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Net exchanges of methane and carbon dioxide on the Qinghai-Tibetan Plateau from 1979 to

2100

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2015 Environ. Res. Lett. 10 085007

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Environ. Res. Lett. 10 (2015) 085007 doi:10.1088/1748-9326/10/8/085007

PAPER

Net exchanges of methane and carbon dioxide on the Qinghai-Tibetan Plateau from 1979 to 2100

Zhenong Jin1, Qianlai Zhuang1,2, Jin-ShengHe3,4, XudongZhu1 andWeimin Song3

1 Department of Earth, Atmospheric, and Planetary Sciences, PurdueUniversity,West Lafayette, Indiana 47907,USA2 Department of Agronomy, PurdueUniversity,West Lafayette, Indiana 47907,USA3 Department of Ecology, College ofUrban and Environmental Sciences, PekingUniversity, Beijing 100871, People’s Republic of China4 Key Laboratory of Adaptation andEvolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences,

Xining 810008, People’s Republic of China

E-mail: qzhuang@purdue.edu

Keywords:methane, carbon dioxide, climate change, terrestrial ecosystemmodel, Tibetan Plateau

Supplementarymaterial for this article is available online

AbstractMethane (CH4) is a potent greenhouse gas (GHG) that affects the global climate system. Knowledgeabout land–atmospheric CH4 exchanges on theQinghai-Tibetan Plateau (QTP) is insufficient. Usinga coupled biogeochemistrymodel, this study analyzes the net exchanges of CH4 andCO2 over theQTPfor the period of 1979–2100.Our simulations show that the region currently acts as a net CH4 sourcewith 0.95 TgCH4 y

−1 emissions and 0.19 TgCH4 y−1 soil uptake, and a photosynthesis C sink of

14.1 TgC y−1. By accounting for the net CH4 emission and the net CO2 sequestration since 1979, theregionwas found to be initially awarming source until the 2010swith a positive instantaneousradiative forcing peak in the 1990s. In response to future climate change projected bymultiple globalclimatemodels (GCMs) under four representative concentration pathway (RCP) scenarios, theregional source of CH4 to the atmosphere will increase by 15–77%at the end of this century. Netecosystemproduction (NEP)will continually increase from the near neutral state to around 40 TgCy−1 under all RCPs except RCP8.5. Spatially, CH4 emission or uptakewill be noticeably enhancedunder all RCPs overmost of theQTP, while statistically significantNEP changes over a large-scale willonly appear under RCP4.5 andRCP4.6 scenarios. The cumulative GHGfluxes since 1979will exert aslight warming effect on the climate systemuntil the 2030s, andwill switch to a cooling effectthereafter. Overall, the total radiative forcing at the end of the 21st century is 0.25–0.35Wm−2,depending on the RCP scenario. Our study highlights the importance of accounting for bothCH4 andCO2 in quantifying the regional GHGbudget.

1. Introduction

Methane (CH4), second only to carbon dioxide(CO2), is an important greenhouse gas (GHG) that isresponsible for about 20% of the global warminginduced by human activity since preindustrial times(IPCC, 2013). Because CH4 has amuch higher globalwarming potential (GWP) than CO2 in a timehorizon of 100 years, and actively interacts withaerosols and ozone (Shindell et al 2009), even smallchanges in atmospheric CH4 concentration will haveprofound impacts on the future climate (Bridghamet al 2013, Zhuang et al 2013). Quantifying regionaland global methane budgets has therefore become a

research priority in recent years (Kirschkeet al 2013). Among all natural sources of CH4, globalwetlands are the single largest source that is respon-sible for emissions of 142–284 Tg CH4 per year(Kirschke et al 2013, Wania et al 2013). CH4

emissions from wetlands are the net results of CH4

production under anaerobic conditions, and oxida-tion by oxygen and transport through the soil andwater profile (Olefeldt et al 2013). Over 90% of theCH4 emitted to the atmosphere is oxidized bychemical reactions in the troposphere (Kirschkeet al 2013), while soil sinks through methanotrophyare indispensable as well (Spahni et al 2011, Zhuanget al 2013). The relative strength of sources and sinks

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determines the global net CH4 emission on variousspatial or temporal scales.

CH4 emissions from natural wetlands on the Qin-ghai-Tibetan Plateau (QTP) have drawn increasingattention (Chen et al 2013). This world’s highest pla-teau has been a large reservoir of soil carbon for thepast thousands of years because of the slow soildecomposition rate and relatively favorable photo-synthetic conditions comparedwith high-latitude coldecosystems (Kato et al 2013). The high carbon storageis distributed over a sporadic landscape that accom-modates more than half of China’s natural wetlands,and hence the QTP is responsible for 63.5% of CH4

emissions from natural wetlands in China (Chenet al 2013). In recent decades, natural wetlands on theQTPhave been expanding (Niu et al 2012).Withmoreavailable soil carbon substrate due to higher plant pro-duction and litter fall input (Zhuang et al 2010, Piaoet al 2012), CH4 emissions from this region have beenaccelerating and are projected to increase under futureclimate conditions (Chen et al 2013).

