In the format provided by the authors and unedited ... · In the format provided by the authors and unedited. Emergent constraints on projections of declining primary production in

In the format provided by the authors and unedited.

Emergentconstraintsonprojectionsofdecliningprimaryproductioninthetropicaloceans:SupplementaryinformationLesterKwiatkowski1*,LaurentBopp1,2,OlivierAumont3,PhilippeCiais1,PeterM.Cox4,CharlotteLaufkötter5,YueLi6,RolandSéférian7

1LaboratoiredesSciencesduClimatetdel’Environnement(LSCE),IPSL,CEA/CNRS/UVSQ,OrmedesMerisiers,Gif-sur-Yvette,91190,France;2LaboratoiredeMétéorologieDynamique(LMD),IPSL,CNRS/EcoleNormaleSupérieure/EcolePolytechnique/UPMC,24rueLhomond,75231ParisCedex05,France;3Laboratoired’OcéanographieetdeClimatologie:ExpérimentationetApprochesNumériques(LOCEAN),IPSL,CNRS/UPMC/IRD/MNHN,4PlaceJussieu,75005Paris,France;4CollegeofEngineering,MathematicsandPhysicalSciences,UniversityofExeter,ExeterEX44QF,UK;5NOAA/GeophysicalFluidDynamicsLaboratory,PrincetonUniversity,Princeton,NJ08540,USA;6Sino-FrenchInstituteforEarthSystemScience,CollegeofUrbanandEnvironmentalSciences,PekingUniversity,Beijing,100871,China;7CNRM,CentreNationaldeRecherchesMétéorologiques,Météo-France/CNRS,42AvenueGaspardCoriolis,31057Toulouse,France.

LETTERSPUBLISHED ONLINE: 10 APRIL 2017 | DOI: 10.1038/NCLIMATE3265

Emergent constraints on projections of decliningprimary production in the tropical oceansLester Kwiatkowski1*, Laurent Bopp1,2, Olivier Aumont3, Philippe Ciais1, Peter M. Cox4,Charlotte Laufkötter5, Yue Li6 and Roland Séférian7

Marine primary production is a fundamental component of theEarth system, providing the main source of food and energyto the marine food web, and influencing the concentration ofatmospheric CO2 (refs 1,2). Earth systemmodel (ESM) projec-tions of global marine primary production are highly uncertainwith models projecting both increases3,4 and declines of up to20% by 21005,6. This uncertainty is predominantly driven bythe sensitivity of tropical ocean primary production to climatechange, with the latest ESMs suggesting twenty-first-centurytropical declines of between 1 and 30% (refs 5,6). Here weidentify an emergent relationship7–11 between the long-termsensitivity of tropical ocean primary production to rising equa-torial zone sea surface temperature (SST) and the interannualsensitivity of primary production to El Niño/Southern Oscil-lation (ENSO)-driven SST anomalies. Satellite-based observa-tions of the ENSO sensitivity of tropical primary productionare then used to constrain projections of the long-term climateimpact on primary production. We estimate that tropical pri-mary production will decline by 3 ± 1% per kelvin increasein equatorial zone SST. Under a business-as-usual emissionsscenario this results in an 11 ± 6% decline in tropical marineprimary production and a 6 ± 3% decline in global marineprimary production by 2100.

Net primary production (NPP) by marine phytoplankton is re-sponsible for approximately 50% of global biological carbon fix-ation12,13 and is a key determinant of atmospheric CO2 concen-trations2. With a turnover time of approximately 1 week14, phyto-plankton are also the base of the marine food web, controllingthe energy and food available to higher tropic levels and ulti-mately fisheries1,15,16.

Ocean NPP is tightly coupled to climate variability on seasonaland interannual timescales, as shown by observations17–19 andreproduced by models5. This is particularly the case in the perma-nently stratified tropical oceans where there is a strong relationshipbetween El Niño/Southern Oscillation (ENSO) variability andprimary production17,20. In ENSO positive phases, a reduction inupwelling intensity and an increase in thermal stratification typi-cally reduces the input of nutrients from nutrient-rich deeper watersto nutrient-depleted surface waters. As phytoplankton productionin the euphotic zone is highly dependent on nutrient availability,increased stratification during warmer conditions suppressesprimary production17,20 (Fig. 1). This interannual coupling between

−1.0

−0.5

0.0

0.5

1.0

4

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0

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−4

SST (HadISST1)Primary production (VGPM)Primary production (CbPM)Primary production (Marra)Primary production (Carr)

Tropical primary production anom

aly (%)

Year

1998 2000 2002 2004 2006 2008

Niñ

o 3.

