multiple greenhouse-gas feedbacks from the land biosphere under future climate change scenarios

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE1864

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

SUPPLEMENTARY INFORMATION1

Multiple greenhouse gas feedbacks from the land bio-2

sphere under future climate change scenarios3

Benjamin D. Stocker1,2∗, Raphael Roth1,2, Fortunat Joos1,2, Renato Spahni1, Marco Steinacher1,2,4

Soenke Zaehle3, Lex Bouwman4,5, Xu-Ri6, Iain Colin Prentice7,85

1Climate and Environmental Physics, Physics Institute, University of Bern, 3012, Bern,6

Switzerland7

2Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland8

3Max Planck Institute for Biogeochemistry, Department for Biogeochemical Systems, 077459

Jena, Germany10

4Department of Earth Sciences, Geochemistry, Faculty of Geosciences, Utrecht University11

5PBL Netherlands Environmental Assessment Agency, P.O. Box 303, 3720 AH Bilthoven, The12

Netherlands13

6Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan14

Plateau Research, Chinese Academy of Sciences, Beijing 100101, China15

7Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia16

8Grantham Institute for Climate Change and Division of Ecology and Evolution, Imperial Col-17

lege, Silwood Park, Ascot SL5 7PY, UK.18

S1 Overview19

This document provides supplementary information not provided in the main text of the arti-20

cle Multiple greenhouse gas feedbacks from the land biosphere under future climate change21

scenarios. Setion S2 provides complete references to all input files used to drive simulations.22

Section S3 provides a comprehensive documentation of the model setups used to quantify feed-23

backs and a mathematical framework thereof. Section S4 provides additional figures for results24

referred to in the main text but not given therein. In particular: spatial information of GHG25

emission changes in the 21st century; results from a 2000 yr simulation of the coupled Bern3D-26

LPX model in response to a step increase in radiative forcing by 3.7 Wm−2 ; and a quantification27

1

MULTIPLE GREENHOUSE-GAS FEEDBACKS FROM THE LAND BIOSPHERE UNDER FUTURE CLIMATE CHANGE SCENARIOS

© 2013 Macmillan Publishers Limited. All rights reserved.

http://www.nature.com/doifinder/10.1038/nclimate

2of “traditional” C cycle sensitivities.28

S2 Input data29

Figure S1 illustrates the model setup: which variables are prescribed to individual model com-30

ponents and which are simulated interactively. The naming of variables introduced here is31

followed throughout this document. Components in red represent the model parts used for32

the ’offline’ simulations (see Methods, main article). Inputs prescribed to LPX-Bern 1.0 are N-33

deposition (Ndep)1, mineral N-fertilisation (Nfert)2, distribution of croplands, pastures and urban34

areas (ALU)3, fixed distribution of peatland areas (Apeat)4 and seasonally inundated wetlands35

(Ainund)4. In offline mode, monthly temperature, precipitation and cloud cover are prescribed36

from the CMIP5 outputs (tempCMIP5, precCMIP5, ccovCMIP5, see also Tab.S1). In online mode,37

a spatial pattern per unit temerature change (tempANOM, precANOM, ccovANOM, derived for38

each CMIP5 model used, is scaled by the global mean temperature change simulated online by39

Bern3D (∆T), see Section S2). Simulated terrestrial emissions (eCO2LPX, eN2OLPX, eCH4

LPX)40

are complemented with other sources not simulated by LPX (eCO2EXT, eN2OEXT, eCH4

EXT)41

and an additional flux to close the atmospheric budget in 1900 AD (eN2OADD, eCH4ADD). At-42

mospheric concentrations (cCO2, cN2O, cCH4, cOtropos3 , rOH) are calculated online in Bern3D43

using a simplified atmospheric chemistry model (ATMOS. CHEM.5, 6) to simulate variations in44

the life time of CH4 and using prescribed emissions of reactive gases from the RCP database45

(eVOCRCP, eNOXRCP, eCORCP). cCO2 evolves as a result of the coupled oceanic and ter-46

restrial C cycle and is communicated back to LPX where it affects plant photosynthesis. The47

radiative forcing of all agents affected by variations of terrestrial GHG emissions (fCO2, fN2O,48

fCH4, fOtropos3 , fH2Ostratos) are simulated online in Bern3D after Joos et al. (2001)5 (Bern3D49

RADIATIVE FORCING). Radiative forcing from other agents (fAerosol, fCFCs, fHFCs) are50

prescribed directly from the RCP database. The global mean temperature increase (∆T) is cal-51

culated by Bern3D using a two-dimensional representation of the Earth energy balance7 and52

a three-dimensional physical ocean model8. The top arrow represents the communication of53

land albedo changes (∆α) from LPX to the radiative component of Bern3D. The bottom arrow54

visualizes the feedback from simulated cCO2 and ∆T on LPX. Land surface albedo changes in55

response to vegetation changes and snow cover are simulated based on Otto et al. (2011)9 and56