Recently, several site-level studies have advancedour understanding of CH4 emissions from wetlandson the QTP. Major emissions occur during the grow-ing season, and net effluxes can vary from −0.81 to90 μg m−2 h−1 at different locations (Jin et al 1999,Hirota et al 2004, Kato et al 2011, Chen et al 2013).Furthermore, flux tower observations indicate thatnon-growing season CH4 emissions in an alpine wet-land can contribute to around 45% of the annualemissions (Song et al 2015). These high variations inspace and time are the results of complex environ-mental control over CH4 emissions. Chen et al (2009)found that water table depth is a key factor that con-trols the spatial variations in CH4 emissions in an openfen on the eastern edge of the QTP. Soil temperature isalso an important regulator of CH4 emissions, sincethe QTP is underlain by extensive permafrost (Chenet al 2010, Yu et al 2013). In addition, plant successionfrom cyperaceous to gramineous species during wet-land degradationwill result in a net reduction in plant-aided transport of CH4 to the atmosphere (Hirotaet al 2004, Chen et al 2010). In contrast to the notice-able progress in field measurements and experiments,there is still a lack of systematic and quantitativeunderstanding of the regional CH4 budget for theQTP. The estimated wetland CH4 emissions of0.56–2.47 Tg per year (Jin et al 1999, Ding andCai 2007, Chen et al 2013, Zhang and Jiang 2014)should be viewed as preliminary results, because theyare calculated using a book-keeping approach, i.e. theestimate is a product of the wetlands area, the fluxmeasurements at a few sites, and the approximatenumber of frozen-free days. In addition, the role of thesoil consumption of atmospheric CH4 in most of thealpine steppe and meadow zones is highly uncertain(Kato et al 2011,Wei et al 2012, Zhuang et al 2013), butshould not be neglected when quantifying the regionalnet methane exchange (NME). More importantly, it is

necessary to consider both CH4 and CO2 dynamics inquantifying the regional GHGbudget of theQTP.

Here, we quantify the GHG budget of CH4 andCO2 for both historical data and for the 21st centuryon the QTP using a coupled biogeochemistry model,the terrestrial ecosystem model (TEM) (Zhuanget al 2004, Zhu et al 2013). The model is calibratedagainst field observations using a global optimizationmethod and applied for the period 1979–2100. Thecarbon-based GHG effect of CH4 and CO2, repre-sented by the NME and net ecosystem production(NEP, calculated as the difference between the grossprimary production and ecosystem respiration), areexamined using radiative forcing impact (Frolkinget al 2006). Our research objectives are to: (i) quantifysources and sinks with respect to CH4 and CO2 in thehistorical period of 1979–2011; (ii) explore how theNME and NEP will respond to future climate changes;(iii) attribute the relative contribution of CH4 andCO2 to the regional carbon budget and radiative for-cing, and (iv) identify research priorities to reducequantification uncertainties of the GHG budget in theregion.

2.Methods

2.1.Model frameworkThe TEM is a process-based ecosystem model thatsimulates the biogeochemical cycles of C and Nbetween terrestrial ecosystems and the atmosphere(Zhuang et al 2004, 2010). Within the TEM, two sub-models, namely the soil thermal model (STM) and theupdated hydrological model (HM), are designed tosimulate the soil thermal profile and hydrologicalprocesses with a daily time step, respectively. The STMis well documented for arctic regions and the TibetanPlateau (e.g. Zhuang et al 2010, Jin et al 2013). TheHM, inherited from the water balance model (WBM)(Vörösmarty et al 1989), has six layers for upland soilsand a single box for wetland soils (Zhuang et al 2004).We assume the maximum wetland water table depthto be 30 cm following Zhuang et al (2004), so that soilsare always saturated below 30 cm. These physicalvariables then drive the carbon/nitrogen dynamicsmodule (CNDM), which uses spatially explicit infor-mation on climate, elevation, soil, vegetation andwater availability, as well as soil- and vegetation-specific parameters, to estimate the carbon and nitro-gen fluxes and pool sizes of terrestrial ecosystems(Zhuang et al 2004, 2010).

The methane dynamics module (MDM) was firstcoupled with the TEM by Zhuang et al (2004), toexplicitly simulate the processes of CH4 production(methanogenesis), CH4 oxidation (methanotrophy)and CH4 transport between the soil and the atmo-sphere. During the simulation, the water table depthestimated by the HM, the soil temperature profile esti-mated by the STM and the labile soil organic carbon

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

estimated by the CNDM, as well as other soil andmeteorological information, were fed into the MDMso that the whole TEM and MDM are fully coupled(figure S1). CH4 production, a strictly anaerobic pro-cess, is modeled as a function of the labile carbon sub-strate, temperature, pH, and the redox potential insaturated soils. CH4 oxidation, occurring in the unsa-turated zone, depends on the soil CH4 and O2 con-centration, temperature, moisture and the redoxpotential. The net CH4 fluxes at the soil/water–atmo-sphere boundary are summations of different trans-port pathways (i.e. diffusion, ebullition and plant-aided emission), with a positive value indicating a CH4

source to the atmosphere and a negative value for aCH4 sink (Zhuang et al 2004).

2.2. Fieldmeasurements andmodel calibrationData used to calibrate the TEM were measured at theLuanhaizi wetland on the northeastern Tibetan Pla-teau (37° 35′ N, 101° 20′ E) from July 2011 toDecember 2013. This wetland is classified as an alpineMarsh, and has accumulated rich soil carbon of 24.5%on average for the top 30 cm soil layer due to slowdecomposition. An eddy covariance measurementsystem was installed at a height of 2 m above thewetland surface, which recorded CH4 fluxes every halfhour (Yu et al 2013). Amicro-meteorology stationwasset up adjacent to the eddy covariance tower tomeasure major environmental variables at half hourfrequency, including air and soil temperature, totalprecipitation, downward shortwave irradiance andrelative humidity. We parameterized the STM usingthe measured soil temperature at a 5 cm depth. Majorparameters for the MDM were calibrated so that thesimulations match the observed CH4 fluxes. Due to alack of high quality measurements, the water tabledepth was calibrated indirectly by fitting the final CH4

fluxes, and compared to the observed precipitation asa reference. Parameters for the CNDM were obtainedfrom our previous modeling studies on the TibetanPlateau (Zhuang et al 2010, Jin et al 2015).

