrestrial biosphere [Davidson and Janssens, 2006; Schmidt et al., 2011] and plays an important role in the global carbon cycle [Ciais et al., 2013; IPCC, 2013]. Permafrost regions store

This article is protected by copyright. All rights reserved.

large amounts of SOC, and if the permafrost thaws, parts of this carbon could be released as greenhouse gases [Allison and Treseder, 2008]. SOC losses due to global warming may contribute to positive climate change feedback by increasing the atmospheric CO2 concentration through enhanced microbial decomposition of SOC, particularly over decadal and centennial timescales [Crowther et al., 2016; Hararuk et al., 2015; Bradford et al., 2016; Todd-Brown et al., 2013]. In a warming environment, constraints on microbial activity in permafrost regions such as frozen water may be relaxed, causing huge stocks of SOC to become vulnerable to mineralization [Bradford, 2013; Davidson et al., 2012; Tucker, 2014]. The interaction between climate and carbon stored in permafrost soil is one of the least understood and potentially most significant modes of carbon-climate feedback because of the size of the carbon pools and the intensity of climate forcing at high latitudes [Crowther et al., 2016; Hugelius et al., 2013a; Hugelius et al., 2013b]. Soil respiration in terrestrial ecosystems plays a critical role in regulating global carbon cycling [Zhou et al., 2009]. The largest source of CO2 emissions derives from the microbial decomposition of soil organic matter (SOM). Approximately 60 - 110 Pg carbon dioxide is released to the atmosphere by microbial decomposition of SOM each year [Ågren, 2010; Bond-Lamberty and Thomson, 2010]. Ecosystem processes are largely driven by microorganisms and their metabolic activities [Treseder et al., 2011]. The seasonal dynamics of microbial biomass along the latitudinal gradient is also important for assessing the temporal dynamics of MBC. Earth system models (ESMs) are essential for studying the global carbon cycle [Lawrence et al., 2009]. Because future climate projections depend on the carbon cycle, ESMs must be capable of accurately representing the pools and fluxes of carbon in the biosphere, particularly in soils that store a large fraction of terrestrial organic carbon. However, most evaluation studies conducted so far indicate that global land models poorly represent global SOC [Hararuk et al., 2014], leading to enormous uncertainty in model simulations [Todd-Brown et al., 2012]. A recent analysis of SOC output simulated by 11 ESMs from the fifth Coupled Model Intercomparison Project (CMIP5) [Taylor et al., 2012] indicates that the uncertainty in


the simulated SOC, despite the similarities in model structures, varies six-fold among the 11 models, with the estimates ranging from 510 to 3,040 Pg C [Todd-Brown et al., 2013]. Only 6 out of 11 model estimates were within the range of 890-1,660 Pg C (with a mean value of 1,260 Pg C), estimated based on the Harmonized World Soil Database (HWSD). None of the models’ spatial correlation coefficients between the modeled and empirical SOC estimates exceeded 0.4 [Luo et al., 2015; Todd-Brown et al., 2013]. Because current ESMs assume that decomposition is a first-order decay process proportional to the size of the soil carbon pool, they do not directly represent microbial control over decomposition [Xenakis and Williams, 2014; Sihi et al., 2016]. Therefore, incorporating microbial processes into current ESMs is of high priority to improve the estimation of global SOC and reduce the uncertainty of ESM simulations [Luo et al., 2015]. A single SOC pool or traditional fast/slow/passive pools are often used in SOC decomposition models. Traditional fast/slow/passive pools based on decay rates are empirical and difficult to relate to measurements [Schmidt et al., 2011; Wang et al., 2013]. Recent advances in the theory of microbial decomposition could provide a foundation for major changes in the structure of soil carbon models used in ESMs [Todd-Brown et al., 2013]. Microbial physiology is critical to the earth system [Schimel, 2013; Xu et al., 2013]. ESMs must be capable of simulating microbial physiology to more accurately project climate change feedback modes [Wieder et al., 2013]. The explicit incorporation of microbial dynamics into C cycle models can potentially improve model predictive accuracy when assessed against global SOC databases [Hararuk et al., 2015]. Recently, a handful of microbial models have been developed to simulate warming effects on SOM decomposition by incorporating Michaelis–Menten kinetics or other microbial processes in soil decomposition models [Allison et al., 2010; Allison, 2014; German et al., 2012; Sihi et al., 2016; Sulman et al., 2014; Wang et al., 2013; Wieder et al., 2014]. These models are similar in their basic structure and key biogeochemical processes but differ in terms of model complexity and reference temperature. Li et al. [2014] compared four microbial models, CON and AWB [Allison et al., 2010], GER


[German et al., 2012], and MEND [Wang et al., 2013], and found that the more mechanistic model (e.g., MEND) showed less-pronounced oscillations and was more realistic in terms of the long-term SOC response to climate change. Thus, adding enzyme pools could improve the mechanistic representation of soil carbon dynamics during the transient phase in certain ecosystems over decadal time scales. Although such models are currently being used at the global scale [Wieder et al. 2013], there have been few efforts to compare model structures and behaviors relevant to this scaling process. Coupling these microbial models with global soil carbon models could help better simulate global soil carbon and predict the impact of future climate change on microbial carbon decomposition and feedback modes [Luo et al., 2015; Bradford et al., 2016]. The objectives of this study were to do the following: 1) develop a new microbial process-based model (namely, “TRIPLEX-MICROBE”) by incorporating Michaelis– Menten kinetics and microbial-enzyme processes into the ecosystem model TRIPLEX-GHG [Zhu et al. 2014], which includes explicit descriptions of the five major soil carbon pools, as described in Wang et al. [2013, 2015]: (a) particulate organic carbon (POC), (b) mineral-associated organic carbon (MOC), (c) the active layer of MOC, interacting with dissolved organic carbon (DOC) through adsorption and desorption, (d) DOC, and (e) microbial biomass carbon (MBC); 2) test model simulations of global SOC and soil MBC against global soil carbon observations of the HWSD and estimated global soil MBC by Xu et al. [2014]; 3) analyze the seasonal dynamics of microbial biomass simulated by TRIPLEX-MICROBE along the latitudinal gradient; and 4) conduct model sensitivity analysis for the impact of changes in climate conditions (daily temperature and precipitation) and parameters on key model outputs (e.g., DOC, Enzyme, MBC, and MOC).

