Geosci. Model De ., 10, 1321–1337, 2017
www.geosci-model-de .ne /10/1321/2017/
doi:10.5194/gmd-10-1321-2017
© Au ho (s) 2017. CC A ibu ion 3.0 License.
Da a-mining analysis o he global dis ibu ion o soil ca bon in
obse a ional da abases and Ea h sys em models
Shoji Hashimo o1, Kazuki Nanko1, Bo is ˇ
Tupek2, and Aleksi Leh onen2
1Fo es y and Fo es P oduc s Resea ch Ins i u e (FFPRI), Tsukuba, Japan
2Na u al Resou ces Ins i u e Finland, La oka anonkaa i 9, Helsinki, Finland
Co espondence o: Shoji Hashimo o (shojih@ p i.a c.go.jp)
Recei ed: 27 May 2016 – Discussion s a ed: 30 June 2016
Re ised: 8 Feb ua y 2017 – Accep ed: 28 Feb ua y 2017 – Published: 28 Ma ch 2017
Abs ac . Fu u e clima e change will d ama ically change
he ca bon balance in he soil, and his change will a ec
he e es ial ca bon s ock and he clima e i sel . Ea h sys-
em models (ESMs) a e used o unde s and he cu en cli-
ma e and o p ojec u u e clima e condi ions, bu he soil
o ganic ca bon (SOC) s ock simula ed by ESMs and hose
o obse a ional da abases a e no well co ela ed when he
wo a e compa ed a ine g id scales. Howe e , he speci ic
key p ocesses and ac o s, as well as he ela ionships among
hese ac o s ha go e n he SOC s ock, emain unclea ; he
inclusion o such missing in o ma ion would imp o e he
ag eemen be ween modeled and obse a ional da a. In his
s udy, we sough o iden i y he in luen ial ac o s ha go -
e n global SOC dis ibu ion in obse a ional da abases, as
well as hose simula ed by ESMs. We used a da a-mining
(machine-lea ning) (boos ed eg ession ees – BRT) scheme
o iden i y he ac o s a ec ing he SOC s ock. We applied
BRT scheme o h ee obse a ional da abases and 15 ESM
ou pu s om he i h phase o he Coupled Model In e com-
pa ison P ojec (CMIP5) and examined he e ec s o 13 a i-
ables/ ac o s ca ego ized in o i e g oups (clima e, soil p op-
e y, opog aphy, ege a ion, and land-use his o y). Globally,
he con ibu ions o mean annual empe a u e, clay con en ,
ca bon- o-ni ogen (CN) a io, we land a io, and land co e
we e high in obse a ional da abases, whe eas he con ibu-
ions o he mean annual empe a u e, land co e , and ne
p ima y p oduc i i y (NPP) we e p edominan in he SOC
dis ibu ion in ESMs. A compa ison o he in luen ial ac-
o s a a global scale e ealed ha he mos dis inc di e -
ences be ween he SOCs om he obse a ional da abases
and ESMs we e he low clay con en and CN a io con ibu-
ions, and he high NPP con ibu ion in he ESMs. The esul s
o his s udy will aid in iden i ying he causes o he cu en
misma ches be ween obse a ional SOC da abases and ESM
ou pu s and imp o e he modeling o e es ial ca bon dy-
namics in ESMs. This s udy also e eals how a da a-mining
algo i hm can be used o assess model ou pu s.
1 In oduc ion
Soil is he la ges o ganic ca bon s ock in e es ial ecosys-
ems (Ba jes, 1996; IPCC, 2013; Köchy e al., 2015). The soil
o ganic ca bon (SOC) s ock ep esen s a balance be ween
ca bon inpu s o soil and ca bon losses om soil ia decom-
posi ion and dissol ed o ganic ca bon, and his in lux and
e lux o soil ca bon is con olled di ec ly and indi ec ly by
en i onmen al condi ions (Ca alhais e al., 2014; Schimel
e al., 1994). Fu u e clima e change will d ama ically a ec
he global soil ca bon balance (Bond-Lambe y and Thom-
son, 2010; C ow he e al., 2016; F iedlings ein e al., 2006;
Hashimo o e al., 2011, 2015), and his change will a ec e -
es ial ca bon and consequen ly he clima e i sel (Cox e al.,
2000; Zaehle, 2013).
Ea h sys em models (ESMs) we e de eloped o unde -
s and he cu en clima e and p o ide u u e clima e p ojec-
ions, and hese models inco po a e he e es ial ca bon cy-
cle, including SOC (A o a e al., 2013; F iedlings ein e al.,
2014). In ecosys em ca bon cycle models o ESMs, SOC is
calcula ed as he balance be ween ca bon inpu s ia dead o -
ganic ma e and ca bon emissions ia o ganic ma e decom-
posi ion, wi h bo h p ocesses in luenced by empe a u e and
wa e condi ions. SOC dynamics ha e a c i ical in luence on
Published by Cope nicus Publica ions on behal o he Eu opean Geosciences Union.