The mathematical structure of the TEM is fixedand can be expressed as below:

Y f X eˆ ( ) (1)θ= ∣ +

where f is a conceptual function that represents allprocesses within the TEM, Y y y yˆ ( , , )N1 2= … is avector of the model outputs (e.g. time series of dailysoil temperature or CH4 fluxes), X is the model inputdata, ( , , , , )n1 2 3θ θ θ θ θ= … are n unknown para-meters to be calibrated, ande e e e{ ( ) , ( ), , ( )}N1 2θ θ θ= … are independently andidentically distributed (i.i.d.) errors with zero meanand constant variance. The goal of model calibrationwith classical methods is thus to find a parameter set θsuch that the predefined statistics of e can beminimized. In contrast, Bayesian theory treats θ asrandom variables having a joint posterior probabilitydensity function (pdf). The posterior pdf of θ can be

evolved from the prior distribution with observationsY such that:

p YL Y p

L Y p( )

( ) ( )

( ) ( )(2)post

prior

prior∫θ

θ θ

θ θ∣ =

∣

∣

where L Y( )θ∣ is the likelihood function. Assuming anon-informative prior in the form of p ( ) 1θ σ∝ − andresiduals which are i.i.d normal (Vrugt et al 2003), thelikelihood of a specific parameter set θ′ can becomputed as:

L Ye

( ) exp1

2

( )(3)

i

Ni

1

2⎡⎣⎢⎢

⎛⎝⎜

⎞⎠⎟

⎤⎦⎥⎥∑θ

θσ

′∣ = − ′

=

The influence of σ can be integrated out whenL Y( )θ′∣ is plugged into equation (2), so that the pos-terior pdf of θ′ is:

p Y e( ) ( ) (4)i

N

i

N

post1

2

0.5⎡⎣⎢⎢

⎤⎦⎥⎥∑θ θ′∣ ∝ ′

=

− ×

For complex nonlinear system models like theTEM, however, it is impossible to obtain an explicitexpression for p Y( ),post θ′∣ making analytical optimi-

zation out of the question (Vrugt et al 2003). Alter-natively, Markov chain Monte Carlo (MCMC)methods arewell suited to solving these problems.

In this study, we implemented an adaptiveMCMC sampler, named the shuffled complex evolu-tion Metropolis algorithm (SCEM-UA) for globaloptimization and parameter uncertainty assessment.While the theoretical bases and computational imple-mentation of the SCEM-UA can be found in (Vrugtet al 2003), we outline the key steps with an example ofSTMoptimization below:

(i) Initialize parameter space. Select parameters tobe calibrated, and assign prior range to eachparameter (table 1).

(ii) Generate sample. Randomly sample s sets of

parameter combination { , , , }s1 2θ θ θ⎯→⎯ ⎯→

…⎯→

from the

uniform prior distribution, where iθ⎯→is a vector of

eight parameters for the STM in table 1.

(iii) Rank sample points. Compute the posterior

density of each iθ⎯→using equation (3), and sort the

s points in 8-dimensions with decreasing order ofposterior density. Store the s points and theircorresponding posterior in array D with dimen-sions of s 9× .

(iv) Initialize Markov chains. Assign the first kelements of D as the starting locations of ksequences.

(v) PartitionD into complexes. Partition s rows of Dinto k complexes C C C{ , , },k1 2 … such that eachcomplex contain m s k/= points. More specifi-cally, the i th− complex is assigned everyk j i( 1)− + rows of D,where j m1, ,= … .

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

(vi) Evolve each complex and sequence. Use thesequence evolution Metropolis algorithm toevolve each sequence and complex (Vrugtet al 2003).

(vii) Shuffle complexes. Combine each point in allcomplexes back with D in order of decreasingposterior density.

(viii) Apply stop rule. Stop when convergence criteriaare satisfied; otherwise, go to step (v). Amaximum of 100 000 runs for the STM and200 000 runs for the MDM was imposed tooverride the stopping rule for computationalconsiderations.

We selected s 500= and k 10= following Vrugtet al (2003) for complex model optimization. Giventhe complexity of our coupled-TEM, each run cost atleast 1 minute, including reaching model equilibrium,model spin-up and transient simulations for the per-iod 2011–2013. In this case, parallel implementationof the SCEM-UA was required to obtain computa-tional efficiency. We developed a parallel R version ofthe SCEM-UA using the Open Message Passing Inter-face (MPI; Gabriel et al 2004) and the Rmpi package(Yu et al 2002) on Purdue University’s Conte compu-ter clusters. This parallel SCEM-UA program wouldevoke a master node that controlled the workflow andmessage communication among 10 slave nodes. Eachslave node was responsible for the computation ofposterior density (i.e. step (iii)) and for the evolutionof a specific group of complexes and sequences (i.e.step (vi)). Compared to the parallel implementation ofthe SCEM-UA in Vrugt et al (2006), our program does

not require the target model (e.g. the TEM in thisstudy) to be written in the MPI structure, but onlytreats it as a black box to be executed using a systemcall in R. The source code for this parallel R version ofthe SCEM-UA is available upon request.

2.3. Regional extrapolationTo make spatiotemporal estimates of the CO2 andCH4 fluxes on the QTP using the TEM, the modelwas run at a spatial resolution of 8 × 8 km and with adaily time step from 1979 to 2100. Static dataincluding vegetation types, elevation, and soil tex-ture were the same as those in Jin et al (2013). SoilpH data was derived from the China Dataset of SoilProperties for Land Surface Modeling by Wei et al(2013). The wet soil extent from Papa et al (2010)was used to determine the distribution of wetlands(CH4 source) and uplands (CH4 sink) within eachpixel (figure 1(b)). For this temporally dynamicaland 25 × 25 km resolution data set, we interpolatedthe maximum fractional inundation over 8 × 8 kmusing the nearest neighbor approach. It should benoted that the actual wetland distribution was lesscontinuous than was shown in figure 1(b). Theinland water body was excluded based on the IGBPDISCover Database (Loveland et al 2000). Seasonalflooding of the wetland was indirectly represented bythe fluctuation of the water table depth below andabove the soil surface in our model. Climate inputdata for the historical period of 1979–2011, includ-ing radiation, precipitation, air temperature andvapor pressure, were interpolated from the latestglobal meteorological reanalysis product ERA-interim (0.75° grid) published by the European

Table 1.Prior and posterior range of parameters calibrated in Soil ThermalModule (STM) andMethaneDynamicsMod-ule (MDM).

