Biogeosciences, 16, 663–680, 2019https://doi.org/10.5194/bg-16-663-2019© Author(s) 2019. This work is distributed underthe Creative Commons Attribution 4.0 License.

Modeling anaerobic soil organic carbon decompositionin Arctic polygon tundra: insights into soil geochemicalinfluences on carbon mineralizationJianqiu Zheng1, Peter E. Thornton2,3, Scott L. Painter2,3, Baohua Gu2, Stan D. Wullschleger2,3, andDavid E. Graham1,3

1Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37931, USA2Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37931, USA3Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37931, USA

Correspondence: David E. Graham (grahamde@ornl.gov)

Received: 1 February 2018 – Discussion started: 20 February 2018Revised: 6 January 2019 – Accepted: 9 January 2019 – Published: 4 February 2019

Abstract. Rapid warming of Arctic ecosystems exposes soilorganic matter (SOM) to accelerated microbial decomposi-tion, potentially leading to increased emissions of carbondioxide (CO2) and methane (CH4) that have a positive feed-back on global warming. Current estimates of the magnitudeand form of carbon emissions from Earth system models in-clude significant uncertainties, partially due to the oversim-plified representation of geochemical constraints on micro-bial decomposition. Here, we coupled modeling principlesdeveloped in different disciplines, including a thermodynam-ically based microbial growth model for methanogenesis andiron reduction, a pool-based model to represent upstream car-bon transformations, and a humic ion-binding model for dy-namic pH simulation to build a more versatile carbon de-composition model framework that can be applied to soilsunder varying redox conditions. This new model frameworkwas parameterized and validated using synthesized anaero-bic incubation data from permafrost-affected soils along agradient of fine-scale thermal and hydrological variabilitiesacross Arctic polygonal tundra. The model accurately simu-lated anaerobic CO2 production and its temperature sensitiv-ity using data on labile carbon pools and fermentation ratesas model constraints. CH4 production is strongly influencedby water content, pH, methanogen biomass, and presence ofcompeting electron acceptors, resulting in high variability inits temperature sensitivity. This work provides new insightsinto the interactions of SOM pools, temperature increase, soilgeochemical feedbacks, and resulting CO2 and CH4 produc-tion. The proposed anaerobic carbon decomposition frame-

work presented here builds a mechanistic link between soilgeochemistry and carbon mineralization, making it applica-ble over a wide range of soils under different environmentalsettings.

Copyright statement. This paper has been authored by UT-Battelle,LLC under contract no. DE-AC05-00OR22725 with the US Depart-ment of Energy. The United States Government retains and the pub-lisher, by accepting the article for publication, acknowledges thatthe United States Government retains a non-exclusive, paid-up, ir-revocable, worldwide license to publish or reproduce the publishedform of this paper, or allow others to do so, for United States Gov-ernment purposes. The Department of Energy will provide publicaccess to these results of federally sponsored research in accordancewith the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

1 Introduction

The northern permafrost region contains 1400–1800 Pg soilcarbon (C), which is more than twice as much C as is cur-rently contained in the atmosphere (Tarnocai et al., 2009;McGuire et al., 2012). Persistent cold and saturated soilconditions have limited C decomposition in this reservoir.However, rapid warming and permafrost thaw expose pre-viously frozen organic carbon to accelerated microbial de-composition, potentially leading to emissions of carbon diox-

Published by Copernicus Publications on behalf of the European Geosciences Union.

664 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

ide (CO2) and methane (CH4) that have a positive feed-back on global warming (Zimov et al., 2006; Schuur et al.,2009, 2015). How quickly frozen soil organic matter (SOM)will be mineralized, and how much permafrost C will be re-leased to the atmosphere following thaw, is highly uncertain.Earth system models project 27–508 Pg carbon release fromthe permafrost zone by 2100 under current climate forcing(Zhuang et al., 2006; Koven et al., 2015; MacDougall et al.,2012; Schaefer et al., 2014). Understanding environmentaldependencies of SOM decomposition is therefore essentialfor reducing model uncertainties and improving predictionsof future climate change.

Disagreement in model projections for the northern per-mafrost region could be due to differences in model structure,model initialization, or parameters used in simulations. De-spite increasingly detailed process representations in manymodels that simulate terrestrial CO2 and CH4 fluxes, im-portant geochemical and metabolic constraints might stillbe poorly represented, oversimplified, or missing in currentbiogeochemical models (Xu et al., 2016). The northern per-mafrost region is rapidly changing in response to the chang-ing climate. Rising temperatures not only release more labilecarbon from permafrost for decomposition but also createthermal and hydrological heterogeneity that further affectsbiogeochemical processes. Here, we examine two mecha-nisms that substantially affect SOM turnover in permafrost-affected soils. First, rising temperature alters the kineticsof biogeochemical reactions (Segers, 1998). This effect ismore pronounced at subzero temperature (Bore et al., 2017),and the process rate increase is higher at lower temperatureranges (Davidson and Janssens, 2006). Microbial commu-nities also change with temperature, compounding effectson process rates (Karhu et al., 2014). Models address thistemperature effect using empirical functions and parame-ters (Tuomi et al., 2008; Xu et al., 2016), which might behighly biased depending on model assumptions and originalcurve-fitting techniques, generating large uncertainties. Sec-ond, heterogeneity in permafrost thaw and related hydrologi-cal responses creates geochemical gradients in soils. Modelsuse different levels of detail to simulate effects of water sat-uration (Meng et al., 2012; Xu et al., 2016). Soil moisturelimits gas transport, and it is often used as an implicit con-trol on heterotrophic respiration and methanogenesis. How-ever, the explicit processes resulting from soil oxygen deple-tion (e.g., soil redox status and pH dynamics) are not widelyrepresented (Riley et al., 2011; Meng et al., 2012; Xu et al.,2015).

The extent of SOM decomposition and gas emissions de-pends upon soil geochemical characteristics beyond temper-ature and O2 availability. Among the wide range of envi-ronmental variables, pH emerges as a primary control ondecomposition by regulating both microbial communitiesand microbial metabolic activities (Zhalnina et al., 2015;Bethke et al., 2011; Jin and Kirk, 2018). pH affects microbialmetabolism by modulating the thermodynamics and kinetics

of redox reactions. Redox reactions produce or consume pro-tons, and thus their free energy yields vary with pH (Bethkeet al., 2011; Jin and Bethke, 2007). The Gibbs free energyavailable to anaerobic microorganisms that degrade simpleorganic molecules generally increases (becomes less favor-able) with increasing pH (Bethke et al., 2011). Notably, iron[Fe(III)] reduction is highly proton consuming and becomesless favorable at higher pH (Fig. S1 in the Supplement). Pre-vious studies identified iron reduction as a major process inanoxic Arctic soils (Lipson et al., 2010, 2013), which in-creases local pH and might favor co-occurring methanogen-esis (Tang et al., 2016; Wagner et al., 2017). However, theinfluence of iron reduction on methanogenesis rates in dif-ferent soils is rarely investigated. The reactivity of iron andits pH feedback impose additional complexity on the controlsof SOM decomposition and associated CH4 production.

