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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
2
0
−2
−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]
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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) ✔ ✔ ✔ ✔
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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
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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
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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
−10
0
10
20
NPP
ano
mal
y (%
)
−1.0 −0.5 0.0 0.5 1.0
−4
−2
0
2
4
−2.0 −1.0 0.0 1.0
−10−5
05
1015
NPP
ano
mal
y (%
)
−1.0 0.0 0.5 1.0 1.5
−4−2
024
−1.0 0.0 0.5 1.0 1.5 2.0
−4−2
024
−1.0 −0.5 0.0 0.5
−10
−5
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
Niño 3.4 region SST anomaly (K)
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FigureS4.SensitivityofinterannualNPPtoNiño3.4regionSSTanomalies.Mapsshowingcoefficientsofdetermination(R2*)betweenNiño3.4regionSSTanomaliesandNPPanomaliesinthetropics.TheR2valueshavebeenmultipliedwiththesignoftheregressionslopetoobtainR2*,suchthatpositive(red)valuesofR2*indicatepositivecorrelationsandnegative(blue)valuesindicateanti-correlations.Mapsofmodels(a-m)arederivedfrompiControlruns.ThenegativecorrelationbetweenNiño3.4regionSSTanomaliesandNPPanomaliesinthetropicsispredominatelyduetoanomaliesinPacificNPP.
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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)
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
−15
−10
−5
0
−1 0 1 2 3 4 5
−30
−20
−10
0
10
−1 0 1 2 3
−4−3−2−1
0123
−1 0 1 2 3 4
−5−4−3−2−1
012
0 1 2 3 4 5
−30−25−20−15−10
−505
−1 0 1 2 3 4 5
−15
−10
−5
0
0 1 2 3 4 5
−15
−10
−5
−1 0 1 2 3 4 5 6
−30−20−10
010
−1 0 1 2 3 4 5
−30−25−20−15−10
−50
−1 0 1 2 3 4 5
−30
−20
−10
0
10
−1 0 1 2 3
−20−15−10
−50
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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)
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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
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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
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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
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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
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FigureS11.Scenariosensitivityofemergentconstraints.Theprobabilitydensityfunctions(PDFs)ofthelong-termSSTsensitivityoftropicalprimaryproductionfora,RCP8.5b,RCP6.0c,RCP4.5andd,RCP2.6.Theblacklineshowsthe‘prior’PDFs,assumingallmodelsareequallylikelyandfromaGaussiandistribution.ThecolouredlinesshowtheobservationallyconstrainedPDFs.
RCP4.5 RCP2.6
c
CMIP5 prior: -4.18 ± 2.79VGPM constrained: -3.16 ± 1.19CbPM constrained: -2.85 ± 0.95Marra constrained: -1.85 ± 0.73Carr constrained: -2.30 ± 0.77
CMIP5 prior: -4.43 ± 3.23VGPM constrained: -2.92 ± 1.45CbPM constrained: -2.56 ± 1.19Marra constrained: -1.42 ± 0.97Carr constrained: -1.93 ± 1.01
−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
CMIP5 prior: -4.81 ± 3.26VGPM constrained: -3.23 ± 0.98CbPM constrained: -2.94 ± 0.72Marra constrained: -2.02 ± 0.45Carr constrained: -2.43 ± 0.51
−15 −10 −5 50
Prob
abili
ty d
ensi
ty
0.0
0.2
0.4
0.6
0.8
1.0
Prob
abili
ty d
ensi
ty
0.0
0.2
0.4
0.6
0.8
1.0
Prob
abili
ty d
ensi
ty
0.0
0.2
0.4
0.6
0.8
1.0
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)
Prob
abili
ty d
ensi
ty
0.0
0.2
0.4
0.6
0.8
1.0
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