1322 S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon
he land ca bon sink in ESM simula ions (F iedlings ein e
al., 2014).
Obse a ional global soil da abases a e o en used as
benchma ks o examine whe he ESMs success ully desc ibe
he global dis ibu ion o soil ca bon s ocks (Ana e al.,
2013; Ha a uk e al., 2014; Todd-B own e al., 2013; Wiede
e al., 2014). Se e al global soil da abases ha e been de-
eloped o e he pas wo decades, and se e al a e un-
de going u he imp o emen (Scha lemann e al., 2014).
Ce ain da abases desc ibe he global dis ibu ion o soil
physiochemical p ope ies and enable calcula ions o he
global dis ibu ion o SOC s ocks (e.g., Ha monized Wo ld
Soil Da abase – HWSD), whe eas o he s p o ide SOC
s ocks by de aul (e.g., In e na ional Geosphe e Biosphe e
P og amme’s (IGBP) Da a and In o ma ion Sys em (DIS)
da abase). These da abases inco po a e obse ed da a poin s
wi h global co e age, al hough he e a e biases in he spa ial
dis ibu ion o densi y o he da a poin s. In hese da abases,
g idded SOC da a ha e been gene a ed by linking he soil
p ope ies o soil maps o by in e -ex apola ing he model
ou pu s de i ed om analyses o obse ed SOC da a poin s.
Compa ed wi h he SOC dis ibu ion de i ed om ESMs,
SOC es ima es de i ed om SOC obse a ions a e mo e da a
o ien ed; howe e , e en he obse a ional da abases include
signi ican unce ain y because o e o s in he sou ce da a
and building p ocesses (Köchy e al., 2015; Todd-B own e
al., 2013).
A ecen s udy (Todd-B own e al., 2013) ound ha al-
hough ESM esul s a e mode a ely consis en a he biome
le el, he co ela ion be ween he dis ibu ion o soil ca bon
s ocks simula ed by ESMs and obse a ional da abases is
poo when he wo a e compa ed a ine scales (e.g., a 1◦
scale). Fu he mo e, es ima es o SOC by ESMs and e es-
ial biosphe e models exhibi high unce ain y (Nishina e
al., 2014, 2015; Tian e al., 2015). Se e al s udies ha e ex-
amined he cause o he inconsis ency in da a de i ed by
obse a ional da abases and ESMs, and he high a ia ion
o SOC ou pu s om ESMs (Exb aya e al., 2013; Todd-
B own e al., 2013; Wiede e al., 2013). Todd-B own e
al. (2013) analyzed he soil ca bon ou pu s om 11 ESMs
om he i h phase o he Coupled Model In e compa ison
P ojec (CMIP5) and soil ca bon da a om he HWSD, and
ound ha ne p ima y p oduc i i y (NPP) and empe a u e
could explain he SOC spa ial a ia ions in he ESM ou -
pu bu no in he HWSD ou pu . These au ho s also ound
ha he di e ences in SOC om he ESMs we e d i en by
di e ences in he simula ed NPP and he pa ame e iza ion
o soil he e o ophic espi a ion and no by di e ences in
he soil model s uc u e o he ESMs. The key in luence o
pa ame e izing soil he e o ophic espi a ion (e.g., u no e
ime) on SOC in he CMIP5 ESM has also been discussed by
Exb aya e al. (2013). Ana e al. (2013) examined he ela-
ionships be ween simula ed SOC and ege a ion ca bon and
compa ed hem wi h e e ence da a ob ained om obse a-
ional da abases, and hey ound ha al hough simula ed al-
ues clus e ed a ound he e e ence alues, he a io o SOC
o ege a ion ca bon di e ed among he models. This ind-
ing sugges s ha he pa ame e iza ion o plan p oduc ion,
mo ali y, and decomposi ion a y g ea ly among ESMs.
Mo e ealis ic ep esen a ions o u no e imes (Ko en e
al., 2015) and ocusing on he model ea men o he hy-
d ological cycle on he ca bon cycle (Shao e al., 2013) a e
sugges ed as u u e imp o emen s.
Despi e hese esea ch esul s, he key p ocesses and ac-
o s ha go e n he SOC s ock and he ela ionships among
hem emain unclea . The app op ia e inclusion o hese p o-
cesses/ ac o s would imp o e he consis ency be ween he
model esul s and obse a ional da a. In his s udy, we sough
o iden i y he key ac o s ha go e n he global SOC dis-
ibu ion in obse a ional da abases as well as hose simu-
la ed by ESMs. We applied a da a-mining (machine-lea ning)
scheme (boos ed eg ession ees – BRT) o iden i y he in-
luen ial ac o s and explo e how hey ela e o SOC s ocks
(Eli h e al., 2008). The BRT me hod is based on eg es-
sion ees and boos ing. We combined he po en ially in-
luen ial a iables om many da a p oduc s and SOC da a
om obse a ional da abases and ESMs, and examined he
ac o s in luencing he dis ibu ion o SOC and he ela ion-
ships be ween hese ac o s and SOC s ocks. We assessed
how closely ESMs could ma ch he in luen ial ac o s and
hei ela ionships wi h ac o s ob ained om obse a ional
da abases. By compa ing he in luen ial ac o s in he obse -
a ional da abases wi h hose in he ESMs, we cla i ied he
model–da a disc epancies and he a eas in which ESMs can
be imp o ed.