Acronym Definition Prior Posteriora Unit

Soil ThermalModel

condt1 Thawing soil thermal conductivity forfirst layer [0.6, 3.5] [2.724, 3.266] Wm−1 K−1

condf1 Frozen soil thermal conductivity forfirst layer [0.6, 4.0] [0.6, 0.874] Wm−1 K−1

condt2 Thawing soil thermal conductivity for second layer [0.6, 3.5] [1.194, 2.110] Wm−1 K−1

condf2 Frozen soil thermal conductivity for second layer [0.6, 4.0] [0.723, 1.236] Wm−1 K−1

spht1 Thawing soil heat capacity for first layer [0.5, 4.0] [1.604, 2.615] MJm−3 K−1

sphf1 Frozen soil heat capacity for first layer [0.5, 4.0] [2.066, 3.018] MJm−3 K−1

spht2 Thawing soil heat capacity for second layer [0.5, 4.0] [1.474, 2.445]] MJm−3 K−1

sphf2 Frozen soil heat capacity for second layer [0.5, 4.0] [1.655, 2.586] MJm−3 K−1

MethaneModel

MGO Maximumpotential CH4 production rate [0.2, 0.7] [0.377, 0.545] μmol L−1 h−1

PQ10 Dependency of CH4 production on soil temperature [2.0, 3.5] [2.179, 2.654] unitless

MaxFresh MaximumdailyNPP for a particular ecosystem [2.0, 8.0] [5.606, 7.164] gCm−2 day−1

PROREF Reference temperature inQ10 function [−3.0, 1.0] [−1.546,−0.256] °C

winflxR Winter CH4 emission ratio [0.4, 0.9] [0.628, 0.756] Unitless

Qdmax Maximumdrainage rate belowwater table [0.1, 2.0] [0.251, 0.447] Mmd−1

a Posterior range is defined as the shortest interval that include 68%of the total samples.

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

Centre for Medium-Range Weather Forecasts(ECMWF) (Dee et al 2011). To reduce bias in airtemperature caused by complex topography, we usedelevation as a covariate during interpolation accord-ing to Frauenfeld et al (2005). Future climatescenarios from 2006 to 2100 were generated underfour representative concentration pathways (RCPs)with six global climate models (GCMs) withinCoupled Model Inter-comparison Project phase 5(CMIP5), including: CESM1-CAM5, GFDL-CM3,

GISS-E2-H, HadGEM2-ES, MPI-ESM-Mr andMRI-CGCM3 (figures S3, S4). A total of 24 simula-tions (4 scenarios × 6 GCM datasets) were processedto construct future projections. These selectedGCMs, compared with many other candidates, ingeneral had smaller biases in surface temperatureand total precipitation across the Tibetan Plateau(Su et al 2013). A detailed description of theseCMIP5 GCMs and the data processing method areprovided inmethods S1.

Figure 1.Vegetation distribution ofQinghai-Tibetan Plateau (upper panel) andmaximum fractional inundation (lower panel).

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

2.4. AnalysisTo highlight the spatial pattern of the climate inducedchanges in GHG fluxes, we calculated the spatialdifference between baseline simulations usingECMWF data in the 2000s and future predictions withfour RCP scenarios in the 2090s. A statistical test of thedifference in means was performed, and regions withstatistical insignificance (α= 0.05) were masked out.To quantify the GHG effect of the continuous CH4

and CO2 fluxes from the QTP for the study period of1979–2010, we calculated the net radiative forcingimpact according to Frolking et al (2006). The lifetimeof an individual net input of CH4 into the backgroundatmosphere was represented by a first-order decayfunction such that:

( )r t r tCH ( ) exp / (5)4 0 CH4τ= × −

where r0 is the initial CH4 perturbation, and CH4τ is theadjusted time for CH4 (∼12 y). A linear superpositionof 5 different first-order decay pools was used todescribe the more complicated behavior of CO2 in theatmosphere:

( )r t f tCO ( ) exp / (6)i

i i2

0

4

∑ τ∼ −=

where the fraction of each pool, f ,i and the poolspecific adjustment time, ,iτ were set to be 26%, 24%,19%, 14%, 18% and 3.4, 21, 71, 421, 108 y, respectively(Frolking et al 2006). The instantaneous total radiativeforcing from individual gas contributions since thereference time (here the year 1979) is given by:

( )

(7)

t A f s t s sRF( ) ( ) exp ( ) / di

i i io

t

i i0

5

⎜ ⎟⎛⎝

⎞⎠∫∑ ξ τ= ⋅ Φ − −

=

in which i 0–4 =  is CO2 and i 5 =  is CH4, and iξ is amultiplier for indirect effects (1.3 for CH4 and 1.0 forCO2), Ai is the GHG radiative efficiency (1.3 × 10−13

W m−2 kg−1 for CH4 and 0.0198 × 10−13 W m−2 kg−1

for CO2), fi is the fractional multiplier (1 for CH4 andsee equation (6) for CO2 values), and s( )iΦ is the netflux of GHG i at time s relative to the reference year of1979. The integral term is thus the cumulative flux ofgas i at time t since the reference start point (i.e. theyear 1979) after partial to complete decay in theatmosphere. The numerical integration was appliedwith an annual time step. It should be noted, however,that our calculation here was only for the goal ofcomparing the relative contributions of CO2 and CH4

fluxes since 1979 to the radiative forcing, rather thanto give an accurate estimation of the absolute values oftheGHGeffect.