4 re

gion

SST

ano

mal

y (K

)

Figure 1 | The observed sensitivity of tropical primary production toNiño 3.4 region SST anomalies. Niño 3.4 region (5◦ N–5◦ S, 120◦–170◦ W)HadISST1 SST anomalies and tropical (30◦ N–30◦ S) primary productionanomalies during the SeaWIFS satellite record. Primary productionestimates are shown for the Vertically Generalised Production Model(VGPM; red), the Carbon-based Production Model (CbPM; blue), theMarra et al. 2003 model (Marra; orange) and the Carr et al. 2002 model(Carr; green).

ENSO variability and tropical ocean NPP is predominately drivenby the Pacific18. In other regions, coupling between stratificationand NPP appears limited by concurrent changes in the advectivesupply of nutrients and the wind and buoyancy forcing available tooverturn the water column to the nutricline19.

There are large uncertainties associatedwith observations ofNPPthat predate satellite remote sensing, with no consensus on century-scale global trends. Satellite observations since 1997 have shownthat tropical NPP fluctuations have a strong inverse relationshipwith SST anomalies17; however, the satellite record is too short toseparate natural variability from trends caused by anthropogenicclimate change21,22.

To assess the impact of climate change on NPP, a varietyof ocean biogeochemistry models of wide-ranging complexityhave been developed, and are now integral components of Earth

1Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA/CNRS/UVSQ, Orme des Merisiers, Gif-sur-Yvette 91190, France.2Laboratoire de Météorologie Dynamique (LMD), IPSL, CNRS/Ecole Normale Supérieure/Ecole Polytechnique/UPMC, 24 rue Lhomond, 75231 ParisCedex 05, France. 3Laboratoire d’Océanographie et de Climatologie: Expérimentation et Approches Numériques (LOCEAN), IPSL,CNRS/UPMC/IRD/MNHN, 4 Place Jussieu, 75005 Paris, France. 4College of Engineering, Mathematics and Physical Sciences, University of Exeter, ExeterEX4 4QF, UK. 5NOAA/Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, New Jersey 08540, USA. 6Sino-French Institute for EarthSystem Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China. 7Centre National de Recherches Météorologiques(CNRM), Météo-France/CNRS, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France. *e-mail: [email protected]

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1

© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE3265

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

http://dx.doi.org/10.1038/nclimate3265

TableS1.TheCMIP5multi-modelensemble.TheCoupledModelIntercomparisonProjectPhase5(CMIP5)modelsusedinthisstudyandthesimulationsavailableforeachmodel.Allmodelswererunwithcoupledoceanbiogeochemistryschemes.

Modelname Modelabbreviation Code

SimulationsPiControl(lengthinyears)

RCP8.5 RCP6.0 RCP4.5 RCP2.6

CanadianEarthSystemModelversion2 CanESM21 a ✔ (996) ✔ ✖ ✖ ✖

CommunityEarthSystemModelversion1biogeochemistry

CESM1-BGC2,3 b ✔ (500) ✔ ✖ ✔ ✖

CentroEuro-MediterraneosuiCambiamentiClimaticiCarbon

EarthSystemModelCMCC-CESM4,5 c ✔ (277) ✔ ✖ ✖ ✖

CentreNationaldeRecherchesMétéorologiquesEarthSystemModelCoupled

Modelversion5

CNRM-CM56 d ✔ (850) ✔ ✖ ✔ ✔

GeophysicalFluidDynamicsLaboratoryEarthSystem

Modelversion2GGFDL-ESM2G7 e ✔ (500) ✔ ✔ ✔ ✔

GeophysicalFluidDynamicsLaboratoryEarthSystem

Modelversion2MGFDL-ESM2M7 f ✔ (495) ✔ ✔ ✔ ✔

HadleyCentreGlobalEnvironmentModelversion

2-EarthSystemHadGEM2-ES8 g ✔ (577) ✔ ✔ ✔ ✔

InstitutPierreSimonLaplaceCoupledModelversion5A-LR IPSL-CM5A-LR9 h ✔ (1000) ✔ ✔ ✔ ✔