Despite the importance of pH in controlling redox reac-tions and resulting C emissions, pH change is not explicitlyrepresented in biogeochemical models. Most of the currentbiogeochemical models apply a single initial pH value forredox reactions without considering proton production andconsumption during the processes. Traditional decomposi-tion models use landscape position, soil moisture content, orother proxies for O2 concentration to determine the form ofC release. Scalars on aerobic respiration (Riley et al., 2011;Lawrence et al., 2015) or empirical ratios of CO2 and CH4(Koven et al., 2015) are often used to inform the extent of Cdecomposition and partitioning of CO2 and CH4 production.Reactions that produce or consume protons and the resultingpH changes or ion exchange reactions are not considered inthese empirical models. Some process-rich models explicitlyinclude details of methanogen populations and their inter-actions with substrates and other environmental factors, butthese models still lack the capability to simulate pH changesduring long-term carbon decomposition. Instead, constantpH is often assumed within bell-shaped pH response func-tions (Meng et al., 2012; Tian et al., 2010; Xu et al., 2015).Without underlying proton exchange and pH buffering mech-anisms, a significant error may occur when rate calculationsdepend heavily upon the initial choice of a single optimal pHvalue for various reactions.

In this study, we developed a new anaerobic carbondecomposition model framework with explicit representa-tion of aqueous-phase geochemistry to allow pH and ther-modynamic calculations. By coupling three different mod-els, including a thermodynamically based microbial growthmodel, a substrate pool-based model, and a humic ion-binding model, we built a process-rich carbon decomposi-tion model that allows simultaneous thermodynamic and pHcalculations. Results from anoxic incubations of permafrost-affected soils along a hydrological gradient were synthesizedto parameterize and validate this new model framework. Themain objectives of this study were to (i) examine the role ofsoil geochemical variables in controlling anaerobic carbondecomposition and carbon release (as both CO2 and CH4);

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Figure 1. Conceptual diagram showing key processes in the anaer-obic carbon decomposition framework, beginning with plant mate-rial and coarse wood debris (CWD). The numbers indicate differentprocesses. 1. SOM degradation from soil organic carbon pools withincreasing turnover time produces dissolved organic carbon (DOC)and CO2. 2. Fermentation of DOC into organic acids, H2, and CO2.3. Methanogenesis from organic acids or H2. 4. Fe(III) reductionfrom organic acids or H2. 5. Fe(OH)3 dissolution.

(ii) develop a common set of parameters in the new anaer-obic carbon decomposition framework to capture variabili-ties in CO2 and CH4 production; and (iii) evaluate model un-certainties in responses to both soil heterogeneity and modelparameterization, emphasizing effects of soil saturation, pH,and temperature response.

2 Materials and methods

2.1 Anaerobic carbon decomposition model

The anaerobic carbon decomposition framework was devel-oped with explicit representation of fermentation, methano-genesis, and iron reduction, which were identified as keymechanisms for anaerobic CO2 and CH4 production (RoyChowdhury et al., 2015; Yang et al., 2016; Zheng et al.,2018b). The main structure of this framework included twomajor components: a simplified Community Land Modelwith prognostic carbon and nitrogen (CLM-CN) decomposi-tion cascade (converging trophic cascade, or CTC) (Thorntonand Rosenbloom, 2005) to facilitate parameterization of theupstream carbon flow entering the aqueous-phase dissolvedorganic carbon (DOC) pool (Fig. 1, process 1), and an aque-ous phase to facilitate calculations of thermodynamics andredox-reaction-associated acid–base chemistry. An empiri-cal approach was used to represent non-aqueous-phase soilorganic carbon (SOC) decomposition. Additionally, mecha-nistic representations of methanogenesis and iron reductionwere developed in this work based on aqueous-phase ther-modynamic calculations.

The simplified CTC cascade included four SOM poolsto represent bulk SOC with different levels of complexity.Changes of these SOM pools followed modified first-orderdecay (see the Supplement for details). We modified the orig-inal respiration fraction (Thornton and Rosenbloom, 2005;Koven et al., 2013) into direct and indirect fractions. Thus,for each SOM pool, the direct respiration fraction repre-

sented CO2 lost as originally defined, while the indirect res-piration fraction was labile C produced from bulk C enteringthe aqueous-phase carbon pool (DOC pool, Fig. 1).

The large biomolecules in the DOC pool went throughmultiple hydrolysis and fermentation steps to produce low-molecular-weight organic acids that would further respireinto CO2 and CH4 (Boye et al., 2017; Zheng and Graham,2018; Yang et al., 2016; Roy Chowdhury et al., 2015). Underanoxic conditions, hydrolysis of polysaccharides was con-sidered the rate-limiting step for downstream methanogene-sis (Glissmann and Conrad, 2002). Polysaccharide hydroly-sis has a favorable free energy, due to increased entropy, butcannot be readily coupled to biological energy transductionoutside of the cell. We previously measured a rapid decreasein reducing sugar concentrations in pore water during tundrasoil incubations, which indicated that hydrolysis limits de-composition (Yang et al., 2016). At low temperatures (below15 ◦C), the microbial degradation of cellulose was consid-erably diminished, while other polymers, such as starch orproteins, were degraded much faster at low temperature, re-sulting in the accumulation of organic acids, primarily acetic,propionic, and butyric acids (Kotsyurbenko, 2005; Yang etal., 2016). These low-molecular-weight organic acids furtherfueled microbial mineralization reactions that led to produc-tion of CH4 and CO2. Given that most anaerobic lignocel-lulose degraders also fermented sugars following hydrolysis(Blumer-Schuette et al., 2014), we assumed the turnover ofDOC into low-molecular-weight organic acids was a singlelumped fermentation process (Fig. 1, process 2), in whichlabile DOC (C6H12O6) was fermented into acetate, H2, andCO2 (Appendix A, Reaction AR1, and Table S1 in the Sup-plement). This assumption gave a fixed stoichiometry ratio:one-third of the fermented carbon was oxidized to CO2.