3are described in Steinacher 201110.57

Bern3DATMOS.CHEM.

eCO2EXT

eN2OEXT

eCH4EXT

eVOCRCP

eNOXRCP

eCORCP

eCO2LPX

eN2OLPX

eCH4LPX

eN2OADD

eCH4ADD

emissions

tempCMIP5

precCMIP5

ccovCMIP5

NdepNfertALUApeatAinund

inputs

LPX

cCO2cN2OcCH4cO3

tropos

rOH

concentrations

RCP

fCO2fN2OfCH4fO3

tropos

fH2Ostratos

radiative forcing

fAerosolfCFCsfHFCs

Bern3DRADIATIVEFORCING

RCPtempANOM

precANOM

ccovANOM

Δα

Bern3DCLIMATE

climate change

ΔT

ΔTcCO2

Figure S1: Model setup: Inputs and model components.

Climate58

In offline mode, monthly climate data is prescribed to LPX. CMIP5 climate output for sur-59

face temperature, precipitiation, and cloud cover is applied for all RCP2.6 and RCP8.5 experi-60

ments as given in Tab.S1. To correct CMIP5 model output for bias w.r.t. the present-day CRU61

climatology11, climate fields are anomalized as follows:62

CMIP5∗x,y,t = CMIP5x,y,t − CMIP5x,y + CRUx,y , (S1)

where CMIP5x,y,t is the original and CMIP5∗x,y,t is the offset-corrected CMIP5 climate variable63

field (surface temperature, precipitation, cloud cover) defined for t = 2005-2100 (2300) AD.64

Bars denote the mean over the years 1996-2005 AD.65

In online mode, a spatial pattern per unit temerature change, derived for each CMIP5 model66

and each month is scaled by the global mean temperature change (∆T) simulated online by67

Bern3D. Temperature and precipitation anomaly patterns are illustrated in Figures S2 and S3.68


4

Table S1: CMIP5 ensemble simulations used for offline simulations. ’r1’ refers to the CMIP5

terminology (’r1i1p1’), ’r2’ to ’r2i1p1’, etc. These are different simulation ensemble members

of CMIP5 experiments with equal forcings but slightly different initial conditions.

model RCP2.6 RCP8.5 Modeling Center

HadGEM2-ES r1, r2, r3, r4 r1, r2, r3, r4 Met Office Hadley Centre

MPI-ESM-LR r1, r2, r3 r1, r2, r3 Max-Planck Institute

for Meteorology

IPSL-CM5A-LR r1, r2, r3 r1, r2, r3, r4 Institut Pierre-Simon Laplace

MIROC-ESM r1 r1 Japan Agency for Marine-Earth

Science and Technology

CCSM4 r1, r2, r3, r4, r5 r1, r2, r3, r4, r5 National Center for

Atmospheric Research

N fertiliser input69

Mineral N fertilizer (Nfert) is assumed to be added to croplands only. Nfert inputs on pastures,70

as well as N inputs from manure are not simulated explicitly. Tracking C and N mass flow71

from harvest on agricultural land to soil application of animal manure and recycling of crop72

residues, with denitrification, volatilisation, and N2O emissions along the pathway, is beyond73

the the scope of the present study. N2O emissions from manure are prescribed instead (see74

Section below).75

Four equal doses of mineral N-fertiliser are added during the vegetation period to the soil76

nitrate and ammonium pool with a constant respective split of 1:7. For the historical period77

(1765-2005 AD), Nfert data is from Zaehle et al. (2011)2 (ZAE11), based on country-wise78

ammonium plus nitrate data from the FAO statistical database (1960-2005)12. For years 1910-79

1960, an exponential increase was assumed.80

For the years 2005-2100 AD, spatial Nfert data provided by the the IAM groups (RCP8.5:81

Riahi et al. (2011)13, pers. comm. K. Riahi, January 2012; RCP2.6: VanVuuren et al.82

(2011)14, 15, pers. comm. L. Bouwman, April 2012) is used to scale the 2005 AD-field from83

ZAE11 for each continent separately. Thereby, the relative increase in the total amount of an-84


5

Figure S2: Annual mean temperature anomaly patterns per ◦C global temperature change used

for coupled simulations [◦C/(global mean ◦C)]. Note that values above 1 represent locations

where regional temperature change is larger than on global average.

