ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 35, JUNE 2018, 659–670

• Original Paper •

Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM

Jiawen ZHU∗1, Xiaodong ZENG1,2, Minghua ZHANG1,3, Yongjiu DAI4, Duoying JI5,Fang LI1, Qian ZHANG5, He ZHANG1, and Xiang SONG1

1International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,

Chinese Academy of Sciences, Beijing 100029, China2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,

Nanjing University of Information Science and Technology, Nanjing 210044, China3School of Marine and Atmospheric Sciences, Stony Brook University, NY 11790, USA4School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275, China

5College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

(Received 21 June 2017; revised 18 October 2017; accepted 15 November 2017)

ABSTRACT

In the past several decades, dynamic global vegetation models (DGVMs) have been the most widely used and appropriatetool at the global scale to investigate vegetation–climate interactions. At the Institute of Atmospheric Physics, a new versionof DGVM (IAP-DGVM) has been developed and coupled to the Common Land Model (CoLM) within the framework ofthe Chinese Academy of Sciences’ Earth System Model (CAS-ESM). This work reports the performance of IAP-DGVMthrough comparisons with that of the default DGVM of CoLM (CoLM-DGVM) and observations. With respect to CoLM-DGVM, IAP-DGVM simulated fewer tropical trees, more “needleleaf evergreen boreal tree” and “broadleaf deciduous borealshrub”, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM,including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxesthan CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production inIAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbonemissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area indexsimulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values abouttwice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as wellas for other model communities in terms of a comparative benchmark.

Key words: vegetation dynamics, dynamic global vegetation model, vegetation distribution, carbon flux, leaf area index

Citation: Zhu, J. W., and Coauthors, 2018: Evaluation of the new dynamic global vegetation model in CAS-ESM. Adv.Atmos. Sci., 35(6), 659–670, https://doi.org/10.1007/s00376-017-7154-7.

1. IntroductionLand vegetation plays a pivotal role in regulating the ex-

change of heat, water, and carbon fluxes between the landsurface and the atmosphere (Li and Xue, 2010; Xue et al.,2010). The seasonal growth of vegetation significantly im-pacts surface latent heat, downward and reflected solar radi-ation (Zhu and Zeng, 2015, 2017), and the interannual vari-ability of vegetation can significantly regulate surface energybudgets via evapotranspiration (Guillevic et al., 2002; Zhuand Zeng, 2016). Vegetation dynamics are considered to beas important for climate as atmospheric dynamics, ocean cir-culation (Pielke et al., 1998), and have gained much attentionin recent years (Cramer et al., 2001; Seddon et al., 2016).

∗ Corresponding author: Jiawen ZHUEmail: zhujw@mail.iap.ac.cn

In the past several decades, rapid climate change has re-sulted in considerable changes in terrestrial ecosystems. Onewidespread dynamic change is the so-called “greening” ofarctic ecosystems—a feature considered to be majorly con-tributed by boreal shrubs (Fraser et al., 2011; Myers-Smith etal., 2011). Both observation- and model-based studies haveshown an increase in shrub biomass, coverage and abun-dance in arctic ecosystems in recent decades (Myers-Smithet al., 2011). Another key region is the tropics, which isecologically sensitive to climate variability and shows am-plified responses compared to other regions (Seddon et al.,2016). Many tropical ecosystems are more sensitive thanother regions to environmental perturbations and external dis-turbances, and are highly likely to cross a threshold to an al-ternative state (Holling, 1973; Scheffer et al., 2009).

These vegetation changes can in turn significantly affectthe climate. Over high latitudes, higher shrub abundance

© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

660 EVALUATION OF IAP-DGVM IN CAS-ESM VOLUME 35

warms the winter soil temperature by trapping more snowvia their branches (Sturm et al., 2001). During early spring,taller shrubs that expand into arctic tundra ecosystems alsotend to systematically warm the soil because of their loweralbedo than snow (Bonfils et al., 2012). In boreal summer,however, the shading imposed by shrubs is known to reducethe soil temperature and consequently the thickness of the ac-tive layer (Blok et al., 2010). Many studies have also reportedthat shrub canopies can alter arctic nutrient cycling, biodiver-sity and ecosystem services (Myers-Smith et al., 2011). Con-versely, in the tropics, the complete deforestation of Amazo-nia may result in warmer, drier conditions at the local scaleand lead to extratropical changes in temperature and rain-fall through teleconnections (Lawrence and Vandecar, 2015).Reduced plant cover in the Sahara, meanwhile, may causea decline in precipitation because of the resultant increasein albedo (Charney et al., 1975), and the vegetation dynam-ics of West Africa have been shown to play a crucial role inregulating present-day climate, and probably future climate,via influences on precipitation, evapotranspiration and energybalance (Erfanian et al., 2016; Yu et al., 2016).

At present, Dynamic Global Vegetation Models (DGVMs)are the best available tool to represent vegetation dynamics atthe global scale (Quillet et al., 2010). They can simulate tran-sient and potential responses of vegetation to past and futureclimate change via parameterizing physical and biogeochem-ical processes of vegetation (Peng, 2000). With the help ofDGVMs, it is possible for global climate models (GCMs)to include the bidirectional interactions between vegetationand climate (Quillet et al., 2010). Therefore, the couplingof DGVMs and GCMs is an important approach to assessthe influences of climate change on vegetation dynamics andtheir feedbacks to climate change.

However, there are still many uncertainties in reproduc-ing and forecasting vegetation dynamics with DGVMs be-cause of the complicated physical and biogeochemical pro-cesses. In response to climate change, most DGVMs simu-late an increase in woody coverage over high latitudes, whileothers suggest a gain in herbaceous vegetation or no changes(Falloon et al., 2012). In terms of the Amazon ecosystem,the predictions from different models also vary widely, whichis dominated by the differences in large-scale forest diebackand forest resilience (Betts et al., 2004; Friedlingstein et al.,2006; Baker et al., 2008; Restrepo-Coupe et al., 2017). Fal-loon et al. (2012) suggested that these responses of DGVMsto climate change are strongly associated with their simula-tion of present-day vegetation cover. Therefore, an importantstep to reduce these uncertainties in DGVMs is to systemati-cally evaluate and understand their present-day performance,which is the main focus of the present study.

CoLM-DGVM is the default DGVM of the CommonLand Model (CoLM). It was developed from the Lund–Potsdam–Jena DGVM (LPJ-DGVM; Sitch et al., 2003), andcombined an early version of a temperate shrub sub-model ofthe DGVM developed at the Institute of Atmospheric Physics(IAP-DGVM; Zeng et al., 2008). In recent years, a borealshrub sub-model, a process-based fire parameterization and

a new establishment parameterization scheme have been fur-ther developed in IAP-DGVM. These developments have re-sulted in considerably improved reproductions of the present-day vegetation distribution and carbon cycle by IAP-DGVM(Zeng et al., 2014). At present, IAP-DGVM has been cou-pled to CoLM, and both are important components of the cur-rent version of the Chinese Academy of Sciences’ Earth Sys-tem Model (CAS-ESM). This work assesses the performanceof IAP-DGVM, through comparison with that of CoLM-DGVM, within the framework of CAS-ESM, which is a nec-essary step to using CAS-ESM for investigating vegetation–climate interactions. In the next section, the model, experi-mental design and observational data are described. The re-sults are presented and discussed in section 3, followed byconclusions in section 4.

2. Model description, experimental design andobservational data

2.1. Model descriptionIAP-DGVM is a DGVM developed at the Institute of At-

mospheric Physics, Chinese Academy of Sciences. It orig-inates from LPJ-DGVM (Sitch et al., 2003) and the Com-munity Land Model’s DGVM (CLM-DGVM; Levis et al.,2004). Plants in IAP-DGVM are classified into 14 plant func-tional types (PFTs) (Table 1), of which eight are trees, threeare shrubs and three are grasses. These PFTs are definedby their physical, phylogenetic and phenological parameters,and are assigned bioclimatic limits to determine their estab-lishment and survival. At present, crops are not simulated inIAP-DGVM. More details about the model can be found inZeng et al. (2014).