Redox reactions including methanogenesis and iron reduc-tion were represented using a thermodynamically based ap-proach (Istok et al., 2010), with unique microbial growth ki-netics incorporated into energy-yielding redox reactions. Inthis thermodynamically based approach, the growth equa-tions of methanogens and iron reducers were derived frompaired electron donor (acetate or H2) and electron accep-tor half reactions and a biomass synthesis equation (Is-tok et al., 2010). Using a constant molecular formula asbiomass (C5H7O2N) and ammonium (NH+4 ) as the nitro-gen source for biosynthesis, we derived the growth equationsfor methanogenesis and iron reduction (Appendix A, Reac-tions AR2–AR5). Rate calculations followed the generalizedMonod rate law (Jin and Bethke, 2007) with an additionalthermodynamic factor representing the thermodynamic driv-ing force. The thermodynamic factor f (G) is calculated us-ing Eq. (1) (Jin and Bethke, 2003):

f (G)= 1− exp(−−1G−m1Gp

χRT

), (1)

where 1G (kJ (mol reaction)−1) is the free energy changeof the redox reaction. 1G depends on the standard Gibbs

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666 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

free energy change (1G0) and the concentrations of chemi-cal species involved in the reaction.1Gp is the phosphoryla-tion potential, i.e., the energy required to synthesize adeno-sine triphosphate (ATP) from adenosine diphosphate (ADP)and dihydrogen phosphate in the cell’s cytoplasm. 1Gp isabout 45 kJ (mol ATP)−1 (Jin and Kirk, 2018).m is the num-ber of ATP molecules synthesized per redox reaction. χ isthe average stoichiometric number (Jin and Kirk, 2018), R isthe gas constant (kJ mol−1 K−1), and T is the absolute tem-perate (K). The factor f (G) ranges from 0 to 1, where thereaction is thermodynamically favorable when f (G)>0.

Both methanogenesis and iron reduction contribute topH change. Reactions such as ferrihydrite reduction sub-stantially increase alkalinity (Appendix A, Reactions AR4,AR6). Furthermore, the solubility of CO2 and the composi-tion of dissolved CO2 and bicarbonate vary significantly overtypical soil pH values, affecting all C mineralization pro-cesses. In the organic-rich soils modeled here, SOM ratherthan minerals provides most buffering capacity. Therefore,we used the humic ion-binding model to describe pH buffer-ing during carbon decomposition. A simplified parameteri-zation of proton binding is available in the Windermere Hu-mic Aqueous Model (WHAM; Tipping, 1994, 1998), whichhas been extensively calibrated to represent the acid–basechemistry of “average” humic and fulvic acids, and bench-marked with heterogeneous natural organic matter (Atalay etal., 2009). We adopted the WHAM parameterization to rep-resent proton-binding characteristics (pH buffering) providedby SOM (Tang et al., 2016). Using representative bindingconstants provided by WHAM, the pH buffering capacity canbe directly adjusted by altering the number of proton-bindingsites, which is assumed to be linearly correlated with the totalamount of SOM (see the Supplement for details).

2.2 Model implementation and initialization

The above model structure was implemented using theopen-source geochemical program PHREEQC 3.0 (Charl-ton and Parkhurst, 2011) with a new database describingSOC decomposition cascade, redox reaction kinetics, and pHbuffering (redox.dat, available at https://github.com/jianqiuz/decomposition, last access: January 2019). This model as-sumed thermodynamic equilibrium of aqueous chemicalspeciation, mineral dissolution/precipitation, and ion sorp-tion/desorption based on the updated PHREEQC thermody-namic database (phreeqc.dat; Charlton and Parkhurst, 2011).The database was modified to include WHAM pH bufferingand reaction kinetics for SOM pools decay and reaction ki-netics for fermentation, methanogenesis, and iron reduction.The kinetic rate constants and microbial biomass growth anddecay rates were adopted from former thermodynamicallybased studies (Istok et al., 2010) and previously tested withlow-center polygon Arctic soils (Tang et al., 2016).

The model initialization was based on both the incubationconditions and soil geochemical characterizations (Fig. S2).

The initial partitioning of SOM pools was assumed to beat fixed ratios due to the limitation of short-term incuba-tion data. Under the experimental conditions, we assumedSOM1 and SOM2 pools with relatively shorter turnover rates(τ = 14 days and 70 days, respectively) were most relevant inthe model. On the other hand, SOM3 and SOM4 pools wererelatively inert (τ>2 years for both pools). We started withthe relative fractions of SOM pools at approximately 10 %,40 %, 10 %, and 40 % of SOC in organic and mineral soilsfor SOM1–4. We further assessed the bias of this assumptionwith sensitivity analysis. Sizes of SOM1 and SOM2 poolswere reduced by 90 % for permafrost to better account forthe overall low levels of carbon degradation.

All other variables required were initialized using mea-surements based upon 10 to 15 g of wet soil incubated in60 to 70 mL sealed bottles. Total SOC, total water (TOTW),total organic acid carbon (TOAC), pH, and the initial con-centration of Fe(II) were specified in the model based onmeasurements (Table S2). The DOC pool in the model wasinitialized using the measured water-extractable organic car-bon (WEOC) expressed as a fraction of SOC (fdoc). Onaverage, WEOC accounts for approximately 2 % of SOCbased on our synthesized data (see Sect. 3.1). This value isconsistent with previous long-term incubations, which sug-gested less than 5 % of SOC was fast-decomposing carbon inpermafrost-affected soils at a standardized temperature of 4–5 ◦C (Knoblauch et al., 2013; Schädel et al., 2014). The start-ing biomass of methanogens and iron reducers was assumedto be within the range of 10−3 to 10−5 g C g SOC−1 for or-ganic soils, 10−4 to 10−6 g C g SOC−1 for mineral soils, and10−7 to 10−9 g C g SOC−1 for permafrost (Table S2). Thisstratified microbial biomass distribution was used to repre-sent the vertical gradient in the relative abundance of micro-bial communities (Yang et al., 2017).

The lumped fermentation process was the rate-limitingstep in the model and was fitted individually with data fromeach soil microcosm. Based upon reaction stoichiometry, thefermentative conversion of each mole of labile C led to two-thirds of mole of organic acids and one-third of a mole ofCO2. Organic acids were mineralized via methanogenesis oriron reduction to convert approximately 49 % to 88 % of Cin organic acids into CO2. This estimation was based on re-action stoichiometry of Reactions (AR2) and (AR4), and afraction of the C was incorporated into microbial biomass.Therefore, the percentage of respired C would be less than100 % even if all organic acids were respired as CO2. If weassume all fermentation products were mineralized into CH4and CO2, we could estimate the fermentation rate (Rfer) frommeasured CO2 production. Thus, Rfer was estimated usingthe initial CO2 production rate in the incubation data,and fur-ther optimized using the least squares method by fitting withobserved CO2 production values (Table S3).

Temperature and pH response functions were used to fur-ther constrain model simulations (Fig. S2). A temperatureeffect was parameterized using the CLM-CN temperature re-

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sponse function (Appendix B, Eq. B1). Additional tempera-ture response functions were evaluated by sensitivity analysis(see Sect. 2.4). The effect of pH on biological reaction ratesis modulated by bell-shaped pH response functions (Tanget al., 2016; Xu et al., 2016). Here, we used the DynamicLand Ecosystem Model (DLEM) pH response function (Ap-pendix B, Eq. B5), since it generated the least variation inparameter perturbation tests (Tang et al., 2016).