2. Model structure and descriptions The TRIPLEX-GHG model [Zhu et al., 2014] was developed based on the Integrated Biosphere Simulator (IBIS), coupled with a new methane (CH4) biogeochemistry module (incorporating CH4 production, oxidation, and transportation


processes) and a water table module to investigate CH4 emission processes and dynamics that occur in natural wetlands. TRIPLEX-GHG includes six main modules, namely, the Plant Phenology, Vegetation Dynamics, Land Surface, Water Table, Methanogenesis and Soil Biogeochemistry modules. Details of the model structures and features are provided in Zhu et al. [2014] and [2015]. The microbial-enzyme-mediated decomposition (MEND) model [Wang et al., 2015; Wang et al., 2013] was constructed based on Michaelis–Menten kinetics and describes the dynamics of physically defined pools of SOC. The scope of this study was to couple the TRIPLEX-GHG model with the MEND model and then use the coupled TRIPLEX-MICROBE model to simulate global SOC and soil MBC. The SOC density and MBC density simulated by TRIPLEX-MICROBE was multiplied by the global grid area to calculate the global SOC and MBC. There is a uniform biome for every grid in the biome map. The global SOC and MBC are counted in each biome by using ArcGIS. The code of the MEND sub-model was developed in the FORTRAN language, as in the TRIPLEX-GHG, and the model was run at an hourly time-step. The basic concept and structure of the SOC cycle and its integration into the TRIPLEX-GHG are presented in Figure 1, and the details are described below. Figure 1. 2.1 The MEND module and major parameters The MEND model, developed by Wang et al. [2013], contains six C pools: (1) POC (represented by the variable P in model equations); (2) MOC (M); (3) the active MOC layer (Q) interacting with DOC through adsorption and desorption; (4) DOC (D); (5) MBC (B); and (6) extracellular enzymes (EP and EM). The component fluxes are DOC uptake by microbes (denoted by the flux F1), POC decomposition (F2), MOC decomposition (F3), microbial growth respiration (F4) and maintenance respiration (F5), adsorption (F6) and desorption (F7), microbial mortality (F8), enzyme production (F9), and enzyme turnover (F10). Model equations for each component are listed in Appendix Table 1, and the transformation fluxes are described by equations 13-26 in Appendix Table 2, following Wang et al. [2015].


The main equation in MEND was based on the Michaelis–Menten equation [Wang et al., 2015; Wang et al., 2013]. This equation is frequently used to describe enzyme-catalyzed processes, as it relates to the metabolic conversion of a compound [Cossa et al., 2014]. There is an assumption in the Michaelis–Menten equation that a steady state pertains. The following equation is the Michaelis–Menten equation used in MEND, where [E] denotes the concentration of enzymes or microbes, Km is the half-saturation constant, Vmax is the maximum reaction rate at a saturating substrate concentration, [S] is the substrate concentration, and V is the reaction rate: V=

𝑉𝑚𝑎𝑥 × [𝐸] × [𝑆] 𝐾𝑚 + [𝑆]

The major parameters for the MEND module are presented in Appendix Table 3. Some of the parameter values have been fully discussed and reported in previous studies [Wang et al., 2015; Wang et al., 2014; Wang and Post, 2012; 2013; Wang et al., 2013]. 2.2 Coupling MEND with the TRIPLEX-GHG The MEND model was divided into three parts for the model integration. First, additional parameters and variables pertaining to the MEND module were defined, and the code was written into the TRIPLEX-GHG before the soil biogeochemistry loop. Next, MEND module equations were incorporated within an hourly loop. Finally, the codes for model outputs were embedded in the previously defined NetCDF format. There are four main inputs (soil temperature, soil moisture, soil pH, and litterfall) in the MEND model. There are annual, monthly, daily and hourly time-steps in the TRIPLEX-GHG, whereas MEND runs at an hourly time-step; thus, we coupled MEND with TRIPLEX-GHG at an hourly time-step. As a result, the soil temperature, soil moisture and soil pH were estimated from the Land Surface module, and the litterfall was derived from the Vegetation Dynamics module of TRIPLEX-GHG, and these were all converted into an hourly scale. In addition, the litterfall estimated annually by TRIPLEX-GHG was allocated into POC1, POC2 and DOC pools in a certain fraction [Bonan et al., 2013; Wang et al., 2015]. Because the soil in the Land Surface module is divided into 4 layers within a 1-m depth (i.e., 0-0.1


m, 0.1-0.25 m, 0.25-0.5 m and 0.5-1.0 m), and because the MEND model only has one soil layer, average values were calculated by weighting the soil depths and then importing them to the MEND model. The temperature, moisture and pH in the four soil layers are represented by temp1, temp2, temp3, and temp4; mois1, mois2, mois3, and mois4; and pH1, pH2, pH3, and pH4. Soil temperature, moisture and pH averaged over 1 m are respectively represented by soil_temp, soil_mois and soil_pH and are calculated using soil thickness as a weighting factor (Eqs. 1-3). The litterfall per hour is calculated using equation 4, which was derived from the annual litterfall (litterfall_year). soil_temp = (temp1*0.1+temp2*0.15+temp3*0.25+temp4*0.5)/ (0.1+0.15+0.25+0.5)