In recent years, IAP-DGVM has undergone several ma-jor developments. These include the following: (1) A shrubsub-model was established (Zeng et al., 2008; Zeng, 2010).With this sub-model, IAP-DGVM can reproduce the globaldistribution of temperate and boreal shrubs realistically, anddistinguish shrubs from grasses effectively (Zeng et al., 2008;Zeng, 2010). (2) A process-based fire parameterization of

Table 1. Plant functional types (PFTs) in IAP-DGVM and their cor-responding abbreviations in this paper.

PFT Abbreviation

Needleleaf evergreen temperate tree NEMNeedleleaf evergreen boreal tree NEBNeedleleaf deciduous boreal tree NDBBroadleaf evergreen tropical tree BETBroadleaf evergreen temperate tree BEMBroadleaf deciduous tropical tree BDTBroadleaf deciduous temperate tree BDMBroadleaf deciduous boreal tree BDBBroadleaf evergreen shrub BEshBroadleaf deciduous temperate shrub BDMshBroadleaf deciduous boreal shrub BDBshC3 arctic grass C3ArC3 non-arctic grass C3NAC4 grass C4

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intermediate complexity was introduced (Li et al., 2012),which comprises fire occurrence, fire spread and fire impact.This fire parameterization significantly improves simulationsof global fire, including burned area and fire carbon emis-sions (Li et al., 2013). The fire parameterization is now alsoadopted in the Community Earth System Model at the Na-tional Center for Atmospheric Research (NCAR) to inves-tigate the influences of fire on carbon balance in terrestrialecosystems, and on global land energy and water budgets (Liet al., 2014; Li et al., 2017; Li et al., 2017). (3) A new es-tablishment parameterization scheme was developed (Song etal., 2016). This scheme significantly improves IAP-DGVM’ssimulation of vegetation density by introducing soil water asan impact factor. These improvements, together with otheroptimized modifications, contribute to a better performanceof IAP-DGVM in reproducing the present-day vegetation dis-tribution and carbon cycle.

The land surface model used in this study is CoLM. Start-ing from the code of the NCAR’s Land Surface Model (Bo-nan, 1996), Dai et al. (2003) developed the first version ofCoLM, combining the codes of the Biosphere–AtmosphereTransfer Scheme (Dickinson et al., 1993) and the IAP’s landmodel (Dai and Zeng, 1997). Since then, CoLM has beencontinually improved at Beijing Normal University in manyaspects and has been adopted as the land component of theBeijing Normal University Earth System Model (Ji et al.,2014).

2.2. Experimental designTwo global simulations were conducted within the frame-

work of CAS-ESM. One, which coupled CoLM and CoLM-DGVM, is the control (CTL) experiment, while the other,which coupled CoLM and IAP-DGVM, we refer to as IAP.Both simulations were spun up from bare ground for in ex-cess of 1200 years. Then, a further 660 years were run to

approach an equilibrium state through cycling the 33-year(1972–2004) atmospheric forcing data of Qian et al. (2006),with a T85 resolution (128×256 grid cells). The relative hu-midity and associated fire parameters needed for the IAP sim-ulation were derived from Li et al. (2012) and fixed in 2004.The atmospheric CO2 was set to 365 ppmv over all simulatedyears.

2.3. Observational dataThis paper focuses on the vegetation distribution, carbon

cycle and leaf area index (LAI). The observed vegetationdistribution and LAI came from CLM4 surface data, whichwere themselves derived from Moderate Resolution ImagingSpectroradiometer (MODIS) measurements (Lawrence andChase, 2007). The benchmarks for gross primary produc-tivity (GPP), net primary production (NPP) and fire carbonemissions were from Beer et al. (2010), MODIS (Zhao andRunning, 2010) and version 4 of the Global Fire EmissionsDatabase (GFEDv4; Randerson et al., 2015), respectively.The average of the last five years (2000–2004) of the IAPsimulation was compared with that of CTL and these bench-marks. Furthermore, to reduce the impacts of crops, the veg-etation cover in each grid cell was weighted by a factor of(100%-FCcrop), where FCcrop is the fraction of crop cover-age in the CLM4 surface dataset (Zeng et al., 2014).