2.3 Incubation data synthesis for model validation

Incubation data from Utqiagvik (Barrow) Alaska soil coresthat represent the microtopographic heterogeneity of polyg-onal tundra were synthesized to validate the new anaerobiccarbon decomposition model. The selected datasets representfine-scale variabilities in thermal and hydrological regimesacross the gradient of soil microtopographic positions (Hern-don et al., 2015). The synthesized data contain completesets of soil geochemical descriptions for organic, mineral,transition zone (if identified), and permafrost layers fromeach microtopographic feature (see the Supplement for de-tails). Levels of total soil organic carbon, WEOC, and TOACwere available before and after soil incubation. Besides CH4and CO2 production during low-temperature soil decomposi-tion, data on Fe(II) concentrations and pH changes were alsoavailable for model initialization and validation.

2.4 Model parameter uncertainty

This model was designed as a generic framework to simu-late anaerobic carbon decomposition across a range of soilphysiochemical conditions. Two types of sensitivity analysiswere conducted to evaluate model performance. First, pos-sible bias and variations associated with model initializationvariables (soil geochemical attributes) were assessed usingperturbation simulations. Variations of ±25 % and ±50 %(+100 % and 200 % for some variables) were applied to thesevariables, and the resulting changes in cumulative CO2 andCH4 production were evaluated by comparison with refer-ence simulations. This evaluation helps to identify criticalmeasurements needed for initializing the model. Second, pa-rameters specifically benchmarked in this study and parame-ters adopted from empirical relationships were also evaluatedwith perturbation simulations. This test helps to apportion themodel prediction uncertainties into different sources, includ-ing model input, parameters, or model structure.

3 Results

3.1 Meta-analysis to validate model assumptions

Incubation data used in this study were generated from soilsrepresenting different microtopographic features with a widerange of moisture and SOC contents and reported elsewhere(Roy Chowdhury et al., 2015; Zheng et al., 2018b). Correla-

tion analysis revealed a close relationship between soil mois-ture and organic carbon pools (measured as SOC, WEOC,and TOAC) among examined soil microtopographic featuresand across soil depth (p<0.01, Table S4). All these soil prop-erties significantly correlated with cumulative CO2 and CH4production (p<0.05), suggesting the important role of initialsoil geochemical properties in controlling carbon degrada-tion.

Although various levels of carbon mineralization weremeasured as CO2 and CH4 production during incubations,changes in WEOC and TOAC were consistent among treat-ments with distinct patterns. WEOC represents 0.3 % to2.6 % of total SOC among all test soils, and this ra-tio remained constant before and after anoxic incubations(Fig. S3). On the other hand, TOAC showed much more dy-namic changes among different soils and different incuba-tion temperatures. TOAC generally increased in soils fromthe organic layer, transition zone, and permafrost. In con-trast, TOAC drastically decreased by up to 90 % in min-eral soils. These results indicate that WEOC was in a steadystate among examined soils, while TOAC varied substan-tially due to microbial mineralization processes, supportingthe model assumption of lumped fermentation (the conver-sion of WEOC to TOAC) as the rate-limiting step.

Both CO2 and CH4 production rates responded stronglyto rising incubation temperature (p = 0.02 and p = 0.04, re-spectively; Fig. S4, Tables S5 and S6). The estimated Q10values of CO2 production showed a relatively narrow range,while methanogenesis had much larger variation in estimatedQ10 values ranging from 1.6 to 48.1. Using Q10 valuesto simulate the temperature dependence of processes mightwork for CO2 production but could generate significant er-rors in predicting CH4 production.

3.2 Modeled CO2 and CH4 production using observedparameters

The model performed well in simulating CO2 and CH4 dy-namics across a range of moisture and SOC gradients andamong different soil types (Figs. S5 and S6). Variations ingas production among different conditions, including mi-crotopographic features, soil layers, and different incubationtemperatures, were well captured (Fig. S7). The comparisonsbetween modeled and observed CO2 and CH4 production areshown in Fig. 2. The model slightly underestimates CO2 pro-duction towards the end of the incubations but still maintainsa good agreement between modeled and observed CO2 pro-duction (R2

= 0.89). The underestimation of CO2 produc-tion is likely due to substrate limitations caused by the ini-tial distribution of different carbon pools. Model-predictedCH4 production also showed good agreement with observa-tions (R2

= 0.79). More variation between modeled and ob-served CH4 production suggests a systematic pattern in themodel parameterization of methanogenesis: the model under-

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668 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

Figure 2. Comparison between modeled and observed production of CO2 (a) and CH4 (b). Averaged measurements of triplicate microcosmsat each time point from each incubation temperature were calculated as observed values.

estimates CH4 production at 4 and 8 ◦C, and overestimatesCH4 production at −2 ◦C.

To assess the model sensitivity to initial model inputs,we compared model predictions in response to varying ini-tial model inputs via perturbation simulations. First, we ex-amined the influence of the partitioning of different carbonpools. Significant changes in model predictions of CO2 andCH4 were observed in response to perturbations of initial in-put of SOC and WEOC but not TOAC (Fig. 3). SOC deter-mines the size of different carbon pools in the model, andit further influences the predictions of WEOC, TOAC, CO2,and CH4. For example, predicted CO2 and CH4 productionincreased by about 200 % when +200 % changes were ap-plied to initial SOC input. This trend is consistent with cor-relation analysis of incubation results, described above (Ta-ble S4). Perturbations in initial WEOC strongly altered thepredictions of TOAC and CO2, consistent with the model as-sumption of the conversion of WEOC to TOAC (fermenta-tion process) as the rate-limiting step. The model also pre-dicted increases in CH4 and Fe(II) accumulation in responseto lower WEOC. Lower WEOC significantly reduced organicacid accumulation and thus increased system pH and accel-erated rates of both methanogenesis and iron reduction. Thestarting level of TOAC showed minimal influence on modelpredictions of CO2 and CH4, suggesting other factors ratherthan substrate availability were limiting carbon mineraliza-tion. The initial sizes of SOM1 and SOM2 pools showed veryslight changes in model predictions of WEOC and CO2, andminimal influence on CH4 prediction, further supporting theassumption that downstream fermentation is the rate-limitingstep in the model. Additional soil geochemical factors, in-cluding soil moisture, Fe(II), and pH, also significantly in-fluence model output. In particular, initial soil pH showed adramatic effect on predicted CO2 and CH4 production. Withinitial soil pH increasing from 5 (reference simulation) to 6,

the model predicted 160 % and 308 % increases in CO2 andCH4 production, respectively. Perturbations in initial soil pHhad the strongest effect on the prediction of CH4 by assign-ing different values in fpH that were directly proportional tothe methanogenesis rates. The above results of perturbationsimulations demonstrated the high sensitivity of this modelin response to varying soil geochemical properties.