(1)

soil_mois = (mois1*0.1+mois2*0.15+mois3*0.25+mois4*0.5)/ (0.1+0.15+0.25+0.5)

(2)

soil_pH = (pH1*0.1+pH2*0.15+pH3*0.25+pH4*0.5)/ (0.1+0.15+0.25+0.5)

(3)

litterfall = litterfall_year/ (365*24)

(4)

3. Data 3.1 Calibration data We compiled observed SOC data based on 28 previous studies or field work from 44 sites worldwide and used these observations for model parameter fitting and calibration. These observed SOC data were divided according to vegetation types. The parameters for each vegetation type in the TRIPLEX-MICROBE were calibrated by the corresponding observed SOC. The model was run many times for each site to calculate the residuals between the simulated and observed SOC, and we selected the set of parameters minimizing the sum of squared residuals for the same vegetation type. Information related to these observed sites, including location, soil type, and land use, is summarized in Table 1. The selected sites were geographically spread over low- to mid- to high-latitude regions. Table 1. 3.2 Input data The vegetation map used in TRIPLEX-MICROBE was based on both the IBIS and the land cover classification system. The vegetation map used was generated by


overlaying GlobCover2009 (developed by the European Space Agency (ESA) from MERIS

data)

with

the

ecoregion

framework

(designed

by

WWF)

(http://due.esrin.esa.int/page_globcover.php). The vegetation type for farmland in ESA is replaced by grassland in TRIPLEX-MICROBE. For the simulations, the CRU-TS 3.1 Climate Database (http://badc.nerc.ac.uk/data/cru) was adopted to construct monthly climate input data for the observed sites and global soil. Selected variables included cloud cover, diurnal temperature range, precipitation, temperature, vapor pressure and wet-day frequency. For other input data, such as CO2 concentration, the soil classification map and the global grid area, we used the same datasets as those of Zhu et al. [2014]. 3.3 Global soil carbon and microbial carbon data The global soil carbon simulations were compared with the HWSD [FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012] and high-latitude soil carbon stocks from the Northern Circumpolar Soil Carbon Database (NCSCD) [Hugelius et al., 2013a; Hugelius et al., 2013b]. Soil carbon stocks were calculated from bulk densities and SOC concentrations given in the HWSD for the top 1 m of soil at a 0.25° × 0.25° resolution, which was regridded from a 0.5° × 0.5° resolution (Figure 3). Because high-latitude soils contain a large fraction of global soil carbon, we also validated TRIPLEX-MICROBE simulations of soil carbon at high latitudes with the NCSCD, which is an independent survey of soil carbon [Hugelius et al., 2013a; Hugelius et al., 2013b]. We used the 0.25° × 0.25°-resolution soil carbon data product for the first meter of soil (Figure 3). We also verified soil MBC in the 1 m soil and compared it with the database compiled by Xu et al. [2014], which summarizes the concentrations and stoichiometry of C, N and P in soils and soil microbial biomass at global and biome levels. We then estimated the global C and N storage of the soil microbial biomass. 3.4 Statistical analyses TRIPLEX-MICROBE simulations were compared to datasets using Pearson correlation coefficients in R version 3.3.0 [The-R-Core-Team, 2016]. SOC was


compared to observations using linear regression. The sensitivity of variation in the parameters was examined using the Taylor diagram [Taylor, 2001], which provides a visual framework for comparing model results to a reference model or, most commonly, to observations. On the diagram, the correlation coefficient and the root-mean-square (RMS) difference between the two fields, along with the ratio of the standard deviations of the two patterns, are all indicated by a single point on a two-dimensional plot [Taylor, 2001]. The correlation coefficients are used to quantify the similarity between the simulated and referenced or observation patterns. The standard deviation of the reference is normalized by itself and plotted at a unit distance from the origin along the abscissa. The normalized standard deviation (SD) and the centered RMS difference are used to measure the observation variability and simulated observation error of the model, respectively. The position of each dot appearing on the plot quantifies how closely the simulated patterns match the reference, and the centered RMS difference between every point and reference field (centered solid arc, reference) is proportional to their distance apart (in the same unit as the standard deviation).

4. Results 4.1 Model parameterization and calibration The calibration sites are categorized in Table 1. The selected parameters that were most sensitive were calibrated to determine the best combination of site-specific parameters. The parameter alpha (alpha = Vmt/(Vg + Vmt), Vg: maximum specific uptake rate of DOC for growth, Vmt: specific maintenance rate of active microbial biomass) is the most sensitive parameter in our model. The order of sensitivity for other parameters is rE (Turnover rate of EP1, EP2 and EM), Yg (True growth yield), pEP (Fraction of mR for production of EP1 and EP2), fpEM (fpEM=pEM/pEP), and VM (Maximum specific decomposition rate for MOC), which is consistent with Wang et al. [2013]. The selected parameters were adjusted to yield the best agreement for each site between the simulations and observations (Figure 2). The best parameter combination for each vegetation type is listed in Table 2, with the rest of the