3. Results and discussion3.1. Vegetation distribution

In general, IAP simulated more realistic distributions forthe four aggregated vegetation types (trees, shrubs, grassesand bare soil) than CTL. Over most latitudes, trees simulatedby IAP were in better agreement with the observation, rela-tive to CTL, which produced more trees (Fig. 1a). In CTL,

Fig. 1. Zonal average fractional coverage (units: %) of (a) trees, (b) shrubs, (c) grasses and (d) bare groundin CTL (blue), IAP(red) and CLM4 surface data (OBS; black).

662 EVALUATION OF IAP-DGVM IN CAS-ESM VOLUME 35

trees were overestimated over northern high latitudes, south-eastern South America and Africa, with magnitudes of 50%(Fig. 2a). Moreover, there was a band over central Eurasiawhere CTL’s trees were underestimated by more than 50%.In contrast, IAP simulated fewer trees over the tropics andmore over central Eurasia than CTL, which resulted in a re-duction in IAP’s biases (Fig. 2b). Further investigation indi-cated that the new establishment scheme contributed most tothe reduced biases of tropical trees, especially over transitionzones (Song et al., 2016).

In terms of shrubs, both CTL and IAP underestimatedthem over arctic regions, such as northeastern Canada andthe northern coastline of Eurasia (Figs. 1b, 2c and d). Furthersensitivity experiments and analysis suggested that these un-

derestimated shrubs were limited by the minimum thresholdof growing degree days over 5◦C (GDD5) set by the model,which is 350. Shrubs were unable to establish because theannual GDD5 was smaller than 350. Over northern high lat-itudes, shrubs could not grow and were severely underesti-mated in the CTL simulation, while IAP simulated a moresimilar shrub pattern than CTL with that observed (Fig. 1b).This improvement can be attributed to the boreal shrub sub-model of IAP-DGVM, which can distinguish shrubs fromgrasses effectively (Zeng et al., 2008; Zeng, 2010). However,IAP simulated more shrubs than CTL over northern middlelatitudes, such as western North America and central Eura-sia, which further increases the biases of CAS-ESM. More-over, both CTL and IAP underestimated the shrub coverage

Fig. 2. Differences in fractional coverage (units: %) of (a) trees, (c) shrubs, (e) grasses and (g) bare ground betweenCTL and observations. (b, d, f, h) as in (a, c, e, g), respectively, but between IAP and observations.

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in the Southern Hemisphere, such as in Australia.IAP’s grasses also agreed better with the observation than

those of CTL (Fig. 1c). The grasses in CTL were largelyoverestimated over middle and high latitudes, such as north-eastern Canada, central North America, middle and easternRussia and central Eurasia, where the biases exceeded 50%.However, IAP reduced these deficiencies to around 10% overthese regions (Fig. 2f). Over the tropics, both CTL and IAPunderestimated grasses, although IAP’s biases were a littlesmaller than those of CTL. The main underestimation was insoutheastern South America and most regions of Africa.

Figure 1d shows that the bare soil simulated by CTL wasunderestimated over middle and high latitudes, and overesti-mated over the tropics, while IAP simulated more bare soilover almost all latitudes except southern middle latitudes.Over high latitudes, such as northeastern Canada, the under-estimated bare soil of CTL mainly resulted from its over-estimated grasses (Fig. 2g). IAP significantly reduced thefractional coverage of grasses over northeastern Canada (Fig.2f); however, other vegetation, such as boreal shrubs, did notgrow in this region (Fig. 2d). Consequently, the bare soilof IAP was overestimated over northern high latitudes (Fig.2h). In the tropics, both CTL and IAP simulated more baresoil than observations (Fig. 1d). These overestimations weremainly in northeastern Africa, southern Africa and Australiafor CTL, and most regions of Africa for IAP (Fig. 2h). Theunderestimated grasses were mostly responsible for these bi-ases of tropical bare soil.