3.3 Model sensitivity to parameterization uncertainties

To further validate the model, we performed additional sensi-tivity analysis to justify model assumptions and estimate theuncertainties generated from model parameterizations. Onemajor assumption of this modeling framework is to lumpmultiple fermentation processes into one reaction stoichiom-etry, controlled by one reaction rate constant. It is critical toevaluate how this simplified structure influences model per-formance and contributes to model output uncertainties. Themodel parameter sensitivity analysis indicated the TOACpool was most sensitive to changes in the fermentation rate(Rfer) and reaction stoichiometry (Fig. 4). Downstream re-actions were less affected by the uncertainties of the twotested parameters. These results supported our assumption oflumped fermentation with fixed stoichiometry, indicating therobustness of the model structure presented here.

The selection of temperature response functions representsone of the major sources of model uncertainties. A sensi-tivity analysis was performed by comparing four differenttemperature response functions (Appendix B). In our simula-tions, the quadratic temperature response function proposedby Ratkowsky et al. predicted much higher CO2 and CH4production rates at higher temperature, and the lowest ratesof both CO2 and CH4 at temperatures below 0 ◦C, giving thehighest temperature response among tested response func-tions (Fig. 5). In contrast, the Arrhenius equation predictedmuch lower temperature response for both CO2 and CH4.

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Figure 3. Perturbations of initial soil geochemical conditions differentially affected model predictions (including CH4, CO2, Fe(II), TOAC,WEOC, and pH) during anaerobic carbon decomposition. For example, when the initial pH decreased by 8 % and 17 %, CH4 productiondecreased by 40 % and 80 %, respectively. Normalized changes in model output were calculated as the ratio of changes caused by perturbationsimulations (differences between perturbation and reference runs) to reference simulation output after 60 days of anaerobic decompositionat 8 ◦C. To test model sensitivity in response to initial pH, the reference run started with pH 6, and up to 1 pH unit changes were applied inperturbation simulations to represent a realistic pH range for soils. Reference simulations were based on soils with 30 % SOC (water contentof 2 g g−1 dwt, and pH= 5).

Empirical functions used in the CLM-CN and CENTURYmodels gave similar temperature responses for both CO2 andCH4. Variations in low-temperature CO2 production are wellconstrained by established temperature response functions,while CH4 production at −2 ◦C showed a much wider rangeof temperature response, and the median value is best simu-lated using the Ratkowsky function. This sensitivity analysis

is consistent with model output of CO2 and CH4 production,where CO2 is well constrained by the model, but CH4 is sig-nificantly overestimated at −2 ◦C using the CLM-CN tem-perature response function. A unified temperature responsefunction for all reactions under different biotic or abiotic con-straints substantially contributes to the disagreement betweenmodel output and observations.

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670 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

Figure 4. Simulated changes in model predictions (including CH4, CO2, Fe(II), TOAC, WEOC, and pH) during anaerobic carbon decompo-sition in response to perturbations of (a) fermentation rate and (b) fermentation stoichiometry (acetate : CO2 is 1 : 1 for reference simulation).Normalized changes in model output were calculated as the ratio of perturbation simulation output to reference simulation output after 60days of anaerobic decomposition at 8 ◦C. Reference simulations were based on soils with 30 % SOC (water content of 2 g g−1 dwt, andpH= 6).

Figure 5. Comparison of simulated and observed temperature responses for the production of CO2 (a) and CH4 (b). Results were allnormalized to CO2 or CH4 production rates at 8 ◦C for direct comparison. Observations at −2 and 4 ◦C were plotted in black dots andthe median values were marked in red. The shaded area represents output uncertainties generated from rate estimations within 60± 5 days.Reference simulations were based on soils with 30 % SOC (water content of 2 g g−1 dwt, and pH= 5).

Redox reactions contribute to proton production or con-sumption, and the resulting pH alters the value of the pH re-sponse function (fpH) that directly controls reaction kineticfunctions, creating a feedback loop. pH buffering capacity(BC) provided by SOM with proton-binding sites and fpHrepresent two major sources of uncertainties in this feedbackloop. Thus, we performed perturbation simulations to charac-terize the sensitivity of model output to variations in BC andfpH (Fig. 6). Higher BC stabilized system pH during pro-longed incubations, while lower BC permitted a pH increaseby up to 0.71 pH unit compared to the reference simulation.This 14 % pH increase led to a 123 % increase in fpH, accel-erating both methanogenesis and Fe(III) reduction rates sub-

stantially. Perturbations on the pH response function were di-rectly reflected in the slopes of pH response curves (Fig. S8).We found up to 372 % change in the value of fpH during a 60-day simulation, as a steeper increase in fpH accelerated bothmethanogenesis and iron reduction (Reactions AR2–AR5),which contributed to pH rise that further accelerated fpH in-crease. Correspondingly, both CH4 and Fe(II) increased bymore than 100 % after the simulation. While BC is an impor-tant factor controlling both redox reactions and pH fluctua-tions, a unified fpH for all reactions may impose significantvariations in model output.

BC is an intrinsic soil property simulated with a simpli-fied linear relationship to soil SOM. However, it generates a

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J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization 671

Figure 6. Simulated changes in model predictions (including CH4, CO2, Fe(II), TOAC, WEOC, pH, and fpH) during anaerobic carbondecomposition in response to perturbations of (a) pH buffering capacity and (b) pH response function. Normalized changes in model outputwere calculated as the ratio of perturbation simulation output to reference simulation output after 60 days of anaerobic decomposition at8 ◦C. Reference simulations were based on soils with 30 % SOC (water content of 2 g g−1 dwt, and pH= 5).

Figure 7. Temperature response of CH4 and Fe(II) production rates at varying soil pH buffering capacities (BCs). Varying BCs with respectto the reference simulation (BC= 1) creates strong feedback to rates of methanogenesis and iron reduction. Reference simulations werebased on soils with 30 % SOC (water content of 2 g g−1 dwt, and pH= 5).

strong nonlinear response in the simulations of methanogen-esis and Fe(III) reduction (Fig. 7a). Simulations with varyingsoil BC revealed dynamic pH change at lower BC (Figs. 8and 9, with BC= 1 as the reference simulation) and stabi-lized pH at higher BC. At constant temperature, rates of bothmethanogenesis and Fe(III) reduction increased significantlyat lower BC due to pH control. At lower BC, when pH changeis not well buffered, higher pH accelerated CH4 and Fe(II)production rates (Fig. 7), giving much higher apparent tem-perature responses, while at higher BC with stabilized pH inthe system, apparent temperature responses of these redoxprocesses were significantly lower than the reference sim-

ulation (BC= 1). Variations in pH buffering capacity gen-erated large variations in apparent temperature responses ofmethanogenesis and Fe(III) reduction due to this pH feed-back loop.