parameters using the default values from MEND [Wang et al., 2015]. Table 2. The tropical evergreen forest exhibited the lowest VM (maximum specific decomposition rate for MOC) and the highest rE (turnover rate of enzymes). The polar desert exhibited the highest VM, pEP (fraction of maintenance for POC-enzyme production), Alpha (Vmt/(Vg + Vmt), where Vmt and Vg denote the specific maintenance rate and specific growth rate, respectively), fpEM (ratio of MOC-enzyme production to POC-enzyme production) and Yg (true growth yield). All of the observations used in this paper are classified according to the ESA land cover classification system. The 44 observations belong to 11 vegetation types and lack the vegetation types of tropical deciduous forest, boreal evergreen forest, savanna, and polar desert. Due to the lack of tropical deciduous forest, boreal evergreen forest, savanna and polar desert vegetation types, the corresponding parameters were fine-adjusted to the temperate evergreen broadleaf forest, boreal deciduous forest, grassland or shrubland and desert vegetation types, respectively. The SOC observations and the SOC simulated by TRIPLEX-MICROBE and TRIPLEX-GHG are compared in Figure 2. Obviously, there is a significant improvement in the TRIPLEX-MICROBE SOC simulation compared with the TRIPLEX-GHG SOC simulation. Model fitting was achieved with a higher R-squared of 0.77 and a P-value of less than 0.01. Figure 2. 4.2 Validation of global SOC and soil MBC The SOC density and soil MBC density modeled in TRIPLEX-MICROBE are shown in Figure 3. Model estimates of the average global SOC stock are approximately 1,195 Pg C compared to an estimate of 1,188 Pg C from the HWSD and 1,345 Pg C simulated by TRIPLEX-GHG. The model-simulated SOC for the high northern latitudes is approximately 348 Pg C, compared to 500 Pg C for the NCSCD and 290 Pg C for the HWSD. The global soil carbon simulated by TRIPLEX-MICROBE was compared with 11 CMIP5 ESMs [Todd-Brown et al., 2013] and observations from the HWSD, as shown in Figure 4. A parallel global


map of residuals between TRIPLEX-MICROBE and TRIPLEX-GHG was produced and is shown in Supplement Figure 1. Figure 3. Figure 4. The modeled SOC and soil MBC classified by vegetation type compared with the HWSD and those of Serna-Chavez et al. [2013] and Xu et al. [2014] are shown in Figure 5. The SOC densities modeled in eight vegetation types (tropical evergreen forest, tropical deciduous forest, temperate evergreen conifer forest, boreal evergreen/deciduous forest, savanna and grassland/cropland) are 0.17% - 4.9% higher than those in HWSD, 19.15% higher in open shrubland and 46.94% higher in polar desert. However, the densities are 1.26% - 16.25% lower in temperate evergreen broadleaf forest, temperate deciduous forest, mixed forest, dense shrubland, tundra and desert. The global MBC modeled for five vegetation types (temperate deciduous forest, boreal deciduous forest, dense shrubland, tundra and desert) are 2.8% - 64.36% lower than the MBC computed from Xu et al. [2014], whereas the others are 1.2% -57.9% higher than Xu et al. [2014]. The global MBC modeled by TRIPLEX-MICROBE is higher than that of Serna-Chavez et al. [2013] except the tundra. Figure 5. We compared the global SOC density and global soil MBC density modeled in TRIPLEX-MICROBE with HWSD and Xu et al. [2014] classified by vegetation type, as shown in Figure 6, in which the gray line indicates a 1:1 relationship and the black line shows the linear regression. The R-squared values for the regression of SOC and MBC are 0.96 and 0.39, respectively, with P-values less than 0.01. Figure 6. 4.3 Monthly soil MBC dynamics We analyzed the global soil MBC simulated by TRIPLEX-MICROBE in different latitudinal zones (Figure 7). The soil MBC reached the maximum in June between 90°N and 75°N, in July between 75°N and 60°N, in August between 60°N and 45°N, and in September


between 45°N and 30°N. The soil MBC reached the maximum in January between 30°N and 25°N, in March between 25°N and 20°N, in April between 20°N and 15°N, in May between 15°N and 10°N, and in April between 10°N and 0°N. The soil MBC reached the maximum in October between 0°S and 15°S, in November between 15°S and 20°S, and in December between 20°S and 25°S. The soil MBC reached the maximum in June between 25°S and 35°S and in April between 35°S and 60°S. We found that the latitudinal MBC densities display a very interesting phenomenon, i.e., at approximately 30°N, near the equator and 25°S, the microbial carbon quantity undergoes a reversion. At approximately 30°N and 25°S, the MBC gradually reached the maximum in the coldest month and showed an opposite seasonal pattern on both sides of the equator. We suspect that this phenomenon is caused by the seasonal pattern of the revolution of the Earth, as the sun moves back and forth between the north and south tropics. Although we do not know the specific mechanism, this finding demonstrates that the influence of temperature on microorganisms is relatively strong. Figure 7. 4.4 Sensitivity analysis We conducted a sensitivity analysis to investigate the impact of changes in environmental conditions (daily temperature and precipitation) and parameters (Table 2) on six carbon pools, including DOC, Enzyme (EP1+EP2+EM), MBC (AMB+DMB), MOC, TSOC (POC+MOC+QOC+DOC+MBC+EP1+EP2+EM), and TMGMR (microbial growth + maintenance respiration rate). We chose observations in Table 1 for each vegetation type and averaged them for the sensitivity analysis. The sensitivity of the six carbon pools was addressed by varying one factor, keeping all the others constant, and applying a 1°C increase/decrease in daily temperature and a 10% increase/decrease in precipitation relative to the baseline scenarios (Table 3). We found that the DOC, MBC and MOC in the tundra are the most sensitive to the 1°C increase or decrease in temperature. The responses of Enzyme, MBC and TMGMR to a