To investigate the contribution of each PFT to the im-provements, Fig. 3 shows the global average fractional cov-erage for each PFT in the two simulations and the observa-tion. The total coverage of IAP’s trees (26.51%) was moreconsistent with that of the observation (28.51%) than that ofCTL (35.60%). The smaller fractional tree coverage in IAPwas mainly contributed to by a reduction in “broadleaf ev-

ergreen tropical tree” (BET; 3.17%), “broadleaf deciduoustropical tree” (BDT; 3.27%), “broadleaf deciduous temperatetree” (BDM; 2.10%) and “broadleaf deciduous boreal tree”(BDB; 2.10%). However, IAP’s BDT was less than halfthat of the observation, which was the major contributor tothe underestimation of IAP’s total tree coverage. Further in-vestigation indicated that the new establishment parameteri-zation of IAP-DGVM resulted in this underestimated BDT,mainly over tropical semi-arid regions (Fig. S1 in electronicsupplementary material). IAP also simulated 1.32% more“needleleaf evergreen boreal tree” (NEB) than CTL, whichcontributed to the band of increased tree coverage over cen-tral Eurasia apparent in Fig. 2b. In terms of shrubs, the in-creased “broadleaf deciduous boreal shrub” (BDBsh) cov-erage (5.44%) in IAP was the main contributor to its betteragreement with the observation than CTL. For grasses, al-though the total fraction in CTL was closer to that observedthan IAP’s grass fraction, the “C3 arctic grass” (C3Ar) cov-erage in CTL was 6.33% larger than the observation, whichcorresponds to the severely overestimated grasses over highlatitudes shown in Fig. 1c. The composition of IAP’s grasseswas generally in good agreement with the observation. How-ever, the “C4 grass” (C4) coverage in IAP was 4.64% lessthan the observation, resulting in the underestimation of to-tal grasses in IAP. The bare soil of CTL was close to thatobserved, but IAP simulated 7.67% more bare soil than theobservation. The overestimated bare soil in IAP mainly re-sulted from its underestimated shrubs in arctic regions andunderestimated grasses in the tropics (Fig. 2).

Figure 4 shows the global distribution of the dominantvegetation type, which is the PFT with the highest fractionalcoverage. Clearly, the dominant vegetation simulated by IAPwas more consistent with that obtained from the observationthan that of CTL. In CTL, C3Ar dominated over most north-ern high-latitude regions. However, IAP showed the northern

Fig. 3. Global weighted average fractional coverage (%) of each PFT for CTL (blue), IAP (red)and observation (black). The abbreviations correspond to the information in Table 1.

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Fig. 4. Global distribution of the dominant vegetation type obtained from (a) CTL, (b) IAP and(c) observation. The abbreviations correspond to the information in Table 1.

high latitudes to be dominated by bare soil, BDBsh and NEB,which was a similar situation to that observed. In the tropics,CTL overestimated regions dominated by BET, as comparedto observation. For example, BET was the dominant veg-etation in CTL over southeastern South America and WestAfrica, which, according to observation, are actually dom-inated by C4. With respect to CTL, IAP simulated fewerregions dominated by BET, which agreed well with obser-vation. Nonetheless, IAP’s C4 was also not the dominantvegetation in southeastern South America and West Africabecause of the underestimated C4 (Fig. 3).

3.2. Carbon fluxesCompared to CTL, IAP simulated an overall more reason-

able distribution of key carbon fluxes. Over most latitudes,

the GPP in IAP was similar to that of Beer et al. (2010), whileCTL’s GPP was overestimated in middle and high latitudes(Fig. 5a). The overestimated GPP in CTL stretched over thewhole of central and eastern North America, Europe, SouthAsia and southeastern South America, while underestimatedGPP dominated over the Amazon and Africa (Fig. 6a). Rela-tive to CTL, IAP’s GPP was closer to observation, especiallyover the Amazon (Fig. 6b). Both CTL and IAP underesti-mated GPP over most regions of Africa.