2 Ma e ials and me hods
2.1 Obse a ional global SOC da abase
We used SOC da a om wo global da abases and one no h-
e n obse a ional da abase. The i s global da abase was
he HWSD (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012). The
HWSD is a global da abase o soil physiochemical p ope -
ies ha has been de eloped by he In e na ional Ins i u e o
Applied Sys ems Analysis (IIASA) and he Food and Ag i-
cul u e O ganiza ion o he Uni ed Na ions (FAO) in collab-
o a ion wi h he In e na ional Soil Re e ence and In o ma-
ion Cen e (ISRIC) Wo ld Soil In o ma ion, he Eu opean
Commission Join Resea ch Cen e (JRC), and he Ins i u e
o Soil Science, Chinese Academy o Sciences (ISSCAS).
The da abase was cons uc ed by compiling he Eu opean
Soil Da abase (ESDB), a 1 :1 million soil map o China, a -
ious egional SOTER da abases (SOTWIS da abase), and a
soil map o he wo ld om he FAO. We used an SOC s ock
da abase ob ained wi h HWSD om he Join Resea ch Cen-
e (JRC) (Hiede e and Köchy, 2011) (Fig. 1a). The second
da abase included global g idded su aces o selec ed soil
cha ac e is ics (IGBP-DIS) (Global Soil Da a Task G oup,
Geosci. Model De ., 10, 1321–1337, 2017 www.geosci-model-de .ne /10/1321/2017/
S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon 1323
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(a) HWSD
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(b) IGBP−DIS
180˚ 90˚ W 0˚ 90˚ E 180˚
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0 20406080100
kg C m−2
(c) NCSCD
Figu e 1. Soil ca bon s ock in he uppe 100 cm (kg C m−2) om
he obse a ional da abases (HWSD, IGBP-DIS, and NCSCD).
2000) (Fig. 1b), which con ains g idded soil physiochemical
p ope ies. The da abase has been de eloped by he Global
Soil Da a Task G oup o he In e na ional Geosphe e Bio-
sphe e P og amme’s (IGBP) Da a and In o ma ion Sys em
(DIS), and he da abase was gene a ed by linking he pedon
eco ds in he Global Pedon Da abase o he FAO/UNESCO
digi al soil map o he wo ld. The hi d da abase was he
No he n Ci cumpola Soil Ca bon Da abase, e sion 2 (NC-
SCD) (Hugelius e al., 2013; Ta nocai e al., 2009) (Fig. 1c).
This da abase is a spa ial da abase o SOC s ock o he no h-
e n ci cumpola pe ma os egion. The soil map da a we e
ob ained om di e en egions/coun ies (USA, Canada,
Russia, e c.) and we e ha monized. The NCSCD we e based
on 1778 pedon da a poin s.
We used he HWSD and IGBP-DIS o analyze he global
dis ibu ion o SOC s ocks; hen, we ex ac ed a da abase o
no he n ci cumpola egions om he h ee abo e da abases
and analyzed he SOC s ocks in he no he n egion. The e-
la ionships among he da abases a e shown in Fig. S1 in he
Supplemen . The SOC in he uppe 100 cm in each da abase
was used.
2.2 Global SOC es ima ed using Ea h sys em models
The global dis ibu ion o SOC s ocks es ima ed by ESMs
was ob ained om CMIP5. We examined he esul s o 15
ESMs (Fig. 2; Table 2). When mo e han one esul was ob-
ained by he same model amily (e.g., MIROC-ESM and
MIROC-ESM-CHEM), we gene a ed an ensemble a e age
da abase o each amily (e.g., a e age o MIROC-ESM and
MIROC-ESM-CHEM): Todd-B own e al. (2013) showed
h ough a hie a chical clus e analysis ha SOC dis ibu ions
we e e y simila among ESMs om he same clima e cen-
e . The mean alues om 1980–2004 we e calcula ed. The
esul s o he his o ical and ensemble membe 1i1p1 we e
used in his s udy. The no a ion “ 1i1p1” is an iden i ie o
he model simula ion and is an ensemble membe ha is o en
used o analyses (Chang e al., 2012; Di meye e al., 2013;
Jiang e al., 2015; Kuma e al., 2014). The o e iews o
SOC submodels in he ESMs ha e been p e iously desc ibed
(Exb aya e al., 2014; Todd-B own e al., 2013, 2014) and
a e also shown in Table 2. In gene al, each soil submodel
consis ed o one o nine pools and inco po a ed he e ec s
o empe a u e and mois u e. Some ESMs ha e li e ca bon
pools; hese we e excluded om his s udy. A compa ison be-
ween he mean o ESMs and global obse a ional da abases
in a 1◦g id is shown in Fig. S2.