3. Results

3.1.Model optimization and validationBy applying the SCEM-UA method, initial parameterranges evolved into narrower posterior intervals(table 1). Both the STM and MDM outputs were able

to reproduce the seasonal dynamics of the observeddaily soil temperature and CH4 fluxes (figure 2). Theadjusted R2 and RMSE for the soil temperaturesimulated with the optimal STM are 0.95 and 1.88 °C,respectively. The model performance is comparablewith other modeling results (Wania et al 2009, Jinet al 2013, Zhu et al 2014), with only one under-estimation during the summer of 2013. The MDMalso performed well (R2 = 0.82, RMSE= 18.41 mgCH4 m

−2 day−1) in capturing the annual magnitude,cycling and a small peak of CH4 burst in spring.Considering the uncertainties in the CH4 flux mea-surements (Yu et al 2013) and the CH4 modelstructures (Wania et al 2013), our model performancewas comparable with similar studies (Wania et al 2009,Lu and Zhuang 2012, Zhu et al 2014). Parameteruncertainty was well constrained (figures 2(a), (c)),indicating that a global search near the optimal spacewould produce many parameter sets to allow modelsimulationmatching the observations. The water tabledepth followed the observed daily precipitation pat-tern (figure 2(b)). A severe drought was detected forthe 2012 summer, which was also reflected by thedistinctively lowCH4 emission during that year.

Due to a limited number of available field studiesof CH4 fluxes on the QTP, our model validation wasdone by comparing values reported in the literature toour simulations for the nearest 8 × 8 km pixel(table 2).Model estimates reasonablymatchmost fieldmeasurements with respect to the mean and range,except for an apparent underestimation of the excep-tionally high CH4 emission rate from the littoral zonein Chen et al (2009). Considering the high variationamong field measurements from different wetlandtypes and vegetation covers, the model simulationswere able to fit the mean (R2 = 0.87, RMSE= 67 for alldata and R2 = 0.96, RMSE= 21mg CH4 m−2 day−1

excluding Chen et al (2009)). The validation gave usconfidence for model extrapolation and regional CH4

budget estimation.

3.2. Spatiotemporal trends of CO2 andCH4fluxesOur estimated annual regional mean net primaryproduction (NPP) (145–170 gC m−2 y−1) increasedslightly for the historical period, and fluctuated underfuture scenarios (figure 3(a)). NPP increased almost40% under RCP6.0 (241 22.3 gC± m−2 y−1) and50% under RCP8.5 (252 22.4 gC± m−2 y−1) at theend of the 21st century compared to the 2000s. RCP2.6and RCP4.5 showed a similar trend in NPP underhigher emission scenarios before the 2050s, but leveledoff thereafter at around 210 and 230 gC m−2 y−1,respectively. Simulated regional mean NEP showedhigh variation around 0 gC m−2 y−1 for the period1979–2011, and decreased continually under futureprojections except for RCP8.5 (figure 3(b)). The NEPdensity was lowest under RCP4.5 ( 33.1 9.5 gC− ±m−2 y−1), followed by RCP6.0 ( 30.3 11.4 gC− ± m−2

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

y−1) and RCP2.6 ( 26.8 11.5 gC− ± m−2 y−1) for the2090s. Under RCP8.5, the NEP density decreased tothe 2060s but reversed to 19.5 16.1 gC− ± m−2 y−1,possibly because heterogeneous soil respiration (RH;figure S5) was stimulated more than NPP under thiswarmest andwettest scenario.

Simulated annual mean CH4 emissions frompotential wetland areas increased gradually from6.3 gCH4 m

−2 y−1 in 1979 to 8.5 in the 2050s underdifferent scenarios, but diverged noticeably thereafter(figure 3(c)). The highest emissions for the 2090s fromRCP8.5 projections (12.7 1.9± gCH4 m−2 y−1)increased 70% relative to the beginning of the 21stcentury, followed by 46%, 30% and 16% underRCP6.0, RCP4.5 and RCP2.6, respectively. The rela-tive change percentages of CH4 emissions were in gen-eral higher than those of NPP, implying that CH4

production was more favored than photosynthesisunder future climate conditions. Annual mean CH4

uptake density (increased from 0.13 in the early 1980sto 0.17 gCH4m

−2 y−1 around the 2050s, and ended up

between 0.16 to 0.23 gCH4 m−2 y−1 in the 2090s) was

much smaller than that of emissions, while the inter-annual variations were similar under the four RCPscenarios (figure 3(d)).

Spatial patterns of the decadal mean NME showedsubstantial variations over the QTP (figure 4). For the2000s, net CH4 emissions simulated using reanalysisdata were similar to the average of the RCP scenarios(figures 4(a) and (b)), with high net CH4 emissionsoccurred at Zoige wetland and in the southwest part ofthe QTP. Net CH4 sinks based on the two maps werecomparable in magnitude, but high CH4 uptakes werefound at the southern edge of the QTP in figure 4(a)rather than in the northeastern part in figure 4(b). Atthe end of the 21st century, the NME increased notice-ably with respect to the magnitude over the QTPexcept for some southeastern parts (figures 1(c)–(f)).Among the models, RCP8.5 led to the strongest CH4

emissions in wetlands and the highest CH4 consump-tion in uplands. The difference in means between theRCP scenarios and the 2000s historical mean was

Figure 2. Simulated and observed soil temperatures at 5 cmdepth (a), water table depth (b) andCH4fluxes (c) at Luanhaizi wetlandstation. Solid blue lines show the optimal simulation, while shades of light blue are the 95% range of simulationswith the top 500parameter combinations. Red dots are observations.Water table depth is comparedwith observed daily precipitation data.

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

Table 2.Comparison betweenmodel simulations and literature reported fieldmeasurements of daily CH4flux density (mgCH4m−2 day−1).

Observation Simulation

Location Measurement time Vegetation type Mean Range Mean Range References

33.93N, 102.87E Jun.–Sep., 2005-2007 Kobresia tibetica 64.1 n.a. 171.5 [135.5, 208.8] Chen et al 2013

Cychrusmuliensis 311.28 n.a.

Eleocharis valleculosa 266.2 n.a.