In terms of NPP, IAP showed a better agreement withMODIS than CTL over northern high latitudes (Fig. 5b). Rel-ative to CTL, IAP simulated lower NPP over northeasternCanada and central and eastern Russia, where CTL simu-lated ∼ 200 gC m−2 yr−1 more NPP than the observation (Fig.6c). Both CTL and IAP underestimated NPP over middle lat-

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Fig. 5. Zonal average (a) GPP, (b) NPP and (c) fire carbon emissions (FireC) in CTL (blue), IAP (red) and thebenchmarks (OBS; black). All units are gC m−2 yr−1.

itudes, mainly over arid and semi-arid regions. In the tropics,the simulated NPP in CTL was higher than the observation,mainly because of the overestimated NPP over the Amazon,West Africa and the Maritime Continent. On the contrary,IAP’s NPP was consistent with observations over the Ama-zon, Africa and the Maritime Continent. Both CTL and IAPunderestimated the NPP over most parts of Africa.

Fire carbon emissions were overestimated by IAP overmiddle latitudes, especially in central and western NorthAmerica, northeastern China, southern South America andAustralia (Fig. 6f). However, the fire carbon emissions sim-ulated by IAP were much more consistent with observationin the tropics, where CTL showed a severe underestimation(Fig. 5c). Fire carbon emissions in CTL were 10 gC m−2 yr−1

higher than the observation in eastern North America, Eu-rope, the Amazon, Southeast Asia and the Maritime Conti-nent, and 50 gC m−2 yr−1 lower in central South Americaand most regions of Africa (Fig. 6e). IAP reduced these er-rors to different degrees by increasing or decreasing fire car-bon emissions in these regions, respectively. Broadly, IAPcaptured a better spatial distribution of fire carbon emissionsthan CTL.

Overall, within the framework of CAS-ESM, IAP simu-lated carbon fluxes closer to observations than CTL, as sum-marized in Fig. 7. The GPP simulated by IAP was 150.5PgC yr−1, which is closer to the 123± 8 PgC yr−1 reportedby Beer et al. (2010) than CTL’s GPP, and comparable to therange from 101 to 150 PgC yr−1 published elsewhere (Far-quhar et al., 1993; Ciais et al., 1997). Meanwhile, IAP alsooverestimated autotrophic respiration, which was almost thesame as its counterpart in CTL. Consequently, IAP’s NPP,

59.1 PgC yr−1, compared better with the expected value of 60PgC yr−1 (Castillo et al., 2012) than the result of CTL (75.31PgC yr−1). Moreover, heterotrophic respiration in IAP was56.20 PgC yr−1, which is closer to the 55.4 PgC yr−1 fromIPCC (2013) than that of CTL (74.08 PgC yr−1). Thus, thenet ecosystem production in IAP was more reasonable thanthat in CTL, in comparison to the baseline from IPCC (2013).The fire carbon emissions in IAP were slightly higher thanthose of GFEDv4, because of the overestimated fire carbonemissions in the midlatitudes. Consequently, the net biomeproduction (NBP) of IAP-DGVM was −0.2 PgC yr−1, whichis outside the range of 2.63±1.22 PgC yr−1 reported by otherprocess-based terrestrial ecosystem models driven by risingCO2 and by changes in climate (IPCC, 2013). This near-zero value of NBP could be acceptable, however, becausethe results were based on the equilibrium state, which wascyclically forced by atmospheric datasets and a constant CO2value (Castillo et al., 2012).

3.3. LAIGenerally, both CTL and IAP overestimated LAI, al-

though the bias in IAP was smaller than that in CTL (Fig.8). The simulated annual mean LAI in CTL and IAP was 1.0m2 m−2 more than the observation over most of the northernmiddle and high latitudes, such as central and eastern NorthAmerica, Europe, central Eurasia and southeastern China. Inthe tropics, CTL’s bias in LAI exceeded 5.0 m2 m−2, whileIAP’s was ∼ 3.0 m2 m−2. In terms of seasonal variabil-ity, both CTL and IAP were consistent with observations,the largest being during June–August and the smallest dur-ing December–February. However, the simulated LAI ampli-

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Fig. 6. Differences between CTL and the benchmarks (CTL minus benchmarks) in (a) GPP, (c) NPP and (e) fire carbon emis-sions (FireC). (b, d, f) As in (a, c, e), respectively, but between IAP and the benchmarks. All units are gC m−2 yr−1.

tudes in CTL and IAP were around twice as large as thoseobserved for each month. Although the LAI values in IAPwere closer to observation compared to those of CTL, theimprovements were not remarkable. Therefore, it is neces-sary to further investigate the causes of these systematicallyoverestimated LAI values.