2.3 O he da abases
We used i e g oups o a iables/ ac o s o examine hei e -
ec s on global SOC: clima e, soil p ope y, opog aphy, eg-
e a ion, and land-use his o y. De ailed da a sou ces o he
da abases a e desc ibed in Table 1. The mean annual em-
pe a u e and annual p ecipi a ion we e used as he clima e
a iables, and he clay con en , ca bon- o-ni ogen (CN) a-
io, and ex u e (Appendix Table A1) we e used as he soil
a iables (0–30 cm). The compound opog aphic index, ele-
a ion, slope, and we land a io we e used as he opog aphic
indices. The CN a io was calcula ed by di iding he ca -
bon densi y by he ni ogen densi y. The we land a io was
calcula ed by di iding he numbe o we land g ids a 30 s
by he o al g ids a 1◦. The lake, ese oi , and i e we e
no quan i ied as we lands and we e excluded om he o-
al g ids. The land co e ype (Appendix Table A2) and NPP
we e adop ed as ege a ion indices, and he c opland a io
and human app op ia ion o ne p ima y p oduc ion pe cen -
age, which is a pe cen age o human consump ion o NPP o
local NPP (Imho and Bounoua, 2006), we e used as he in-
dices o land-use his o y. The a e age human app op ia ion
o he NPP pe cen age was calcula ed a 1◦. His og ams o
he a iables a e shown in Fig. S3.
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1324 S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon
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(a) BCC−ensemble (b) BNU−ESM (c) CanESM2
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(d) CCSM4 (e) CESM1−ensemble ( ) CMCC−CESM
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(g) GFDL−ESM2M (h) GISS−ensemble (i) HadGEM2−CC
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(j) INM−CM4 (k) IPSL−ensemble (l) MIROC−ensemble
180˚ 90˚ W 0˚ 90˚ E 180˚
60˚ S
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(m) MPI−ensemble
180˚ 90˚ W 0˚ 90˚ E 180˚
0 20406080100
kg C m−2
(n) MRI−ESM1
180˚ 90˚ W 0˚ 90˚ E 180˚
(o) No ESM1−ensemble
Figu e 2. Soil ca bon s ocks (kg C m−2) om Ea h sys em models (CMIP5). The e m “ensemble” indica es he esul o an ensemble o
amily membe s.
2.4 Da abase handling
All global da abases, excep o he da abases wi h a spa-
ial esolu ion o 1◦by de aul , including obse a ional and
ESM model ou pu s, we e eg idded o a spa ial esolu ion o
1◦ o he analyses. Reg idding o da a in he Ne CDF o -
ma was pe o med using he Clima e Da a Ope a o s (CDO)
so wa e, e sion 1.6.9, p o ided by he Max Plank Ins i u e
o Me eo ology (h ps://code.zmaw.de/p ojec s/cdo). A bi-
linea in e pola ion, which is one o he mos widely used
algo i hms, was used ( emapbil in CDO).
2.5 Boos ed eg ession ees (BRT) scheme
To iden i y he in luen ial ac o s and hei ela ionships wi h
SOC s ocks, BRT scheme was used in his s udy (Eli h e
al., 2008). This echnique in ol es a da a-mining (machine-
lea ning) algo i hm ha combines he ad an ages o a eg es-
sion ee (decision ee) algo i hm and boos ing. Reg ession
ees a e a classi ica ion algo i hm ha classi y da a h ough
ecu si e bina y spli s, and boos ing is a machine-lea ning
algo i hm ha gene a es many ough models and combines
hem o imp o e hei p edic i e capabili y. The main ad an-
ages o his me hod a e ha he BRT scheme can analyze di -
e en ypes o a iables and in e ac ion e ec s among a i-
ables, and is applicable o nonlinea ela ionships. In ecen
yea s, he BRT echnique has been used o examine he dis-
ibu ion o soil cha ac e is ics a a egional scale (Ae sen e
al., 2011; Cools e al., 2014; Ma in e al., 2011). Majo ou -
pu s om BRT analyses can iden i y he ollowing: (1) he
ela i e impo ance (pe cen age o in luence o con ibu ion)
o p edic o a iables (explana o y a iables) on he basis o
he weigh ed and scaled numbe o imes a a iable is se-
lec ed o spli ing (Eli h e al., 2008) and (2) he ela ion-
ships among a iables and he explained a iable shown in
pa ial dependence plo s.
Geosci. Model De ., 10, 1321–1337, 2017 www.geosci-model-de .ne /10/1321/2017/
S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon 1325
Table 1. Va iables used in he analyses and hei sou ces.