InstitutPierreSimonLaplaceCoupledModelversion5A-MR IPSL-CM5A-MR9 i ✔ (300) ✔ ✖ ✔ ✔

ModelforInterdisciplinaryResearchonClimateEarth

SystemModelMIROC-ESM10 j ✔ (680) ✔ ✔ ✔ ✔

Max-Planck-InstitutfürMeteorologieEarthSystem

ModelLRversionMPI-ESM-LR11 k ✔ (1000) ✔ ✖ ✔ ✔

Max-Planck-InstitutfürMeteorologieEarthSystem

ModelMRversionMPI-ESM-MR11 l ✔ (1000) ✔ ✖ ✔ ✔

NorwegianEarthSystemModelversionME NorESM1-ME12 m ✔ (252) ✔ ✔ ✔ ✔

© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.




FigureS1.Modelspreadinprojectionsofmarineprimaryproduction.a,globalandb,tropicaldepthintegratedmarineprimaryproductionanomaliesrelativetothe1990-2000meanvaluesforeachoftheCMIP5modelsanalysed.

2020 2040 2060 2080 2100Year

−30

−20

−10

0

10

Prim

ary

prod

uctio

n an

omal

y (%

)

Global Tropics

2020 2040 2060 2080 2100Year

−30

−20

−10

0

10

a b

GFDL-ESM2MCNRM-CM5IPSL-CM5A-MR

IPSL-CM5A-LRMPI-ESM-MRHadGEM2-ES

MIROC-ESM MPI-ESM-LRGFDL-ESM2G

NorESM1-ME

CMCC-CESMCanESM2 CESM1-BGC





FigureS2.ThemechanismscontrollingmodelledNPPvariabilityoninterannualandlong-termtimescales.Therelativechangesintemperaturelimitationfactor(red),lightlimitationfactor(yellow),nutrientlimitationfactor(orange),growthrate(green),biomass(blue)andNPP(purple)fornanophytoplankton(full)anddiatoms(hatched)inthetropicalsurfaceocean.Anincreaseinlimitationfactordenotesweakerlimitation,whichleadstostrongergrowth.Therelativechangeinlong-termvariables(a-d)correspondstotheratiobetweenthe2081-2100andthe2012-2031meansfromRCP8.5simulations13.Therelativechangeininterannualvariables(e-h)correspondstotheratiobetweenthe20yearswithhighestandlowestNPPvaluesinpiControlsimulations.Theproductoftherelativechangeintemperature,lightandnutrientlimitationresultsintheapproximatechangeingrowthrate.

limita

tions Temperature

LightGrowth

Biomass

nanophytopl.

NPPNutrients

diatoms

limitations

Temp

Light Nutr.

growth

Biomass NPPnano

phytop

l.

diatom

s

Long

-ter

mIn

tera

nnua

l

e CESM1-BGC f GFDL-ESM2G g IPSL-CM5A-LR h CNRM-CM5

a CESM1-BGC b GFDL-ESM2G c IPSL-CM5A-LR d CNRM-CM5





FigureS3.PrimaryproductionENSOsensitivity.TheannualmeananomalyintropicalprimaryproductionagainsttheannualmeanNiño3.4regionSSTanomalyforCMIP5pre-industrialcontrolsimulations.

−2 −1 0 1 2

−2−1

0123

NPP

ano

mal

y (%

)

−2 −1 0 1 2

−10−5

05

1015

−1.5 −0.5 0.5 1.0 1.5

−4−2

0246

−1.5 −0.5 0.5 1.0 1.5

−20

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0

10

20

NPP

ano

mal

y (%

)

−1.0 −0.5 0.0 0.5 1.0

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2

4

−2.0 −1.0 0.0 1.0

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1015

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ano

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y (%

)

−1.0 0.0 0.5 1.0 1.5

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024

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024

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0

5

10

NPP

ano

mal

y (%

)

−1.5 −0.5 0.5 1.0 1.5

−10

−5

0

5

Niño 3.4 region SST anomaly (K)−1.5 −0.5 0.5 1.0 1.5

−10−5

05

10

Niño 3.4 region SST anomaly (K)