4. Conclusions

This work evaluated the performance of IAP-DGVMwithin the framework of CAS-ESM through comparisonwith that of CoLM-DGVM, as well as observations andbenchmarks. The results sufficiently demonstrated that IAP-DGVM can simulate a realistic vegetation distribution, in-cluding spatial patterns, total areas and compositions, as wellas reasonable carbon fluxes, such as GPP, NPP and fire car-bon emissions.

The total tree coverage of IAP-DGVM was found to bein good agreement with observations, because of the reducedtropical trees and increased NEB relative to CoLM-DGVM.The shrub coverage in IAP-DGVM also showed a similar dis-tribution to that observed, which resulted from the signifi-cantly increased fractional coverage of BDBsh, with replace-ment of C3Ar. Meanwhile, the reduced C3Ar was the majorcontributor to the better consistency between the grass cover-age in IAP-DGVM and that observed than between CoLM-DGVM and that observed. Consequently, the global distri-bution of the dominant vegetation type simulated by IAP-DGVM was similar to that observed, especially over north-ern high latitudes. Moreover, the biases of IAP-DGVM interms of GPP and NPP were smaller because of improve-ments in the GPP over middle and high latitudes, as well as inthe tropics. The tropical patterns of fire carbon emissions inIAP-DGVM were much more consistent than CTL with ob-servations. These better performances of IAP-DGVM in sim-

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Fig. 7. Global means of carbon fluxes in CTL and IAP, as well as that of thebenchmarks. Units: PgC yr−1.

Fig. 8. Differences in annual mean LAI between (a) CTL and observations, and (b) IAP and observations. (c)Globally averaged LAI in CTL (blue), IAP (red) and observations (black) for each month. All units are m2 m−2.

ulating the global vegetation distribution and carbon fluxesprovide a foundation to use CAS-ESM to study vegetation–climate interactions.

Despite the above positive results, deficiencies in IAP-DGVM were also found. Specifically, BDT was severely un-

derestimated because of the new establishment parameteriza-tion; BDBsh could not grow in northeastern Canada becausethe simulated GDD5 did not exceed the threshold; and the C4coverage was much smaller than the observation. Therefore,improved parameterization is necessary to simulate a more

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realistic distribution of vegetation types. Furthermore, bothsimulations overestimated GPP and autotrophic respiration,which is likely associated to the parameterization in CoLMbeing insufficiently sensitive to the DGVM. Additionally, thefire emissions simulated by the model in the high and mid-dle latitudes were high. An understanding of the underlyingmechanisms of these biases is needed to further improve themodel, which will be the subject of future research.

The primary purpose of this study was to evaluate theperformance of IAP-DGVM within the framework of CAS-ESM, which makes the evaluations more model specific.However, this work also exerts the following influences onother studies. This study reported the improvements and de-ficiencies of IAP-DGVM in CAS-ESM, which is valuable in-formation for the application of CAS-ESM, as well as a sam-ple for other model communities in terms of a comparativebenchmark. The selection of global observations of carbonfluxes in this study was limited by the spatial and temporalscale of existing datasets, which is a pivotal message for ob-servational scientists to observe carbon fluxes with large spa-tial scale and continuous time. Overall, we hope that the ad-vantages and disadvantages of the simulations reported in thispaper will prove valuable for scientists seeking to investigateclimate change.

Acknowledgements. This work was supported by the NationalMajor Research High Performance Computing Program of China(Grant No. 2016YFB02008) and the National Natural Science Foun-dation of China (Grant Number 41705070). Fang LI and XiangSONG are supported by the National Natural Science Foundationof China (Grant Numbers 41475099 and 41305096).

Electronic supplementary material: Supplementary materialis available in the online version of this article at https://doi.org/

10.1007/s00376-017-7154-7.

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