Va iable Abb e ia ion Sou ce (da abase) O iginal esolu ion Re e ence
Mean annual empe a u e1MAT ISLSCPII (CRU05) 1◦New e al. (2011)
Mean annual p ecipi a ion1MAP ISLSCPII (CRU05) 1◦New e al. (2011)
Clay con en (0–30 cm) Clay ISLSCPII 1◦Scholes and B own de Cols oun (2011)
CN a io (0–30 cm)2CN a io ISLSCPII 1◦Scholes and B own de Cols oun (2011)
Soil ex u e (0–30 cm) Tex u e ISLSCPII 1◦Scholes and B own de Cols oun (2011)
Compound opog aphic index3CTI ISLSCPII 1◦Ve din (2011)
Ele a ion3Ele . ISLSCPII 1◦Ve din (2011)
Slope3Slope ISLSCPII 1◦Ve din (2011)
We land a io We land Global Lakes and We lands Da abase 30 s Lehne and Döll (2004)
Land co e LandCo e ISLSCPII 1◦F iedl e al. (2010)
Ne p ima y p oduc ion NPP ISLSCPII 1◦P ince and Zheng (2011)
C opland a io C opland ISLSCPII 1◦Ramanku y and Foley (2010)
Human app op ia ion o NPP pe cen age HANPPpc HANPP collec ion 0.25◦Imho e al. (2004)
1The o iginal da abase p o ides mon hly da a. Annual means we e calcula ed by he au ho s. 2The CN a io was calcula ed by di iding he ca bon densi y by he ni ogen densi y. 3The na i e da abase is hyd o1k,
and i s esolu ion is 1 km. The mean alue o 1 km was used in his s udy.
We used he open-sou ce BRT package (b . unc ions.R)
in R so wa e e sions 3.2.1 and 3.2.2 (R Co e eam, 2013)
de eloped by Eli h e al. (2008). The R code o he BRT al-
go i hm is a ailable in he supplemen a y ma e ial o Eli h
e al. (2008). The gbm package was used ( e sion 2.1.1) o
un he BRT package. The calcula ions we e pe o med in
Mac OS X ( e sion 10.9.5 and e sion 10.10.5). To do so,
he “windows” unc ion in he “b . unc ions.R” needed o
be eplaced wi h he “qua z” unc ion in R. In p ac ice, h ee
pa ame e s in he BRT package – he lea ning a e (l ), ee
complexi y ( c), and bag ac ion (bg) – con ol he BRT pe -
o mance. The l de e mines he con ibu ion o each ee,
he c con ols he numbe o spli s, and he bg is he p opo -
ion o da a selec ed a each s ep. The numbe o ees was
de e mined using he c oss- alida ion me hod in he R pack-
age. The maximum numbe o ees was se o 15 000. The c
alue was se o 5. We es ed di e en l (0.001, 0.005, 0.01,
0.05, 0.1) and bg alues (0.5, 0.6, 0.7) and used he bes pa-
ame e se o each da abase, bu he changes in pa ame e
alues had li le e ec on he model pe o mance.
2.6 Model pe o mance
The goodness o i be ween he BRT model and da a was
assessed by using he linea ela ionship be ween he p e-
dic ed and obse ed alues, he coe icien o de e mina ion
(R2), and he oo mean squa e e o (RMSE); i is shown
in Tables S2 and S3 in he Supplemen . Fo bo h he obse -
a ional da abases and ESM da abases, he BRT models ex-
hibi ed good pe o mance, wi h high R2 alues in mos o
he da abases, bu he pe o mance was ela i ely lowe o
NCSCD and CMCC (no he n soils).
3 Resul s
3.1 Obse a ional da abases
3.1.1 Global soil
The ela i e con ibu ions o a iables in he BRT model o
global SOC s ocks o he obse a ional da abases a e shown
in Fig. 3a and b. In HWSD, he con ibu ions o land co e ,
mean annual empe a u e, CN a io, and we land a io we e
high. Fo IGBP-DIS, he mean annual empe a u e, ollowed
by clay con en , CN a io, and land co e also highly con-
ibu ed. In pa icula , he mean annual empe a u e was e y
in luen ial. The con ibu ion o ele a ion o each HWSD and
IGBP-DIS was 6 and 7 %, espec i ely. The NPP con ibu ed
5 % in bo h da abases.
The ela ionships be ween he in luen ial a iables and
SOC a e shown in Fig. 4a–e. In gene al, he wo da abases
showed simila ela ionships. Fo example, he SOC de-
c eased wi h inc easing mean annual empe a u e, pa ic-
ula ly a si es wi h a mean annual empe a u e >0◦C
(Fig. 4a), bu inc eased wi h inc easing clay con en and CN
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1326 S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon
0
10
20
30
40
50 (a) Global, HWSD
Con ibu ion (%)
0
10
20
30
40
50
MAT
MAP
Clay
CN a io
Tex u e
CTI
Ele
Slope
We land
LandCo e
NPP
C opland
HANPPpc
(b) Global, IGBP-DIS
Con ibu ion (%)
Va iables
0
10
20
30
40
50 (c) No h, HWSD
Con ibu ion (%)
0
10
20
30
40
50 (d) No h, IGBP-DIS
Con ibu ion (%)
0
10
20
30
40
50
MAT
MAP
Clay
CN a io
Tex u e
CTI
Ele
Slope
We land
LandCo e
NPP
C opland
HANPPpc
(e) No h, NCSCD
Con ibu ion (%)
Va iables
Figu e 3. Rela i e con ibu ion (in luence) o p edic i e a iables o he model o soil ca bon s ocks in he global obse a ional da abases
(le ) and no he n obse a ional da abases ( igh ).