33.1N, 102.03E Jun.–Aug., 2006-2007 362.4 [−2.4,NA] 205.5 [147.2, 264.6] Chen et al 2009

32.78N, 102.53E May–Oct., 2001-2002 Cychrusmmuliensis 71.04 [3.8, 240] 72.9 [46.7, 96.7] Ding et al 2004

37.48N, 101.2E Jul.–Sep., 2002 Hydrocotyle vulgaris 214 n.a. 163.9 [136.8, 181.5] Hirota et al 2004

Carex allivescers 196 n.a.

Scirpus distigmaticus 99.5 n.a.

Potamogenton pectinatus 33.1 n.a.

35.65N, 98.8E Jul. 24–Aug. 8, 1996 43.9 [−4.4, 147.6] 61.8 [50.2, 70.6] Jin et al 1998

Apr.–Sep., 1997 37.3 [6.5, 71.9] 46.3 [31.3, 66.9] Jin et al 1999

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etal

statistically significant (α= 0.05) for most of the studyarea (figures S6(a)–(d)). Future climate change wouldstrengthen the upland CH4 sink in the inner QTP, andsubstantially stimulate wetland CH4 emission innortheastern regions. For simulated NEP, most of thestudy region had a negative value (i.e. CO2 sink), withthe magnitude up to −30 gCm−2 y−1 during the 2000s(figures 5(a) and (b)). The patterns were comparablewith contemporary estimation for alpine grasslands byZhuang et al (2010) and Piao et al (2012). Strong CO2

sinks occurred in the southeast of the QTP, where for-est and shrubs were the dominant vegetation cover(figure 1(a)). CO2 sources accounted for a very limitedportion of the total study area. Spatial patterns of NEPevolved substantially under future scenarios(figures 5(c)–(f)). The QTP became a uniform carbonsink under RCP2.6, indicating disproportional climateimpacts on different ecosystem types. Increases inNEPwere profound under twomedian scenarios, withdifferences statistically significant (α= 0.05) for mostof the grassland (figures S6(f), (g)). Among these, thehighest sink (up to 50 gCm−2 y−1) occurred in the for-est ecosystem under RCP4.5 and in meadow regionsunder RCP6.0. NEP under RCP8.5 was distinctively

lower than results under other scenarios and not sta-tistically different from the historical 2000s mean(figure S6(h)), suggesting similar changes in the mag-nitude ofNPP andRH for all ecosystem types.

3.3. Regional GHGbudgetOur estimate of the total CH4 emissions from naturalwetland over the QTP was 0.95 Tg CH4 y

−1 during the2000s, which was within the estimation range of0.13–2.47 Tg CH4 y−1 from several other studies(table 3). The high variation among different estimateswas mainly due to the uncertainty in wetland areaestimates. Total CH4 consumption from those uplandsoils were 0.19 Tg CH4 y

−1 (i.e. 20% of the regionalCH4 emissions) for the 2000s, and increased slowerthan emissions under future scenarios (table 4), indi-cating that the QTP was likely to be a stronger CH4

source in the 21st century. The simulated regionalNEP of 10.22− Tg C y−1 for the period 2006–2011 waslower than those for other modeling studies coveringsimilar regions (Zhuang et al 2010, Piao et al 2012), buthigher than that of Yi et al (2014). The near neutralproperties of the historical NEP (also see figure 3(b))were consistent with the results of Fang et al (2010),

Figure 3.Annual trends of (a)mean net primary production (NPP); (b)mean net ecosystemproduction (NEP); (c)meanCH4

emissions frompotential wetland areas, and (d)meanCH4 consumption fromupland area for the period 1979–2100. Solid black linerepresents historical data from1979 to 2011. Solid color lines aremulti-modelmeans under different RCP scenarios; shadingrepresent one standard deviation.

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Environ. Res. Lett. 10 (2015) 085007 Z Jin et al

who found that soil C stock in China’s grassland didnot show a significant change during the past twodecades. Future NEP increased under all RCP scenar-ios (table 4). Counter-intuitively, higher increases inNEP occurred under RCP4.5 and RCP6.0 instead ofthe warmest and wettest RCP8.5, indicating thatcarbon accumulation was more likely favored withmodest warming andwetting.

Instantaneous radiative forcing due to net CH4

emission and net CO2 sequestration since 1979increased to a plateau during the 1990s, dropped tozero around the 2010 s, and decreased almost linearly

afterwards (figure 6(a)). Therefore, climate change inthe 21st century is likely to trigger negative feedback(cooling) in the climate system. Among these, the fast-est decreased rate occurred under RCP4.5, indicatingthat this moderate climate warming scenario stimu-lated vegetation production much more than themethanogenesis process. A seemingly level-off trendafter the 2080s was identified for the RCP8.5 scenario,most likely because of declining NEP (figure 3(b)).Cumulative radiative forcing was positive until the2030s, and become negative in an accelerated manner(figure 6(b)). Thus, the cumulative GHG fluxes from

Figure 4. Spatial patterns ofmean annual netmethane exchange (NME) under different climate scenarios: (a) ECMWF reanalysisdata for the 2000s; (b)meanCMIP5RCPs from2006–2010; (c) CMIP5RCP2.6 for the 2090s; (d) CMIP5RCP4.5 for the 2090s; (e)CMIP5RCP6.0 for the 2090s; (f) CMIP5RCP8.5 for the 2090s. Positive values indicate net emissions.

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Figure 5. Spatial patterns ofmean annual net ecosystem production (NEP) under different climate scenarios: (a) ECMWF reanalysisdata for the 2000s; (b)meanCMIP5RCPs from2006–2010; (c) CMIP5RCP2.6 for the 2090s; (d) CMIP5RCP4.5 for the 2090s; (e)CMIP5RCP6.0 for the 2090s; (f) CMIP5RCP8.5 for the 2090s.Negative values indicate carbon sinks.

Table 3.Existing estimates of CH4 emissions fromwetland and alpine plant communities on theQinghai-Tibetan Plateau.