−1.5 −0.5 0.5 1.0 1.5

−5

0

5

NPP

ano

mal

y (%

)CESM1-BGC p<0.001

IPSL-CM5A-MR p<0.001

GFDL-ESM2M p=0.001

CNRM-CM5 p<0.001CMCC-CESM p<0.001

GFDL-ESM2G p=0.014CanESM2 p<0.001

NorESM1-ME p<0.001

MPI-ESM-MR p<0.001MPI-ESM-LR p<0.001MIROC-ESM p<0.001

IPSL-CM5A-LR p<0.001HadGEM2-ES p<0.001

a b

l

i

e

g h

d f

c

kj

m0.0 0.0−1.0 −1.0

−0.5 −0.5

0.0−1.0

−1.5 −0.5 0.5

−1.0 0.0

−1.0 0.0

−2 −1 0 1 2

−3−2−1

0123






FigureS4.SensitivityofinterannualNPPtoNiño3.4regionSSTanomalies.Mapsshowingcoefficientsofdetermination(R2*)betweenNiño3.4regionSSTanomaliesandNPPanomaliesinthetropics.TheR2valueshavebeenmultipliedwiththesignoftheregressionslopetoobtainR2*,suchthatpositive(red)valuesofR2*indicatepositivecorrelationsandnegative(blue)valuesindicateanti-correlations.Mapsofmodels(a-m)arederivedfrompiControlruns.ThenegativecorrelationbetweenNiño3.4regionSSTanomaliesandNPPanomaliesinthetropicsispredominatelyduetoanomaliesinPacificNPP.





FigureS5.Primaryproductionprojectedclimateimpact.Theannualmeananomalyintropicalprimaryproductionrelativeto1990smeanvaluesagainsttheannualmeanNiño3.4regionSSTanomalyforCMIP5RCP8.5simulations(2006-2100).

NPP

ano

mal

y (%

)N

PP a

nom

aly

(%)

NPP

ano

mal

y (%

)N

PP a

nom

aly

(%)

Niño 3.4 region SST anomaly (K) Niño 3.4 region SST anomaly (K)


NPP

ano

mal

y (%

)CESM1-BGC p<0.001

IPSL-CM5A-MR p<0.001

GFDL-ESM2M p<0.001

CNRM-CM5 p<0.001CMCC-CESM p<0.001

GFDL-ESM2G p<0.001CanESM2 p<0.001

NorESM1-ME p<0.001

MPI-ESM-MR p<0.001MPI-ESM-LR p<0.001MIROC-ESM p<0.001

IPSL-CM5A-LR p<0.001HadGEM2-ES p<0.001

a b

l

i

e

g h

d f

c

kj

m

−1 0 1 2 3

−8

−6

−4

−2

0

−2 0 2 4 6

−20−15−10

−505

10

−1 0 1 2 3

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−4−3−2−1

0123

−1 0 1 2 3 4

−5−4−3−2−1

012

0 1 2 3 4 5

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−505

−1 0 1 2 3 4 5

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0

0 1 2 3 4 5

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−30−20−10

010

−1 0 1 2 3 4 5

−30−25−20−15−10

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−1 0 1 2 3 4 5

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−20

−10

0

10

−1 0 1 2 3

−20−15−10

−50





FigureS6.Primaryproductionsensitivitiesintheobservationalrecord.Theannualtropical(30°N-30°S)primaryproductionanomaliesagainsttheHadISST1Niño3.4region(5°N-5°S,120°-170°W)SSTanomaliesduringtheSeaWIFSsatelliterecordfortheVGPM(red),CbPM(blue),Marra(orange)andCarr(green)satellitealgorithms.Dashedlinesshowcaseresamplingbootstrappedregressionsindicatingthattheuncertaintyforagivensatellitealgorithmistypicallylessthantheuncertaintyacrossalgorithms.AllregressionsaresignificantattheP<0.05level.

VGPM , r=0.66CbPM , r=0.71Marra , r=0.77Carr , r=0.80

−4

−2

0

2

4Tr

opic

al p

rimar

y pr

oduc

tion

anom

aly

(%)

−1.0 −0.5 0.0 0.5Niño 3.4 region SST anomaly (K)





FigureS7.WeightedCMIP5probabilitydensityfunctions.ThePDFsofthelong-termsensitivityoftropicalprimaryproduction(RCP8.5)withalternativeweightedpriors.a,TheCMIP5prior(modelfamilyweighted)PDFdividesmodelweightsequallybetweenmodelsofthesamefamilywhiletheCMIP5prior(OBGCmodelweighted)PDFdividesweightsequallybetweenmodelsthatcontainthesameoceanbiogeochemistryscheme.b,TheCMIP5priors(VGPM,CbPM,MarraandCarrweighted)arederivedbyweightingmodelsaccordingtotheirpositionintherespectiveobservationalPDFsfollowingthemethodologyofGillet(2015)14.ColouredlinesshowtheobservationallyconstrainedPDFsgiveninFigure3ofthemainmanuscript.