a io (Fig. 4b and c). The SOC inc eased apidly wi h an in-
c easing CN a io. Rela ionships wi h he mean annual em-
pe a u e we e simila (Fig. 4a). The ela ionship wi h clay
was s eepe in IGBP-DIS han in HWSD, bu he opposi e
was ue o he CN a io (Fig. 4b and c). Wi h espec o land
co e , e e g een needlelea o es s and pe manen we lands
had highe SOC (Fig. 4e).
3.1.2 No he n soils
In he no he n egion, he dominan con ibu o s di e ed
among no he n soil da abases and om hose iden i ied in
he global da abase analyses desc ibed abo e (Fig. 3c–e). In
HWSD, he CN a io was he dominan con ibu o , ollowed
by he we land a io, clay con en , and mean annual p ecip-
i a ion. In IGBP-DIS, clay con en , CN a io, and ele a ion
we e he mos impo an con ibu o s. Fo NCSCD, ele a ion
con ibu ed he mos (∼25 %), bu all o he a iables excep
o he c opland a io and HANPPpc con ibu ed 5–15 %.
The mean annual empe a u e was no as in luen ial as he
global da abases.
The ela ionships be ween a iables and SOC s ock a ied
mo e among he da abases o no he n soils han hose o
global da abases (Fig. 4 –k). Fu he mo e, because he no h-
e n egions we e ex ac ed, he anges o a iables we e na -
owe han he global da abases. In NCSCD, he SOC de-
c eased wi h inc easing empe a u e (Fig. 4 ) and inc eased
wi h inc easing p ecipi a ion (Fig. 4g). The SOC inc eased
wi h inc easing clay con en and CN a io in HWSD and
IGBP-DIS (Fig. 4h and i), which was consis en wi h he
indings ob ained om he global da abases. The inc easing
end wi h inc easing CN a io was also obse ed in NC-
SCD. The SOC dec eased wi h inc easing ele a ion in all
da abases bu showed conside able a iabili y a low ele a-
ions (Fig. 4j).
3.2 Ea h sys em models
3.2.1 Global soil
The con ibu ions o some a iables a ied among ESMs, bu
he mean o he esul s o he ESMs showed ha he mean
annual empe a u e, land co e , and NPP clea ly con ibu ed
o SOC dis ibu ion (Fig. 5a and b). La ge inconsis encies
be ween he obse a ional da abases and ESMs we e ound
in he low con ibu ions o clay con en and he CN a io and
in he high con ibu ions o NPP in ESMs (Fig. 5a and b).
The con ibu ion o NPP o ESMs was g ea e han in he
obse a ional da abases.
The ela ionships be ween SOC and ce ain a iables sub-
s an ially a ied among he ESM da abases (Fig. 6a–e), pa -
icula ly in he mean annual empe a u e (Fig. 6a). The SOC
dec eased wi h inc easing mean annual empe a u e (Fig. 6a)
bu inc eased wi h inc easing p ecipi a ion (Fig. 6b) and NPP
(Fig. 6e). The mean o he ela ionship wi h mean annual
empe a u e o ESMs was highly consis en wi h ha in he
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S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon 1327
-10
0
10
-20 -10 0 10 20 30
(a)
Fi ed unc ion (kg C m−
2
)
MAT ( C)
-10
0
10
0 10 20 30 40 50
(b)
Clay (%)
-20
-10
0
10
20
0 10 20 30 40 50
(c)
C / N a io
-10
0
10
0 0.5 1
(d)
Fi ed unc ion (kg C m−
2
)
We land a io
-10
0
10
0 5 10 15
(e)
Land co e
HWSD
IGBP-DIS
-10
0
10
-20 -10 0 10 20 30
( )
Fi ed unc ion (kg C m−
2
)
MAT ( C)
-10
0
10
0 1000 2000 3000 4000
(g)
MAP (mm)
-20
-10
0
10
20
0 10 20 30 40 50
(h)
Clay (%)
-20
-10
0
10
20
0 10 20 30 40 50
(i)
Fi ed unc ion (kg C m−
2
)
C / N a io
-10
0
10
0 1000 2000 3000
(j)
Ele (m)
-10
0
10
0 0.5 1
(k)
We land a io
HWSD
IGBP-DIS
NCSCD
o
o
Figu e 4. E ec s o he mos in luen ial a iables in he model o he soil ca bon s ock o each global (a–e) and no he n ( –k) obse a ional
da abases. The i ed unc ions we e cen e ed by sub ac ing hei means. See Table A2 o land co e classi ica ions. Because o he small
numbe o da a poin s, he esul s o “pe manen snow and ice” a e no shown (e). The y-axis scales o clay and he CN a io a e di e en
om hose o o he ac o s (c, h, i).