10% increase/decrease in precipitation were most pronounced in the desert, open shrubland and grassland, respectively. This finding verifies that colder ecosystems, such as those in the arctic, are sensitive to changes in temperature and that drier places, such as deserts, open shrublands and grasslands, are more sensitive to changes in precipitation. Table 3. A Taylor diagram provides an opportunity to test the basic behavior of the new model [Wu et al., 2014]. The sensitivity of Tsoc to the different parameter values (as listed in Table 2) was tested by halving or doubling the parameter value given in Table 2. According to the Taylor diagram (Figure 8), Tsoc is sensitive to changes in the values of six key parameters (e.g., VM, rE, pEP, fpEM, alpha, and Yg) [Taylor, 2001]. The sensitivity results indicate that the correlations between the simulated Tsoc and the references were between 0.9 and 1.0. As shown in Figure 8, there is equifinality between the doubled VM and pEP or fpEM and between the doubled alpha and Yg, suggesting that the change values of these patterns eventually led to a similar variation in soil carbon. Figure 8.

5. Discussion 5.1. Comparison with previous modeling studies Accurate modeling of the soil carbon cycle is essential for predicting carbon-climate feedback in the future because soil carbon stocks are large relative to the atmospheric CO2 reservoir and are sensitive to climate change [Todd-Brown et al., 2013]. The new TRIPLEX-MICROBE model has enhanced the simulation of global SOC relative to the previous TRIPLEX-GHG model according to our results shown in Figure 2 and Figure 5, except for two vegetation types (tropical deciduous forest and polar desert). Todd-Brown et al. compared soil carbon simulations from 11 CMIP5 ESMs to empirical data from the HWSD (Figure 4) and the NCSCD. The global SOC modeled by our new model TRIPLEX-MICROBE fell within the range simulated by the 11 CMIP5 ESMs. Most model estimates of global soil carbon stocks have ranged


from 510 to 3,040 Pg C and 60 to 820 Pg C for the high northern latitudes [Todd-Brown et al., 2013]. Based on our TRIPLEX-MICROBE modeling results, the global soil carbon stocks were estimated to be approximately 1,195 Pg C and, for the high northern latitudes, approximately 348 Pg C. The global soil MBC modeled by TRIPLEX-MICROBE is approximately 21 Pg C, which is between the values of 14.6 Pg C estimated by Serna-Chavez et al. [2013] and 23.2 Pg C estimated by Xu et al. [2014]. The global MBC pools modeled by TRIPLEX-MICROBE for five vegetation types (temperate deciduous forest, boreal evergreen/deciduous forest, dense shrubland, tundra and desert) (Figure 5) are lower than those estimated from Xu et al. [2014], whereas the others are higher. The global MBC simulated by TRIPLEX-MICROBE is higher than estimates of Serna-Chavez et al. [2013] except the tundra. We attributed the monthly variations in MBC to the north of 30°N primarily to temperature. In these areas, microbes become more active and SOM decomposition accelerates as temperature increases. Cold temperatures are the primary climatic factor limiting decomposition at high latitudes [Hobbie et al., 2000]. Under warming conditions, liquid water becomes available, and the microbial decomposition of SOC initially proceeds slowly because of cool temperatures. Continued warming accelerates the decomposition of SOC by relieving the temperature limitation on microbial activities and the catalytic activities of intracellular and extracellular microbial enzymes [Bradford et al., 2016]. Between 30°N and 25°N, and to the south of 25°S, the MBC reaches the maximum in the coldest season of the year. While microorganisms decompose more SOC with increasing temperature, at relatively lower temperature, microbial carbon use efficiency (CUE, the fraction of assimilated carbon that is allocated to growth) gradually increases, indicating that microorganisms may convert more C to biomass. Between 25°N and 0°N, the MBC reached the maximum from March to June, whereas between 0°S and 25°S, the MBC reached the maximum from October to December. Allison et al. [2010] concluded that the SOC response to climate warming depends on the efficiency of soil microbes in using carbon, and with increasing


temperature, there might be a decline in CUE. 5.2. Sensitivity analysis of TRIPLEX-MICROBE The results from TRIPLEX-MICROBE showed that a 1°C increase in temperature resulted in a variation of -5.48 to 11.23% in Tsoc. The temperature increase had a positive effect on SOC for five vegetation types (temperate evergreen broadleaf forest, temperate evergreen conifer forest, temperate deciduous forest, mixed forest, and open shrubland) but had a negative effect on others. Most ecosystem models predict that climate warming will stimulate the microbial decomposition of soil carbon. Allison et al. [2010] concluded that the soil-carbon response to climate warming depends on carbon use efficiency. Microorganisms have acted as gatekeepers for terrestrial carbon fluxes, either causing its release to the atmosphere through their decomposition activities or preventing its release by stabilizing the carbon in a form that cannot be easily decomposed [Liang and Balser, 2012]. Therefore, we cannot assume that there must be a positive feedback between the terrestrial ecosystem and global climate warming. Although the overall parameter sensitivity of the MEND model was tested in Wang et al. [2013], in this study, we only selected six key parameters, including KM, rE, pEP, fpEM, alpha, and Yg, to conduct a series of sensitivity analyses of the model simulations. As expected, alpha is the most sensitive parameter, which is consistent with Wang et al. [2013, 2015]. The parameter values analyzed in this study were spatially heterogeneous. For the parameter fitting process, we attempted to find the best site-specific parameter combination and show how well the model could perform at each site. This issue will continue to be an important one for model simulation and development at both regional and global scales. Collecting more observed SOC datasets from different geographic locations and vegetation types could reduce uncertainties and produce more-reliable parameter sets under different conditions and locations [Zhu et al., 2014]. One possible reason for the differences between the simulations and observations could be the uncertainties in forcing data. Uncertainties from field observations should also be taken into account in such comparisons. To reduce the uncertainty of