4 Discussion

4.1 Synthesized soil geochemistry and model validation

Soil geochemical characteristics represent important abioticcontrols on anaerobic carbon decomposition and subsequent

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672 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

Figure 8. Cluster analysis of soil geochemical properties related to CO2 and CH4 production using Ward’s linkage method. (a) Clusteranalysis of measured soil geochemical characteristics and observed CO2 and CH4 production (n= 42); (b) cluster analysis of modeledresults (n= 42). Model-simulated CO2, CH4, and Fe(II) production, and final pH values are labeled as M_CO2, M_CH4, M_Fe, and M_pH,respectively. Biomasses of methanogens and iron reducers were tracked in the model and labeled M_Meb and M_Feb, respectively.

CO2 and CH4 production. SOC content, soil pH, water ta-ble position, C : N ratio, and landscape position were all sug-gested to contribute to the variability in anaerobic CO2 andCH4 production (Lee et al., 2012; Schädel et al., 2014; Treatet al., 2015). We synthesized incubation data for gelisol soilsfrom different pedons and soil moisture regimes representingheterogeneity across the Barrow Environmental Observatory(BEO). This coordinated dataset allowed us to focus on in-dividual factors and their roles in relation to anaerobic CO2and CH4 production.

Carbon released as CO2 and CH4 during anoxic incuba-tions decreased with depth. Permafrost was associated withlow levels of CO2 production and very low CH4 production,consistent with a previous synthesis (Treat et al., 2015). Nev-ertheless, permafrost TOAC, WEOC, and SOC concentra-tions were all comparable to organic soils, suggesting highsubstrate availability but low microbial activity. This trendis consistent with previous studies (Walz et al., 2017; Treatet al., 2015), where highest microbial abundance and diver-sity were observed in surface soil, and permafrost containedlow microbial abundance (Treat et al., 2014; Waldrop et al.,2010). Among surface soils, higher moisture in low-centeredpolygon soils significantly promoted CO2 and CH4 produc-tion and the accumulation of fermentation products (mea-sured as TOAC), emphasizing the importance of soil SOCcontent and moisture as strong environmental drivers for car-bon decomposition. Given the bias in correlation analysiscreated by the skewed distribution of CO2 and CH4 produc-tion in our dataset, additional cluster analysis was performedbased on data similarity rather than correlations. High sim-ilarity of soil attributes (depth, moisture, pH, C : N ratio,SOC, TOAC) with CH4 production (Fig. 8a) was found, sug-gesting methanogenesis is potentially controlled by a set ofsoil geochemical characteristics in the local microenviron-ment.

These synthesized observations support the major assump-tions of our model development: (1) the coupled hydroly-sis and fermentation processes converting macromolecularSOM into low-molecular-weight organic acids are the rate-limiting step; and (2) different rates of CO2 and CH4 pro-duction from different soil layers can be attributed to vari-ations in microbial activity manifested as differences in ini-tial microbial biomass or growth rates. Additional observa-tions of substantial Fe(III) reduction and associated pH in-creases during anaerobic decomposition (Fig. S9) confirmedthe need to simulate pH variations associated with redox re-actions and corresponding microbial responses. This anaero-bic carbon decomposition framework adequately modulatedthe involved biotic and abiotic interactions by splitting thecarbon flow to different redox reactions and simulating pHbuffering capacity to mediate associated changes in acidityor alkalinity.

The model presented here identified fermentation, aceto-clastic methanogenesis, and acetotrophic iron reduction askey mechanisms for anaerobic CO2 and CH4 production(Vaughn et al., 2016; Lipson et al., 2010). Although deni-trification, ammonification, and sulfate reduction are all ther-modynamically more favorable, low nitrate and sulfate con-centrations in BEO soils limit flux through these pathways(Newman et al., 2015). We performed another cluster analy-sis on the model output (Fig. 8b), where we not only simu-lated fermentation, methanogenesis and iron reduction rates,and associated pH changes but also tracked the biomass ofmethanogens (M_Meb) and iron reducers (M_Feb). A den-drogram depicting data similarity showed four distinct clus-ters consisting of WEOC, CO2 (CO2 prediction), ferrous(Fe(II) prediction), and CH4 (CH4 prediction) that closelyassociated with soil geochemical properties and incubationtemperature. This result is similar to the cluster analysisof synthesized data, demonstrating that the proposed modelstructure captured major relationships between carbon min-

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eralization and soil geochemical attributes. Predicted CH4production is strongly influenced by incubation temperature,soil pH, soil moisture, and depth which determine the sizeof the methanogen population. This model prediction is con-sistent with previous studies on the vertical distribution ofmethanogen population (Waldrop et al., 2010). Environmen-tal factors, such as labile organic matter, water table depth,and soil redox status, soil alkalinity, and salinity (Wachingeret al., 2000; Rivkina et al., 2007; Høj et al., 2006; Yang etal., 2017) are all likely to contribute to the variabilities in thedistribution and abundance of methanogens and subsequentmethane production.

4.2 Temperature and pH response of anaerobic carbondecomposition

Rising temperature promotes anaerobic carbon decomposi-tion, resulting in increased rates of anaerobic CO2 and CH4production (Treat et al., 2014; Lupascu et al., 2012). It iswidely recognized that methanogenesis is more sensitive totemperature than respiration (Yvon-Durocher et al., 2012,2014), and it is usually associated with large variations.Segers estimated the Q10 value of methanogenesis rangedfrom 1.5 to 28 among 1043 incubation experiments usingwetland soils (Segers, 1998). Our data synthesis revealedhigher temperature sensitivity than other reported values.High estimated temperature sensitivity across the freezingpoint of water has previously been documented (Waldrop etal., 2010) and further attributed to limited water availabilityfor microbial activities at subzero temperature (Tilston et al.,2010). Ratkowsky et al. proposed a quadratic relationship forthe temperature dependence of microbial growth rates thatmodeled low-temperature growth better than the Arrheniuslaw (Ratkowsky et al., 1982). Our simulations suggest betterprediction of methanogenesis with this temperature responsefunction, possibly due to a more suitable representation ofgrowth limitation of methanogens at subzero temperature.Methanogenesis rates are also influenced by the availabil-ity of alternative electron acceptors and carbon source. Pro-cesses contributing to the accumulation or consumption ofcarbon substrates and competing electron acceptors may re-spond differently to temperature change, which could furthercomplicate the temperature sensitivity of methanogenesis.Current modeling approaches heavily depend upon empiri-cal temperature response functions, which may be associatedwith large uncertainties due to variations in the selection ofdata and curve-fitting methods. Extrapolation of carbon de-composition rates, particularly methanogenesis rates, into afuture warmer climate remains uncertain. More accurate sim-ulations will require additional information on geochemicalproperties that contribute to the variations of methanogensdistribution and methanogenesis activity.