HWSD and IGBP-DIS da abases o he empe a u e ange
−5 o 15 ◦C (Fig. 6a). The inc easing end wi h inc easing
NPP in ESMs was consis en wi h ha o he HWSD, pa -
icula ly below app oxima ely 500 g C m−2o NPP (Fig. 6e).
Al hough he we land a io did no con ibu e o he ESMs
(Fig. 6a) wi h espec o land co e , pe manen we lands had
highe SOC (Fig. 6d).
3.2.2 No he n soils
The mean o he ESMs showed ha o no he n soils, he
main con ibu o s (mean annual empe a u e, land co e , and
NPP) we e mainly he same as in he ESM global ou pu s
(Fig. 5c and d). The con ibu ion o he mean annual empe -
a u e was lowe han ha o he global esul s o he ESMs
(mean o 14 % o he no he n and 29 % o he global em-
pe a u es). The ela i ely la ge disc epancy be ween he ob-
se a ional da abases and ESMs included he lowe con ibu-
ion o clay con en , CN a io, and ele a ion, and he highe
con ibu ion o he mean annual empe a u e, land co e , and
NPP in he ESMs.
The ela ionship be ween SOC and a iables in ESMs as
well as he esul s o he obse a ional da abases a e shown
in Fig. 6 –i. The mean o he ESMs indica ed ha he SOC
in he no he n egion inc eased wi h inc easing NPP, and
he ela ionship was simila o ha in HWSD (Fig. 6i), al-
hough he con ibu ion o NPP in he ESMs di e ed om
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1328 S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon
(b)
(d)
%
%
0
10
20
30
40
50
60
70
80
MAT
MAP
Clay
CN a io
Tex u e
CTI
Ele
Slope
We land
LandCo e
NPP
C opland
HANPPpc
(a) Global
Con ibu ion (%)
ESM mean
HWSD
IGBP-DIS
0
10
20
30
40
50
60
70
80
MAT
MAP
Clay
CN a io
Tex u e
CTI
Ele
Slope
We land
LandCo e
NPP
C opland
HANPPpc
(c) No h
Con ibu ion (%)
Va iables
ESM mean
HWSD
IGBP-DIS
NCSCD
Da abases
No ESM1−ensemble
MRI−ESM1
MPI−ensemble
MIROC−ensemble
IPSL−ensemble
INM−CM4
HadGEM2−CC
GISS−ensemble
GFDL−ESM2M
CMCC−CESM
CESM1−ensemble
CCSM4
CanESM2
BNU−ESM
BCC−ensemble
ESM−mean
IGBP−DIS
HWSD
MAT
MAP
Clay
CN a io
Tex u e
CTI
Ele
Slope
We land
LandCo e
NPP
C opland
HANPPpc
0
20
40
60
80
100
Va iables
Da abases
No ESM1−ensemble
MRI−ESM1
MPI−ensemble
MIROC−ensemble
IPSL−ensemble
INM−CM4
HadGEM2−CC
GISS−ensemble
GFDL−ESM2M
CMCC−CESM
CESM1−ensemble
CCSM4
CanESM2
BNU−ESM
BCC−ensemble
ESM−mean
NCSCD
IGBP−DIS
HWSD
MAT
MAP
Clay
CN a io
Tex u e
CTI
Ele
Slope
We land
LandCo e
NPP
C opland
HANPPpc
0
20
40
60
80
100
Figu e 5. Rela i e con ibu ion (in luence) o p edic i e a iables o he model o he soil ca bon s ock om ESMs and a compa ison wi h
hose o obse a ional da abases. Box plo s show he esul s o ESMs, and he pu ple, g een, ligh blue, and blue ma ks indica e he mean
o he ESMs and esul s om obse a ional da abases (a: global; c: no h). Mosaic plo s o de ailed ela i e con ibu ions o each ESM
(b: global; d: no h) a e shown.
hose o he obse a ional da abase (Fig. 5c). The dec easing
end wi h ele a ion was no eplica ed in he ESMs (Fig. 6g).
4 Discussion and concluding ema ks
4.1 Iden i ied in luen ial ac o s
Compa ed wi h p e ious s udies, we examined he con ibu-
ions o a wide a ie y o ac o s o SOC dis ibu ions. Ou
analyses e ealed ha he mos dis inc di e ences be ween
he obse a ional da abase da a and he ESM ou pu s we e
he e ec s o he CN a io and clay con en (Fig. 5). Fo bo h
global obse a ional da abases, he CN a io was a subs an ial
con ibu o (Fig. 3a and b). The impo an con ibu ion o he
CN a io was he same in he no he n da abases (Fig. 3c–e).