nual Nfert inputs in each continent is conserved from the original data delivered by the IAM85

groups, while the spatial pattern within each continent is conserved from the data of ZAE11 in86

year 2005 AD (see Figures S4, S5). This scaling can be described by87

NRCPt,i = NZAE11

2005,i

∑i=k

NRCP−origt,i∑

i=k

NRCP−orig2005,i

, (S2)

where NRCPt,i is the harmonized RCP Nfert scenario, NZAE11

2005,i=k is the spatialised (index i) field of88

ZAE11 in year 2005 AD. NRCP−origt,i is the original spatialised RCP scenario data for each time89


6

Figure S3: Annual mean precipitation anomaly patterns per degree global temperature change

used for coupled simulations [mm/month/(global mean ◦C)].

t and grid cell i. The sum over all grid cells i belonging to continent k is used to scale NZAE112005,i .90

For RCP8.5, the scaling factor is corrected to guarantee that the total Nfert input in 2100 and in91

each continent is identical as in the original data.92


7

year AD

TgN

/yr

1900 1950 2000 2050 21000

50

100

150

200

N−fertiliser input

historicalRCP 2.6RCP 8.5

year AD

TgN

/yr

1900 1950 2000 2050 21000

20

40

60

80

N−deposition


year AD

ppm

1900 1950 2000 2050 2100

200

400

600

800

1000

cCO2 for offline simulations


year AD

106 km

2

1900 1950 2000 2050 21000

10

20

30

40

50

60

land use


totalcroppasture

Figure S4: top left: Global mineral nitrogen fertilizer input (Nfert) [TgN/yr] for the historical

period (black), RCP2.6 (blue) and RCP8.5 (red). top right: Global atmospheric N deposition

from Lamarque et al. (2011)1 [TgN/yr] for the historical period (black), RCP2.6 (blue) and

RCP8.5 (red). bottom left: Atmospheric CO2 concentration as prescribed in offline simulations

for the historical period (black), RCP2.6 (blue) and RCP8.5 (red). bottom right: Global land

use area from Hurtt et al. (2006)3 [TgN/yr] for the historical period (black), RCP2.6 (blue) and

RCP8.5 (red).


8

Figure S5: Nfert input [gN m−2 yr−1], RCP2.6. (Left) and RCP8.5 (Right), for years 2005,

2030, 2050 and 2100 (top to bottom). Fertiliser is applied to croplands. These are defined by

Hurtt et al. (2006)3.


9N deposition93

Annual fields for atmospheric NHx and NOy deposition are from Lamarque et al. (2011)1,94

generated by an atmospheric chemistry/transport model and provided for the historical period95

as well as for RCP scenarios of the 21st century. NHx and NOy are added to the ammonium and96

nitrate pool in LPX along with daily precipitation. For the present study, we treat N deposition97

as an external forcing, meaning that it is not affected by climate or CO2. The assessment of a98

feedback between climate and CO2, emissions of NO, NO2 and NH3 from soils, atmospheric99

transport and chemical reactions, deposition and radiative forcing is beyond the present study.100

We summarize the sum of N deposited and Nfert as “reactive N inputs” (Nr).101

Land use change102

Anthropogenic land use change (LU) is treated as an external forcing (see Figure 1 in the main103

text) and is prescribed also in the ’ctrl’ online and offline simulations. LU is prescribed as104

maps for each year from Hurtt et al. (2006)3. Resulting CO2 emissions from deforestation105

are simulted by the model. A thorough description can be found in previous publications16, 17.106

Note that LU also has indirect effects by changing the C sink capacity under rising cCO216.107

This is reflected in a stronger negative feedback factor rC∆C (Figure S18) when the model is set108

up without accounting for LU (simulation DyNrPt, Table S4, and Figure S10).109

N2O and CH4 emissions not simulated by LPX110

For eN2OEXT we use historical emission data for domestic/industrial sources, fire, and manure111

as described in Zaehle et al. (2011)2 (Figure S6). Domestic/industrial emissions were derived112

from VanAardenne et al. (2001)18 giving a flux of 1.2 TgN2O-N/yr in 2005 AD. The biomass113

burning estimate (0.5 TgN2O-N/yr in 2005 AD) is from Davidson (2009)19. Manure-N2O flux114

is taken as a fraction of global manure-N input yielding 2.2 TgN2O-N/yr in 2005 AD. To extend115

N2O emissions to 2100 AD, we scale the total of domestic/industrial plus fire plus manure116

emissions in year 2005 AD with the relative increase in the sum of respective categories in117

each RCP scenario. RCP emission data are consistent with the economical, demographic, and118


10political development in the respective RCP scenarios as simulated by Integrated Assessment119