−15 −10 −5Primary production climate impact (%/K)

0.0

0.1

0.2

0.3

0.4

Prob

abili

ty d

ensi

ty

0 5−15 −10 −5Primary production climate impact (%/K)

0 5

0.0

0.1

0.2

0.3

0.4

Prob

abili

ty d

ensi

ty

a b

CMIP5 prior (unweighted)CMIP5 prior (VGPM weighted)CMIP5 prior (CbPM weighted)CMIP5 prior (Marra weighted)CMIP5 prior (Carr weighted)After VGPM constraintAfter CbPM constraintAfter Marra constraintAfter Carr constraint

CMIP5 prior (unweighted)CMIP5 prior (model family weighted)CMIP5 prior (OBGC model weighted)After VGPM constraintAfter CbPM constraintAfter Marra constraintAfter Carr constraint





FigureS8.Emergentconstraintsonthesensitivityofglobalmarineprimaryproductiontoclimatechange.a,Thelong-termsensitivityofglobalprimaryproductiontoNiño3.4regionSSTanomaliesagainsttheinterannualsensitivityoftropicalprimaryproductiontoNiño3.4regionSSTanomalies.Thebest-fitobservationalconstraintsderivedfromthedifferentprimaryproductionproductsareshownassolidverticallineswithdashedlinesindicating±1standarderror.b,theprobabilitydensityfunctions(PDFs)ofthelong-termsensitivityofglobalprimaryproduction.Theblacklineshowsthe‘prior’PDF,assumingallmodelsareequallylikelyandfromaGaussiandistribution.ThecolouredlinesshowthefourobservationallyconstrainedPDFs.

Tropical primary production ENSO sensitivity (%/K)

Glo

bal p

rimar

y pr

oduc

tion

clim

ate

impa

ct (%

/K)

−12 −10 −8 −6 −4 −2 0

−4

−3

−2

−1

0

−6 −4 −2

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Global primary production climate impact (%/K)

Prob

abili

ty d

ensi

ty

r = 0.90

VGPM constraintCbPM constraintMarra constraintCarr constraint

0 2

CMIP5 prior: -2.27 ± 1.20VGPM constrained: -1.91 ± 0.71CbPM constrained: -1.79 ± 0.65Marra constrained: -1.40 ± 0.61Carr constrained: -1.57 ± 0.62

a

b

c

f e

b

id

h

al

gj

mk





FigureS9.ThesensitivityoftheemergentrelationshiptoNPPdepthintegral.Thelong-termsensitivityoftropicalprimaryproductiontoNiño3.4regionSSTanomalies(RCP8.5)againsttheinterannualsensitivityoftropicalprimaryproductiontoNiño3.4regionSSTanomalies.PrimaryproductionENSOsensitivitiesarecalculatedacrossarangeofdepthintegralswhileprimaryproductionclimateimpactsarecalculatedfortheentirewatercolumn.TheVGPM(red)andCbPM(blue)observationalconstraintsareshownassolidverticallineswithdashedlinesindicating±1standarderror.

−12 −10 −8 −6 −4 −2 0

−8

−6

−4

−2

0

Primary production ENSO sensitivity (%/K))

Prim

ary

prod

uctio

n cl

imat

e im

pact

(%/K

)all depths0−140 m0−120 m0−100 m0−80 m





FigureS10.Scenariosensitivityofmulti-modelemergentrelationships.Thelong-termNiño3.4regionSSTsensitivityoftropicalprimaryproductionagainsttheinterannualsensitivityoftropicalprimaryproductiontoNiño3.4regionSSTanomaliesforRCP8.5(red),RCP6.0(yellow),RCP4.5(green)andRCP2.6(blue).Thebest-fitobservationalconstraintsderivedfromthefourprimaryproductionalgorithmsareshownassolidverticallineswithdashedlinesindicating±1standarderror.