model prediction, it is necessary to combine process-based ecological models with experimental data continually (model-data fusion) [Peng et al., 2011]. Further modeling, observational and data collecting works should be performed to reduce uncertainties and enable more-accurate simulations. Some of the predicted SOC contents were lower than observations, possibly because we simulated the average 1 m SOC contents, whereas the depths of the measured values were often less than 1 m and the upper SOC contents were generally higher than the SOC in the subsoil. 5.3. Model limitations and improvements There are still some limitations to the MICROBE module in this study. The MICROBE module is currently under development, and it does not consider vertically resolved soil layers. As is known, soil nutrients, microbial biomass, soil temperature and moisture are not identical among different soil layers. The litterfall estimated by TRIPLEX-MICROBE does not capture the seasonal trend as well as Eq. 4 does (especially in leaf litter in deciduous forest), which may hamper the seasonal pattern of pools. Cotrufo et al. [2013] had discussed the role of plant litter quality, and there was a high formation rate for SOM in the high-quality litter according high microbial substrate use efficiency. There is an obvious hysteresis in the soil temperature and moisture as the depth increases. Cardona et al. [2013] found that deep water would rise to the upper soil with plant roots. This would affect the activity of microorganisms in the surface layer under the condition of drought and have a significant impact on soil organic matter decomposition, where they showed that hydraulic lift enhances surface soil nitrogen cycling. In addition, a carbon-nutrient coupled MICROBE module (i.e., MEND) is still under development and has not been officially released yet; thus, the C-only version was integrated into the current TRIPLEX-MICROBE. We suggest that the carbon-nutrient coupled MICROBE

could

improve

the

SOC

simulations in

those

areas

under

nutrient-limiting conditions in the future.

6. Summary and conclusions We successfully integrated the MEND module into the process-based ecosystem


model of TRIPLEX-GHG to investigate the influences of environmental factors such as soil temperature, soil moisture and pH controlling microbial decomposition. The SOC decomposition processes were specified and incorporated into the new TRIPLEX-MICROBE model. This new model augments global SOC simulations relative to the previous model, TRIPLEX-GHG. We estimated the average global SOC stocks at 1 m depth as 1,195 Pg C (approximately 348 Pg C for the high northern latitudes) and the estimated global soil MBC as 21 Pg C. We found that the MBC in the latitudinal direction displayed reversions at approximately 30°N, near the equator and 25°S. The results also suggested that the new coupled TRIPLEX-MICROBE model performed better than the 11 CMIP5 ESMs and was successful in simulating global soil carbon. However, improvements, such as adding the nutrient cycle and multi-soil layers, could be critical and beneficial for model validation and simulation with regard to the interaction of the C, N and P cycles and their feedback modes under future global climate warming.

Acknowledgements. This study was partly funded by the National Basic Research Program of China (2013CB956602), the China National Science Foundation (41571081), the Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant and the China QianRen Program. G. Wang is supported by the U.S. Department of Energy, Office of Biological and Environmental Research, including the ORNL Terrestrial Ecosystem Science Scientific Focus Area (TES-SFA) and the BGC Feedbacks project. The FORTRAN code for the TRIPLEX-MICROBE is

uploaded

in

the

open

source

Bitbucket

(https://[email protected]/kefeng_wang/triplex-microbe.git). We express our thanks to senior editor John M. from NPG Language Editing for critical comments on an earlier version of the manuscript and two anonymous reviewers for their valuable comments and constructive suggestions which greatly improved the paper.

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Figure 1. Basic structural concept and integration of the microbial module into TRIPLEX-GHG in TRIPLEX-MICROBE. POC1: particulate organic carbon (lignocellulose-like compounds); POC2: particulate organic carbon (starch-like compounds); MOC: mineral-associated organic carbon; QOC: adsorbed phase of DOC; DOC: dissolved organic carbon; AMB: active microbial biomass; DMB: dormant microbial biomass; EP1: POC1-degraded enzymes; EP2: POC2-degraded enzymes; EM: MOC-degraded enzymes.


Figure 2. Comparison of the soil organic carbon (SOC) modeled by TRIPLEX-MICROBE (this study) and TRIPLEX-GHG (Zhu et al. 2014) with observed SOC across different sites. The gray line indicates a 1:1 relationship, and the black line shows the linear regression.


Figure 3. Comparison of the modeled results with the estimated SOC density and soil MBC density of 1 m soil. Graph a shows the high latitude soil carbon density from the NCSCD; graph b shows the global soil carbon density from the HWSD; graph c shows the global soil carbon density simulated by TRIPLEX-MICROBE; graph d shows the soil microbial carbon density from Xu et al. [2014] and the data downloaded from https://www.ornl.gov/; graph e shows the simulated soil microbial carbon density by TRIPLEX-MICROBE.


Figure 4. Global 1 m soil carbon simulated by TRIPLEX-MICROBE and other ESMs and compared with the HWSD observations.


Figure 5. Comparison of the modeled and estimated SOC and soil MBC in 1 m soil classified by vegetation type for the global soil. HWSD: the HWSD SOC; TRIPLEX-GHG denotes the simulated SOC by TRIPLEX-GHG; TRIPLEX-MICROBE denotes the simulated SOC by TRIPLEX-MICROBE; Serna_MBC denotes the soil MBC data from Serna-Chavez et al. [2013]; Xu_MBC denotes the soil MBC data from Xu et al. [2014] and the data downloaded from https://www.ornl.gov/; TRIPLEX-MICROBE_MBC denotes the simulated soil MBC by TRIPLEX-MICROBE.