WetlandArea Total

Methods Spatial Domain Period (×104 km2) (TgCH4 y−1) Source

Inventory Freshwater wetlands 1996–1997 18.8 0.7–0.9 Jin et al 1999

Inventory Natural wetlandsa 2001–2002 5.52 0.56 Ding andCai 2007

Inventory Alpinemeadow 2003–2006 87.5 0.13 Cao et al 2008

Meta-Analysis Natural wetlands 1.49 Chen et al 2013

Meta-Analysis Natural wetlands 3.76 1.04 Zhang and Jiang 2014

TEM Natural wetlands 1995–2005 11.5 2.47 Xu et al 2010

Coupled-TEM Natural wetlands 2001–2011 13.4 0.95 This study

a Natural wetlands include peatlands, freshwatermarshes, saltmarshes and swamps.

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the QTP will exert a slight warming effect on the cli-mate system until the 2030s, but an increasingly stron-ger cooling effect thereafter. The tipping point was 50years after the reference zero time point, roughly thetime required for nearly complete removal of theinitial CH4 perturbation input. At the end of the 21stcentury, cumulative mean radiative forcing wasbetween −0.25 and −0.35 W m−2, depending on dif-ferent RCP scenarios. Overall, our results show thatgiven a sustained CH4 source and CO2 sink on theQTP, the net GHG warming effect will only peak aftera few decades and will eventually contribute a morecooling effect to the climate system.

4.Discussion

4.1.Model optimizationOverall, our model calibration results were satisfac-tory, but a few tips should be mentioned whenapplying the global optimization method to complexsystem models with high parameter dimensions.Global optimization for problems without explicitanalytic expressions of the objective function ischallenging, because the algorithm must avoid beingtrapped by several local optima, while maintainingrobustness in the presence of parameter interactionand non-convexity of the response surface, and havinghigh efficiency in searching high dimensional space(Duan 2013). When applying the SCEM-UA, a trade-off between goodness of fit and computational cost isstill a problem for users.

The speed of algorithm convergence highlydepends on the model structure and parameter inter-actions. In our case, major parameters evolved to anarrow range after 100 000 total iterations (figure S2).In contrast, the MDM optimization converged muchslower despite the smaller number of parameters to be

optimized. We argue that this is mainly due to themulti-scalar function method used to simulate CH4

production and oxidation. For instance, if scalars f x( )1and f x( )2 are shaped with two parameters only, it isintuitive to imagine that many pairs of these two para-meters can produce similar results, as long as they canadjust scalars f x( )1 and f x( )2 in the opposite direction.Given that the scalar approach is extensively imple-mented in current ecosystem models (Zhuanget al 2004, 2010,Wania et al 2013, Zhu et al 2014), con-vergence criteria (such as the Gelman and Rubin diag-nostic suggested by Vrugt et al (2003)) can hardly bereached for most parameters when calibrating thesemodels.

On the other hand, sub-optimal is usually suffi-cient in practice. Increasing the sampling size fromparameter space can push the posterior of the objec-tive function to the high-end value (figure S2), but isby nomeans a guarantee of bettermodel performance.Most likely, many behavioral parameter combinationswith a similar capability of reproducing the observa-tions can be found by a search conducted in the fea-sible parameter space (Vrugt et al 2003). As shown inour example, the top 500 parameter sets (whose valuescan differ) for either the STMorMDMproduced closegoodness-of-fit and small variations in simulations(figure 2), indicating that the benefits from additionalsearching aremarginal.

4.2.Quantification of total GHGeffectTo quantify and compare the net GHG effect of asustained CH4 source and CO2 sink on the QTP, thetotal radiative forcing rather than the more widelyknownmetrics of theGWPwas computed in our studyaccording to Frolking et al (2006). As a tool originallydesigned for evaluating and implementing policies tocontrol multi-GHGs, the GWP is defined as the time-integrated radiative forcing due to a pulse emission ofa given component, relative to a pulse emission of anequal mass of CO2 (IPCC, 2013). It has usually beenintegrated over a somewhat arbitrary time horizon of20, 100 or 500 years, of which the choice of 100 years ismost commonly adopted (e.g. CH4 is 28 times CO2 interms of warming effect). Applying the GWP tobiogeochemical cases could be problematic as GHGfluxes are often sustained and temporally dynamicalfrom a long existing sink or source, even thoughmanystudies continue to use it because of its simplicity (e.g.Zhu et al 2013, Gatland et al 2014, Vanselow-Alganet al 2015). The method proposed by Frolking et al(2006) goes beyond the standard GWP approach suchthat (1) persistent emission or uptake of GHGs can beaccounted for, and (2) the instantaneous radiativeforcing for each year of simulation is quantified toallow a comparison ofmultiple gases in common unitsat any given time. Although the assumption of a nearconstant background atmosphere in Frolking’smethod is open to debate, and the decision on theappropriate pulse emission to consider (the new flux

Table 4.Decadalmean of regional greenhouse gas budget.

2000s 2050s 2090s

ECMWF 0.95

RCP2.6 0.99 1.14 1.13

CH4 Emission RCP4.5 0.97 1.18 1.28

(TgCH4 y−1) RCP6.0 1.00 1.17 1.42

RCP8.5 0.99 1.27 1.68

ECMWF −0.19a

RCP2.6 −0.20 −0.23 −0.23

CH4Consumption RCP4.5 −0.19 −0.24 −0.25

(TgCH4 y−1) RCP6.0 −0.20 −0.23 −0.27

RCP8.5 −0.20 −0.25 −0.31

ECMWF −10.22

RCP2.6 −18.08 −33.44 −37.01

NEP (TgC y−1) RCP4.5 −23.03 −38.96 −45.76

RCP6.0 −20.92 −30.27 −41.67

RCP8.5 −18.91 −35.66 −26.95

a Negative values indicatemethane or carbon sink.

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rate versus the change in the flux rate) could greatlychange the results of the simulation, it appears a usefulmethod in biogeochemical studies when accountingfor the budget of multiple sustained GHG fluxes.Frolking’s method has evolved with new findings inthe atmospheric sciences. For example, the fraction ofCO2 remaining in the atmosphere after a pulse inputcan be divided into four components (Joos et al 2013)instead of the original five-pool setting, and the multi-plier of the indirect effect for CH4 is now 1.65according to IPCC (2013). A test of these and otherparameter changes is beyond the scope of this study.