pH values impose fundamental physiological restrictionson microbial activities. Soil pH ranges from acidic to cir-cumneutral (pH 4–7.5) in northern Alaska and varies sub-

stantially through the soil profile and along the microtopo-graphic gradient. Accumulation of organic acids in anoxicsoils leads to pH decline (Jones et al., 2003), while consump-tion of organic acids by methanogenesis and iron reductionincreases the alkalinity of the system via the production ofHCO−3 and OH− (Drake et al., 2015; Roy Chowdhury et al.,2015; Howell et al., 1998). The interplay of these processesleads to strong nonlinear pH feedbacks in the system, andprevious studies have observed up to 1–2 pH unit changesduring short-term anoxic incubations (Xu et al., 2015; Drakeet al., 2015; Roy Chowdhury et al., 2015). These relation-ships between pH and organic carbon decomposition canvary in sign and magnitude. Our model simulations withmechanistic pH evolution indicate that constant pH assumedin previous models may cause significant errors in simulatinglong-term anaerobic CO2 and CH4 production. The intrinsicsoil pH buffering capacity plays a large role in stabilizingsoil pH and may be heterogeneous depending upon solutionacidity or alkalinity, cation exchange capacity and residualacidity or mineral dissolution. These properties derive fromSOM characteristics, moisture, mineral content, and addi-tional geochemical properties, leading to complex correla-tions between soil pH and SOC decomposition rate that re-quire future investigation.

4.3 Fast-decomposing carbon pool

Substrate availability is a primary determinant of potentialCO2 and CH4 production (Lee et al., 2012; Schuur et al.,2015; Tarnocai et al., 2009). Total SOC is composed ofheterogeneous C pools characterized by different turnovertimes. Carbon release during short-term incubation origi-nates from the C pool with relatively rapid turnover. The sizeand turnover time of this quickly metabolized carbon poolare usually estimated by two-pool or three-pool conceptualmodels with a maximum likelihood solution using time se-ries of CO2 data (Schädel et al., 2013). A previous study onSiberian permafrost soils using a two-pool model estimateda turnover time of 0.26 years for the fastest-responding pool(Knoblauch et al., 2013). A three-pool model was appliedusing more extensive incubation datasets collected from 23high-latitude ecosystems, yielding an estimate of a 0.35-yearmean turnover time for the fastest-responding carbon pool(Schädel et al., 2014).

In our synthesis study, we directly quantified WEOC andassumed it represented the fast-decomposing labile carbonpool. The size of the labile carbon pool is constant duringanaerobic decomposition, while total CO2 and CH4 releaserepresent up to 194 % of the labile carbon pool, indicat-ing continuous replenishment of the labile carbon pool fromnon-labile carbon pools within the hierarchy. The replen-ishment of the labile carbon pool can be attributed mostlyto decomposition of SOM1 and SOM2 pools with fasterturnover (Koven et al., 2013). Overall, we estimated the fast-decomposed carbon pool is approximately 2 %–4 % of total

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674 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

Figure 9. Model-estimated turnover rates of the fastest-decomposing carbon pool. Organic, mineral, and permafrostlabels represent estimations from our model simulations (ratesestimated at 4 ◦C). Schadel data represent turnover rates estimatedvia a three-pool model from pooled anaerobic incubations withnormalized incubation temperature of 5 ◦C (tags 1, 2, and 3 repre-sent pool estimation from different soil types: 1. organic, 2. mineral<1 m, 3. mineral >1 m). Knoblauch data are rate estimates (at4 ◦C) made via a two-pool model (Schädel et al., 2014; Knoblauchet al., 2013). Open symbols represent the average values, and thevertical lines represent the estimated range.

SOC, similar to previous estimates. The turnover time calcu-lated from the fermentation rate was comparable to estimatesof the turnover time of the fastest-responding carbon poolin previous studies (Fig. 9), suggesting these quantificationsand parameterization in the anaerobic carbon decompositionframework apply broadly.

4.4 Key features of the anaerobic model frameworkand future considerations

Here, we present an anaerobic carbon decomposition frame-work by combining three well-known modeling approachesdeveloped in different disciplines. A pool-based model torepresent upstream carbon transformations and replenish-ment of a DOC pool, a thermodynamically based model tocalculate rate kinetics and biomass growth for methanogen-esis and Fe(III) reduction, and a humic ion-binding modelfor aqueous-phase speciation and pH calculation are imple-mented into the open-source geochemical model PHREEQC(Charlton and Parkhurst, 2011). The model framework pre-sented here has several unique features. First, this modelis built upon a thermodynamically based approach, whichallows consistent parameterization of individual reactionsalong the redox ladder. Such a model structure is particu-larly useful in circumstances when function-specific micro-bial growth is difficult to quantify and parameterize. Second,calculations of free energy changes of redox couples are usedto modulate redox reaction hierarchy. Considering the dif-ficulty in obtaining growth-associated parameters for everyfunctional group, a thermodynamically based approach sig-

nificantly decreases the number of parameters that are dif-ficult to measure. In addition, proton production and con-sumption during redox reactions are incorporated into a dy-namic pH calculation, allowing various simulations on aque-ous solubility and reactivity of different elements. The anaer-obic carbon decomposition framework presented here holdsa significant advantage over traditional models in simulatingcarbon decomposition process within a wide range of envi-ronmental settings.

In permafrost-affected regions, studies consistently iden-tify iron reduction, denitrification, and sulfate reduction (Lip-son et al., 2010, 2013; Ernakovich et al., 2017; Hansenet al., 2007) as alternative anaerobic pathways, which arerecognized as energetically more favorable processes thanmethanogenesis. Fe reduction makes a significant contribu-tion to total respiration (Roy Chowdhury et al., 2015; Hern-don et al., 2015), and adding Fe reduction simulations to abaseline model (without Fe reduction or dynamic pH calcula-tions) caused faster decreases in TOAC and WEOC pools andincreased CO2 production as expected (Fig. S10). More indi-rect feedbacks were revealed when dynamic pH calculationwas enabled. With dynamic pH simulation during anaerobicdecomposition, the model revealed strong pH dynamics thatare counterbalanced by Fe reduction. By including both Fereduction and dynamic pH calculations, the model accuratelyreproduced the initial pH drop and subsequent pH rise duringincubations, which were commonly observed in permafrost-affected soils (Roy Chowdhury et al., 2015; Herndon et al.,2015). The new model framework presented here providesa basis for a deeper understanding of carbon decompositionunder oxygen-limited conditions where the importance of ac-counting for alternative election acceptors and pH feedbacksbecomes more pronounced. Future fine-scale experiments oncarbon decomposition using alternative electron acceptorswould be beneficial for more comprehensive parameteriza-tion of this model framework. Additional observations ontemperature and pH sensitivity of specific redox reactionswould also be quite useful in reducing large uncertaintiesgenerated by the current representation of temperature andpH responses. Application of such a modeling framework atthe field scale requires close coupling with hydrology mod-els to facilitate estimations of aqueous-phase concentrations.Additional assumptions on vertical mixing and gas diffusionin the soil column should also be considered.