Figure 6. Comparison of modeled and observed SOC and soil MBC density for the global soil. Graph a shows the TRIPLEX-MICROBE simulated SOC density compared with the HWSD SOC density, and graph b shows the TRIPLEX-MICROBE simulated soil MBC density compared with the Xu et al. [2014] soil MBC density data. The gray line indicates a 1:1 relationship, and the black line refers to the linear regression.


Figure 7. Monthly variations of soil microbial carbon simulated by TRIPLEX-MICROBE on latitude scales. N90_N75 denotes north latitude from 90° to 75°, and the other latitudes are expressed in a similar manner.


Figure 8. Taylor diagram assessing the sensitivity of Tsoc in soils to variations in the parameters. Halving and doubling values of VM are represented by 0.5*VM and 2.0*VM, respectively, and the other five parameters are expressed in a similar manner. The point 2.0*VM appears under the point 2.0*pEP and 2.0*fpEM, and the point 2.0*alpha appears under the point 2.0*Yg.


Table 1. Information of collected sites for model parameter fitting. ID

Site

Lat

Long

Soil order

Ecosystem / Land use type

Reference

35°01'N ~35°10'N

126°36'E~126°49'E

Inceptisols & Ultisols

crop

Lim et al., 2010

Casas Blancas, Central Mexico

19°25'N

101°36'W

Andisols

corn

Salinas-Garcia, et al., 2001

4

Hofuf, Saudi Arabia

25°18'N

49°33'E

Aridisols

crop

Schenkeveld et al., 2006

5

Guyuan, Hebei, China

41°46'N

115°41'E

Aridisols

grasslands

Liu et al., 2010

6

Segzi Plain, Iran

31°50'N

51° 56'E

Aridisols

barren

Mojiri et al., 2011

7

Duvannyi Yar, Northern Siberia, Sakha, Russia

68°37'N

159°02'E

Gelisols

larch forest

Dutta et al., 2006

8

Fairbanks, Alaska

64°51'N

147°43'W

Gelisols

black spruce forest

Grunzweig, et al., 2004

9

Kerasia, Thessaloniki, Greece

40°38'N

22°23'E

Alfisols

maize seedlings

Panayiotopoulos et al., 1994

10

Teide, Tenerife, Canary Islands, Spain

28°17'N

16°39'W~16°46'W

Entisols

stony barren

Ribes and Grimalt, 2002

18

Hofuf, Saudi Arabia

25°16'N

49°43'E

Entisols

crop

Bashour et al., 1983

20

ARC farm, Gassim, Saudi Arabia

26°00'N

43°42'E

Entisols

crop


21

Sailkabir, Taif, Saudi Arabia

21°26'N

40°30'E

Entisols

crop


22

Al-Ashqar, Wadi Dawasir, Saudi Arabia

20°30'N

45°12'E

Aridisols & Entisols

crop


24

interior Alaska

63°20'N~64°30'N

148°12'W~143°30'W

Gelisols & Inceptisols

black spruce

Kane et al., 2006

27

Stuttgart, SW Germany

48°46'N

9°11'E

Alfisols

winter wheat

Friedel and Scheller, 2002

28

Kaltenborn, Thuringia, Germany

50°47'N

10°13'E

Entisols

grassland

Don and Schulze, 2008

29

Saskatchewan, Canada

53°57'N

106°06'W

Alfisols

spruce-fir

Donald et al., 1993

30

Pyrenees, Spain

pinus

Arbestain et al., 2002

31

Tirap district, Arunachal Pradesh

evergreen forest

Barbhuiya et al., 2004

35

spruce forest with hebs and lichens

Oosterwoud et al., 2010

1

South Korea

3

42°26'N

1°39'E

Spodosols

27°07'N~27°13'N

95°21'E~95°26'E

Ultisols

Taiga site, Syktyvkar, Komi Republic

62°00'N

50°0'E

Inceptisols

36

Luquillo National Forest, Puerto Rico

18°20'N

65°49'W

Entisols

tropical forests

Zou et al., 2005

37

Northern Siberia, Sakha, Russia

68°48'N

161°23'E

Gelisols

larch forest

Dutta et al., 2006

38

Shillong, Meghalaya, India

25°34'N

91°56'E

Oxisols

cut-tree stand

Arunachalam et al., 1996

39

Houlong, Miaoli, NW Taiwan

24°39'N

120°47'E

Entisols

lowland Casuarina

Chen, Chiu, Tian ., 2005

40

Droevendaal, Netherlands

51°59'N

5°39'E

Spodosols

crop

Schenkeveld et al., 2006

41 42

Auburn, central Alabama Cedar Creek, MN

32°00'N 45°00'N

85°00'W 93°0'W

Ultisols Entisols

cotton/soybean/rye White pine forest

Insam et al., 1991 Sinsabaugh et al., 2008

43

New Delhi, India

28°40'N

76°48'E

Inceptisols

rice, puddled

Banerjee et al., 2006

44

Zigui Co., TGRA, China

30°39'N~30°42'N

110°08'E~110°16'E

Alfisols

Orchard

Iqbal et al., 2010


Table 2. List of calibrated parameters for each vegetation type. VM1 Vegetation Type

rE2(×10-3)

pEP3(×10-3)

fpEM4

(mgCmg-1Ch-1)