4.3. Uncertainties and futureworkThis is the first study to quantify both CH4 emissionsand net carbon exchanges on the QTP. However, thequantitative analysis is uncertain due to incompleterepresentation of physical and biogeochemical pro-cesses in the model (Bridgham et al 2013, Meltonet al 2013, Bohn et al 2015), inaccurate modelassumptions (Meng et al 2012), variations in theforcing climate data (Su et al 2013) and the extrapola-tion from site to regional scale. While progresses inmodel structures and mechanisms are usually slowand incremental, efforts to reducing data uncertaintyare more feasible in a short period to improve modelpredictability.

First, seasonal and inter-annual dynamics of thewetland extent is critical to CH4 modeling. Syntheticaperture radar (SAR) is currently the first choice todelineate wetland distribution, such as the monthlydistribution of surface water extent with∼25 km sam-pling intervals used in this study (Papa et al 2010).However, optical remote sensing is highly sensitive tocloud or vegetation cover. Alternative data from pas-sive and active microwave systems that will penetratecloud and vegetation cover is favored (Schroederet al 2010), but is currently unavailable over our studyarea. Some contemporary methane models, such asVIC-TEM and SDGVM, are capable of outputtingdynamical wetland extent as an internal product(Hopcroft et al 2011, Lu and Zhuang 2012), but theytend to substantially overestimate wetland area (Mel-ton et al 2013). In order to capture the seasonal flood-ing area, our study combined a static max potentialinundation map and simulated water table depth torepresent the wetland fluctuation. Improving themodeling abilities to capture wetland distribution andextent, especially the seasonality of water table dynam-ics, should be a research priority in the future (Zhuet al 2011).

The second uncertainty in quantifying CH4 emis-sions on a regional scale is from the spatial-scale extra-polation across highly heterogeneous but poorlymapped wetland complexes (Bridgham et al 2013). Inthis study, we only calibrated our model at one alpine

Figure 6. Instantaneous (a) and cumulative (b) radiative forcing for the period 1979–2100 calculated based onnet CH4 andCO2fluxessimulatedwith both historical (solid black line) and different RCP scenarios. Solid color lines aremulti-modelmeans, with shadingrepresenting one standard deviation.

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wetland site by assuming that the remaining wetlandsover the QTP have the same inert characteristics, butdifferwith climatic conditions. As amatter of fact, alpinewetlands on the QTP can be classified into peatlands,Marshes, and swamps, which have distinct character-istics in vegetation cover, hydrological processes, andsoil history (Chen et al 2013). However, due to severefield experimental conditions, high quality observa-tional data aremostly limited to the Lunhaizi wetland inHaibei Station (Hirota et al 2004, Yu et al 2013) and theZoige wetland (Chen et al 2009, 2013). Field researchersshould consider expanding field measurement foot-prints in the future, and data sharing with modelers ishighly recommended.

Finally, much of the uncertainty in future projec-tions is due to the poor agreement among CMIP5GCMs under the RCP scenarios (figures S3, S4). Themajority of the GCMs have cold biases of 1.1–2.5 °C inair temperature for the QTP, while they overestimateannual mean precipitation by 62%–183% (Suet al 2013). A multi-model ensemble approach, as wassuggested in the IPCC AR5 report (IPCC, 2013), wasused to configure the climatology uncertainty. Whilethe statistical interpolation method used to generatefine resolution data is simple and computationally effi-cient (Wilby et al 1998), dynamical downscaling withregional climatemodels wasmore ideal to compensatefor the shortage of coarse output from GCMs and tocapture details of the complex surface properties of theQTP (Ji and Kang 2012). However, due to the highcomputational cost, dynamically downscaling climatedata that cover representative GCMs under all RCPscenarios is generally not available to ecosystemmode-lers. A publicly accessible database like this wouldgreatly benefit the research community in futurestudies.

5. Conclusions

Using a coupled biogeochemistry model framework,this study analyzed the carbon-based GHG dynamicsover the QTP for the period 1979–2100. Our modelsimulations at the site level were able to closely matchthe field-observed soil temperature and CH4 flux aftercalibrating the model parameters using the SCEM-UAglobal optimization algorithm. Our study showed thatthe region currently acts as a CH4 source (emissions of0.95 TgCH4 y

−1 and consumption of 0.19 TgCH4 y−1)

and a CO2 sink (14.1 Tg C y−1). In response to futureclimate change, the CH4 source and the CO2 sinkstrengthened, leading to an increasingly negativeperturbation of radiative forcing. The spatial patternsand temporal trends of the NME and NEP highlydepend on the RCP scenario. Climate-inducedchanges in themagnitude of theNMEwere statisticallysignificant for most of the QTP, while spatial changesin the NEP were only significantly apparent underRCP4.5 and RCP6.0. The instantaneous radiative

forcing impact is determined by persistent CO2

sequestration and recent (∼5 decades) CH4 emission.The cumulative GHG effect was a negative feedback(cooling) to the climate system at the end of the 21stcentury. Uncertainties in our model estimations canbe reduced by including more explicit information onwetland distribution and classification, and morereliable future climate scenarios. Additional observa-tional data from representative wetland ecosystemsshall be collected to improve future quantification ofthese carbon-basedGHGs.

Acknowledgments

This research is partially supported by funding to Q Zthrough an NSF project (DEB- #0919331), the NASALand Use and Land Cover Change program (NASA-NNX09AI26G), the Department of Energy (DE-FG02-08ER64599), and the NSF Division of Informa-tion & Intelligent Systems (NSF-1028291). Jin-ShengHe and Weiming Song are supported by the NationalBasic Research Program of China (grant number2014CB954004) and the National Natural ScienceFoundation of China (grant numbers 31270481 and31025005).

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