5 Conclusions

Microbial processes are the driving forces for biogeochemi-cal cycling of soil carbon and are subjected to environmentalconstraints beyond temperature and organic substrate avail-ability. The present study incorporated microbial redox re-actions and mechanistic pH evolution to simulate anaerobiccarbon decomposition in Arctic soils with depth and acrosssoil moisture gradients. Our data synthesis and modeling re-

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J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization 675

sults quantify direct effects of temperature on anaerobic car-bon decomposition, as well as indirect effects of soil geo-chemistry that cause strong redox reaction–pH feedback. Weidentified substantial pH feedbacks on the predicted CO2and CH4 production. The anaerobic carbon decompositionframework presented in this study provided the essentialmodel structure to incorporate redox reactions of alternativeelectron acceptors for accurate simulation of CO2 and CH4production. Soil geochemistry imposes critical constraints onSOM decomposition and further regulates permafrost carbonfeedback in response to changing climate.

Code and data availability. PHREEQC (version 3) is publiclyavailable at http://wwwbrr.cr.usgs.gov/projects/GWC_coupled/phreeqc/ (last access: January 2019).

The model is archived at https://doi.org/10.5440/1430703(Zheng et al., 2018c), with a detailed description of model imple-mentation, input files, and various sensitivity analyses described inthis paper.

Datasets used in this work can be found athttps://doi.org/10.5440/1168992 (Herndon et al., 2017),https://doi.org/10.5440/1393836 (Zheng and Graham, 2018),and https://doi.org/10.5440/1288688 (Zheng et al., 2017),and a synthesis of the incubation data is available athttps://doi.org/10.5440/1440029 (Zheng et al., 2018a).

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676 J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization

Appendix A: Anaerobic carbon decomposition model

This section lists reactions used in the anaerobic carbon de-composition model. Under anaerobic conditions, dissolvedorganic carbon is converted to low-molecular-weight organicacids via fermentation. One simplified fermentation reactionis used to represent this lumped fermentation process, whereone-third of the fermented organic carbon is converted toCO2 (Tang et al., 2016; Xu et al., 2015):

C6H12O6+ 4H2O→ 2CH3COO−+ 2HCO−3 + 4H++ 4H2.

(AR1)

This fermentation reaction generates protons and de-creases pH in the system. Fermentation products acetate andH2 are further consumed via methanogenesis and iron reduc-tion. The growth equations of methanogenesis and iron re-duction were derived for each group using a thermodynami-cally based approach, in which biomass synthesis is includedin paired electron donor and electron acceptor half reactions.A general molecular formula (C5H7O2N) is used for micro-bial biomass, and the growth equations are written as (Istoket al., 2010)

1.5H++ 98.2H2O+NH+4 + 103.7CH3COO− (AR2)→ C5H7O2N+ 101.2HCO−3 +CH4

84.9H++NH+4 + 85.9HCO−3 + 333.5H2 (AR3)→ C5H7O2N+ 255.6H2O+ 80.9CH4

72.1H2O+NH+4 + 150.2Fe3++ 21.3CH3COO− (AR4)

→ C5H7O2N+ 150.2Fe2++ 167.4H++ 37.5HCO−3

5HCO−3 +NH+4 + 114.8Fe3++ 57.4H2 (AR5)

→ C5H7O2N+ 114.8Fe2++ 110.8H++ 13H2O.

In addition, Fe(III) concentration was calculated based onthe dissolution of representative amorphous ferric hydrox-ides (Reaction AR6), with a solubility constantKs0 = 103.96.This process consumes many protons and contributes to pHincreases.

Fe(OH)3(s)+ 3H+↔ Fe3++ 3H2O (AR6)

A complete set of rate constants used in this model can befound in Table S1.

Appendix B: Temperature and pH response functions

We used the CLM-CN temperature response function(Eq. B1) in our simulations (Thornton and Rosenbloom,2005). Additional tested temperature response functions in-cluded Eq. (B2), which is used by the CENTURY model(Grosso et al., 2005), the Arrhenius equation (Eq. B3) used inecosys (Grant, 1998), and the quadratic Eq. (B4) (Ratkowskyet al., 1982). Tref is set at 25 ◦C, Ea is the activation energy(J mol−1), and R is the universal gas constant (J K−1 mol−1).Tm used in Ratkowsky’s model represents a conceptual tem-perature of no metabolic significance and is set at −8 ◦C inthis study.

lnf (T )= 308.56×(

171.02

−1

T − 227.13

)(B1)

f (T )= 0.56+ 0.465 arctan [0.097(T − 15.7)] (B2)

f (T )= e−EaR

(1T−

1Tref

)(B3)

f (T )=

(T − Tm

Tref− Tm

)2

(B4)

The discontinuous bell-shaped pH response function fromthe DLEM model was used here (Eq. B5; Tian et al., 2010):

f (pH)=1.02

1.02+ 106exp(−2.5pH)(0<pH<7) (B5)

f (pH)=1.02

1.02+ 106exp(−2.5(14-pH))(7<pH<14). (B6)

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J. Zheng et al.: Insights into soil geochemical influences on carbon mineralization 677

Supplement. The supplement related to this article is availableonline at: https://doi.org/10.5194/bg-16-663-2019-supplement.

Author contributions. DG, SDW, and BG conceived and organizedthe research study; PET, SLP, DEG, and JZ built the conceptualmodel framework; JZ preformed all model simulations; JZ andDEG drafted the manuscript. All authors contributed revisions tothe manuscript and have given approval to the final version of themanuscript.

Competing interests. The authors declare that they have no conflictof interest.

Acknowledgements. We appreciate comments and suggestions onearlier versions of this paper offered by Ethan Coon and manuscriptreviewers. The Next-Generation Ecosystem Experiments in theArctic (NGEE Arctic) project is supported by the Biologicaland Environmental Research program in the US Department ofEnergy (DOE) Office of Science. Oak Ridge National Laboratoryis managed by UT-Battelle, LLC, for the DOE under contract no.DE-AC05-00OR22725.

Edited by: Anja RammigReviewed by: Ali Ebrahimi and one anonymous referee

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