−10 −8 −6 −4 −2 0Primary production ENSO sensitivity (%/K)

−12

−10

−8

−6

−4

−2

0Pr

imar

y pr

oduc

tion

clim

ate

impa

ct (%

/K)

)

RCP8.5 r = 0.91, p < 0.001

RCP2.6 r = 0.95, p < 0.001

RCP6.0 r = 0.99, p = 0.001RCP4.5 r = 0.97, p < 0.001





FigureS11.Scenariosensitivityofemergentconstraints.Theprobabilitydensityfunctions(PDFs)ofthelong-termSSTsensitivityoftropicalprimaryproductionfora,RCP8.5b,RCP6.0c,RCP4.5andd,RCP2.6.Theblacklineshowsthe‘prior’PDFs,assumingallmodelsareequallylikelyandfromaGaussiandistribution.ThecolouredlinesshowtheobservationallyconstrainedPDFs.

RCP4.5 RCP2.6

c



−15 −10 −5 50

Primary production climate impact (%/K) Primary production climate impact (%/K)

−15 −10 −5 50

d

RCP6.0

Primary production climate impact (%/K)

b


−15 −10 −5 50

Prob

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RCP8.5

a

CMIP5 prior: -4.03 ± 2.19 VGPM constrained: -3.36 ± 1.26CbPM constrained: -3.14 ± 1.14Marra constrained: -2.42 ± !.06Carr constrained: -2.74 ± !.07

−15 −10 −5 50

Primary production climate impact (%/K)

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ty

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References

1. Flato,G.M.etal.TheCanadianCentreforClimateModellingandAnalysisglobalcoupledmodelanditsclimate.Clim.Dyn.16,451–467(2000).

2. Gent,P.R.etal.TheCommunityClimateSystemModelVersion4.J.Clim.24,4973–4991(2011).

3. Lindsay,K.etal.Preindustrial-ControlandTwentieth-CenturyCarbonCycleExperimentswiththeEarthSystemModelCESM1(BGC).J.Clim.27,8981–9005(2014).

4. Vichi,M.etal.Globalandregionaloceancarbonuptakeandclimatechange:sensitivitytoasubstantialmitigationscenario.Clim.Dyn.37,1929–1947(2011).

5. Cagnazzo,C.,Manzini,E.,Fogli,P.G.,Vichi,M.&Davini,P.Roleofstratosphericdynamicsintheozone–carbonconnectionintheSouthernHemisphere.Clim.Dyn.41,3039–3054(2013).

6. Voldoire,A.etal.TheCNRM-CM5.1globalclimatemodel:descriptionandbasicevaluation.Clim.Dyn.40,2091–2121(2012).

7. Dunne,J.P.etal.GFDL’sESM2GlobalCoupledClimate–CarbonEarthSystemModels.PartI:PhysicalFormulationandBaselineSimulationCharacteristics.J.Clim.25,6646–6665(2012).

8. Collins,W.J.etal.DevelopmentandevaluationofanEarth-Systemmodel-HadGEM2.Geosci.ModelDev.4,(2011).

9. Dufresne,J.-L.etal.ClimatechangeprojectionsusingtheIPSL-CM5EarthSystemModel:fromCMIP3toCMIP5.Clim.Dyn.40,2123–2165(2013).

10. Watanabe,S.etal.MIROC-ESM2010:modeldescriptionandbasicresultsofCMIP5-20c3mexperiments.Geosci.ModelDev.4,(2011).

11. Giorgetta,M.A.etal.Climateandcarboncyclechangesfrom1850to2100inMPI-ESMsimulationsfortheCoupledModelIntercomparisonProjectphase5.J.Adv.Model.EarthSyst.5,572–597(2013).

12. Bentsen,M.etal.TheNorwegianEarthSystemModel,NorESM1-M-Part1:Descriptionandbasicevaluation.Geosci.ModelDev.Discuss.5,2843–2931(2012).

13. Laufkötter,C.etal.Driversanduncertaintiesoffutureglobalmarineprimaryproductioninmarineecosystemmodels.Biogeosciences12,6955–6984(2015).

14. Gillett,N.P.Weightingclimatemodelprojectionsusingobservationalconstraints.PhilTransRSocA373,20140425(2015).





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