The SOC in he obse a ional da abases inc eased wi h in-
c eases in he CN a io (Fig. 4c), whe eas he SOC alues o
he ESMs we e insensi i e o he CN a io. Ou esul s sup-
po he impo ance o p ope ly inco po a ing he ni ogen
(N) cycle in o SOC models (e.g., con ol o e decomposi-
ion, soil e ili y, nu ien a ailabili y, and plan li e quali y)
(Be g e al., 2001; Co u o e al., 2013; Fe nández-Ma ínez
e al., 2014; Liski e al., 2005; Tuomi e al., 2009; ˇ
Tupek
e al., 2016). None o he ESMs excep o he CESM1 and
No ESM in CMIP5 included e es ial ni ogen p ocesses
(Todd-B own e al., 2013); howe e , including his pa ame e
has been sugges ed as a key imp o emen o he nex model
in e compa ison (CMIP6) (Hajima e al., 2014; Zaehle e al.,
2015). The esul s o ou analysis suppo he impo ance o
including he N cycle in ESM models.
Clay con en is also o en used as a egula o o he decom-
posabili y o o ganic ma e in he soil (e.g., CENTURY and
Ro hC) (Coleman and Jenkinson, 1999; Pa on e al., 1987).
Gene ally, high clay con en inhibi s o ganic ma e decom-
posi ion in he soil. Fu he mo e, high clay con en s o en
esul in low d ainage and anae obic soil condi ions, which
also inhibi o ganic ma e decomposi ion. Fo he IGBP-
DIS da a, he con ibu ion o he clay con en was as high as
ha o he CN a io. The con ol o decomposabili y by he
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S. Hashimo o e al.: Da a-mining analysis o he global dis ibu ion o soil ca bon 1329
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0
10
-20 -10 0 10 20 30
(a)
Fi ed unc ion (kg C m−
2
)
MAT ( C)
-10
0
10
0 1000 2000 3000 4000
(b)
MAP (mm)
-10
0
10
0 1000 2000 3000
(c)
Ele (m)
-10
0
10
0 5 10 15
(d)
Fi ed unc ion (kg C m−
2
)
Land co e
-10
0
10
0 500 1000 1500
(e)
NPP (g C m−2)
ESM mean
HWSD
IGBP-DIS
-10
0
10
-20 -10 0 10 20 30
( )
Fi ed unc ion (kg C m−
2
)
MAT ( C)
-10
0
10
0 1000 2000 3000
(g)
Ele (m)
-10
0
10
0 5 10 15
(h)
Land co e
-10
0
10
0 500 1000 1500
(i)
Fi ed unc ion (kg C m−
2
)
NPP (g C m−2)
ESM mean
HWSD
IGBP-DIS
NCSCD
o
o
Figu e 6. E ec o he mos in luen ial a iables in he model o global (a–e) and no he n ( –i) ou pu s om ESMs and a compa ison wi h
hose o obse a ional da abases. G ey lines show he esul s o each ESM, and he pu ple line indica es he mean o he ESMs. The i ed
unc ions we e cen e ed by sub ac ing hei means. See Table A2 o land co e classi ica ions. Because o he small numbe o da a poin s,
he esul s o “pe manen snow and ice” a e no shown (d, h).
clay con en has been p e iously inco po a ed in si e-scale
p ocess-based models (Pa on e al., 1987) and may be in-
co po a ed in ce ain ESMs because he soil ca bon submod-
els in hese ESMs a e based on he CENTURY model (see
he soil model his o y epo ed in Todd-B own e al., 2014).
Howe e , ega dless o whe he he con ol o decomposabil-
i y by clay is inco po a ed, ou esul s sugges ha he in lu-
ence o clay on he ca bon cycle is no well cap u ed in mos
ESMs.
The mean annual empe a u e was iden i ied as an in luen-
ial ac o in he global da abases (Fig. 3a and b) bu no in
he no he n soil da abases (Fig. 3c–e). Tempe a u e is a main
ac o con olling bo h plan p oduc ion (sou ce o ca bon in-
pu o soil) and soil o ganic ma e decomposi ion, which a e
al eady inco po a ed in ESMs. Based on an analysis o he
ou pu o he e o ophic espi a ion, he empe a u e sensi i -
i y (e.g., Q10 alue) o soil o ganic ma e decomposi ion in
he ESMs has been epo ed as 1.4 o 2.2 (Todd-B own e
al., 2014). In addi ion, ou analyses iden i ied di e se ela-
ionships be ween he mean annual empe a u e and SOC.
The lowe con ibu ion o he mean annual empe a u e in he
no he n soils likely occu ed because empe a u e sensi i -
i y is an exponen ial p ocess, and he magni ude o obse ed
changes unde changing empe a u e is ela i ely small a a
low empe a u e ange, as shown in a compa ison o empe -
a u e unc ions in common biogeochemical models (Sie a
e al., 2015). The ela ionships be ween he SOC and em-
pe a u e ob ained in his s udy include he in eg a ion o he
empe a u e sensi i i y o bo h plan p oduc ion and soil o -
ganic decomposi ion and hus do no p o ide he empe a-
u e sensi i i y pa ame e o indi idual p ocesses o ESMs.
Howe e , he esul s o his s udy can be used o examine
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