Alpha5

Yg6

(-)7

Tropical evergreen forest

7

0.152

0.106

0.905

0.242

0.231

Tropical deciduous forest

8

0.112

0.106

0.905

0.422

0.211

Temperate evergreen broadleaf forest

6

0.112

0.106

0.905

0.402

0.231

Temperate evergreen conifer forest

8

0.142

0.106

0.905

0.402

0.211

Temperate deciduous forest

8

0.112

0.106

0.905

0.362

0.241

Boreal evergreen/deciduous forest

10

0.112

0.126

1.095

0.202

0.451

Mixed forest

8

0.112

0.106

1.055

0.222

0.271

Savanna

12

0.112

0.146

1.095

0.452

0.251

Grassland

9

0.112

0.116

1.055

0.392

0.261

Dense shrubland

8

0.112

0.116

1.055

0.382

0.231

Open shrubland

13

0.112

0.156

1.155

0.452

0.431

Tundra

14

0.102

0.176

1.255

0.372

0.461

Desert

14

0.102

0.176

1.255

0.332

0.461

Polar desert/rock

16

0.102

0.256

1.655

0.472

0.491

1

Maximum specific decomposition rate for MOC. 4

2

Turnover rate of EP1,EP2 and EM.

3

Denoted the production rate of

5

EP(EP1+EP2). fpEM= PEM/ PEP. =Vmt/(Vg+Vmt),Vmt is specific maintenance factor or rate, Vg is maximum specific uptake rate of D for growth. 6 True growth yield. 7 Denoted there was no dimension.


Table 3. Results of sensitivity of key carbon pools to changes in climatic variables for different vegetation types Vegetation types

Carbon pools

+1°

-1°

DOC

-3.80

3.29

Enzyme

(%) Tropical evergreen forest

Temperate evergreen broadleaf forest

Temperate evergreen conifer forest

Temperate deciduous forest

Boreal deciduous forest

Mixed forest

Dense shrubland

Precipitation +10%

-10%

0.24

-0.93

-1.47

1.17

0.47

-1.43

MBC

4.40

-4.39

-2.10

2.92

MOC

-3.27

2.81

-0.01

-0.07

Tsoc

-1.66

1.35

-0.34

0.49

TMGMR

-5.50

4.86

0.74

-1.90

DOC

-5.05

3.01

1.81

-3.46

Enzyme

-1.09

0.49

1.03

-1.78

MBC

16.56

-12.02

-13.36

17.60

MOC

-3.37

1.83

0.90

-1.51

Tsoc

11.23

-8.31

-9.54

12.47

TMGMR

-4.27

2.53

0.98

-2.81

DOC

0.31

-1.92

1.31

-1.32

Enzyme

-0.37

-0.21

0.53

-1.35

MBC

12.75

-8.28

-9.22

10.54

MOC

1.45

-2.51

0.81

0.14

Tsoc

10.69

-7.26

-7.19

8.67

TMGMR

0.38

-2.00

0.91

-1.82

DOC

-3.47

1.74

0.58

-3.28

Enzyme

-0.54

-0.25

0.32

-0.92

MBC

10.62

-7.48

-5.33

9.81

MOC

-2.33

1.31

0.08

-2.50

Tsoc

5.28

-3.54

-2.98

4.72

TMGMR

-3.91

1.70

0.87

-3.43

DOC

-3.59

2.25

-2.11

11.03

Enzyme

1.16

-12.61

-3.87

-4.94

MBC

6.04

-17.53

-5.39

-1.74

MOC

-4.24

5.33

-6.07

9.13

Tsoc

-3.59

4.52

-6.04

8.88

TMGMR

0.53

-12.20

-4.89

-0.38

DOC

-0.22

7.88

3.57

-4.05

1.77

3.75

6.77

-4.24

MBC

7.59

-2.73

1.05

0.31

MOC

-0.58

4.46

-0.06

0.24

Tsoc

1.58

0.96

-1.13

2.28

TMGMR

1.89

7.28

7.32

-4.26

DOC

-8.05

8.14

4.30

-1.53

Enzyme

Enzyme

Grassland

Temperature

-7.49

10.18

11.14

-12.00

MBC

0.01

3.34

4.65

-5.27

MOC

-3.55

4.71

1.02

2.57

Tsoc

-4.66

4.85

1.69

-0.58

TMGMR

-8.26

10.98

11.50

-9.67

DOC

-2.69

8.24

3.87

-2.82

Enzyme

-5.28

7.42

6.18

-8.55

MBC

1.76

-0.04

1.67

-3.47

MOC

-1.86

2.45

0.05

0.32


Open shrubland

Tundra

Tsoc

-5.48

TMGMR

-6.70

DOC

3.26

1.37

-3.86

9.98

6.32

-8.29

-1.67

-9.10

8.86

Enzyme

-4.64

4.11

17.91

-18.84

MBC

-2.90

1.60

12.39

-11.62

MOC

3.88

-4.48

-14.87

6.21

Tsoc

1.96

-2.54

-6.92

1.39

TMGMR

-2.34

1.74

8.63

-11.69

DOC

-14.40

17.13

8.13

-15.16

5.51

-9.49

0.71

-6.34

MBC

22.90

-26.65

-5.82

18.45

MOC

-8.10

8.75

2.27

2.83

Tsoc

-5.26

5.49

1.69

3.34

TMGMR

-0.03

-5.22

1.69

-5.07

Enzyme

Desert

3.80

DOC

0.20

-0.57

-4.76

2.78

-10.50

12.21

30.14

-24.29

MBC

-9.50

10.88

27.10

-22.04

MOC

-3.53

3.50

4.50

-6.43

Tsoc

-3.61

3.57

4.21

-6.32

-10.70

12.02

24.01

-22.12

Enzyme

TMGMR

Values given represent percent of change compared to the baseline scenario.