Modelling.120

To complete the N2O budget and reproduce the atmospheric concentration for pre-industrial121

conditions, we tuned the oceanic source to 3.3 TgN2O-N/yr (eN2OADD in Figure S1). This is in122

agreement with the broad range of available estimates (1.2-5.8 TgN2O-N/yr)20–22. The oceanic123

source is scaled by 3.3% between 1850 and 2005 AD with the scaling factor following the in-124

crease in atmospheric N deposition. This increase reflects the increase in reactive N in oceans125

due to atmospheric deposition23. After 2005 AD, the oceanic source is held constant in all126

scenarios.127

Non-soil CH4 emissions are taken from the RCP database24. These include emissions128

from biomass burning and wet rice cultivation which are not explicitly simulated by LPX. To129

close the atmospheric CH4 budget and reproduce the atmospheric concentration in 1900 AD,130

we tuned the additional prescribed source (representing geological and small oceanic sources)131

to 38 TgCH4/yr (eCH4ADD in Figure S1). This is based on a data spline of southern-hemisphere132

atmospheric records as provided by the RCP database24.133

year AD

TgN

/yr

1900 1950 2000 2050 21000

2

4

6

8

10Zaehle et al., 2011RCP 2.6RCP 8.5ocean

year AD

TgN

/yr

1900 1920 1940 1960 1980 20000.0

0.5

1.0

1.5

2.0

2.5

3.0domestic/industrialfiremanure

Figure S6: Left: N2O emissions not explicitly simulated by LPX (eN2OEXT) and oceanic emis-

sions (green). The black line (’Zaehle et al., 2011’) is the sum of domestic/industrial, fire, plus

manure as given in right plot. Right: eN2OEXT by sources. Both are given in TgN2O-N/yr.


11S3 Simulation protocol and feedback quantification134

Feedback formalism135

Our framework to quantify feedbacks between land and climate follows the formalism applied136

in physical climate science as presented, e.g., in Roe (2009)25 and Gregory et al. (2009)26.137

The latter also address feedbacks between climate and the carbon cycle. Here, we extend138

this concept to other radiative agents (eCH4, eN2O and albedo) mediated by the terrestrial139

biosphere and affected by environmental conditions (climate; and atmospheric CO2 concen-140

trations, cCO2). We start with a brief introduction into the feedback formalism. Consider the141

Earth’s climate to be a system responding to a radiative forcing F with a radiative response H ,142

so that in equilibrium, the net energy flux into the system N is zero and no warming or cooling143

occurs.144

N = F −H , N = 0 ⇒ F = H (S3)

Observations confirm that H can be linearized with respect to the temperature change ∆T 26,145

so that146

F = λ ·∆T (S4)

λ is the climate feedback factor given in Wm−2K−1 and is equal to the inverse of the climate147

sensitivity factor. λ is thus the basic quantity to describe the temperature change of the climate148

system in response to a given radiative forcing. However, λ summarizes all feedbacks operat-149

ing. To quantify an individual feedback, we define a reference system, in which the feedback150

of interest is not operating. The most basic reference system is to consider the Earth as a Black151

Body. Here, we chose the reference system to repesent the ocean-atmosphere climate system152

without any interaction with the land. This is the control simulation (termed ’ctrl’), in which153

the radiative forcing F leads to a temperature change ∆T ctrl (see Figure 4 in main text, bottom154

right, dashed line).155

∆T ctrl =F

λ0

(S5)

λ0 is the sum of all non-land feedbacks operating in the control simulation (the Black Body156

response or Planck feedback (BB), water vapor (WV), ice-albedo (α ), lapse rate (LR), cloud157

(C), etc. λ0 = λBB + λWV + λα + λLR + λC + .... Note, that the radiative forcing F depends158

on the reference system chosen. Note also that in our reference system, the land is still affected159


12by external forcings (land use, Nr inputs), which leads to terrestrial GHG emissions and albedo160

change, eventually affecting ∆T ctrl.161

When a feedback is included, the system adjusts to a different temperature ∆T because162

it now “sees” an additional radiative forcing (∆F ) triggered by the feedback. E.g. a warmer163

climate stimulates terrestrial N2O emissions which increase its atmospheric concentration and164

lead to additionally absorbed energy due to its greenhouse effect. Let us look at “land” as a165

feedback element in the climate system interacting via a multitude of feedbacks. We summarize166

these as λland. With the additional radiative forcing from all land feedbacks written as ∆F =167

rland ·∆T we get168

λ0 ·∆T = F + rland ·∆T . (S6)

With r = −λ we get the form presented in the paper (Eq.2)169

F = (λ0 + λland) ∆T . (S7)

This illustrates that the additional radiative forcing per degree temperature change (r) caused170

by the feedback of interest is equal to the the negative of the feedback factor λ. Equations171

(S5) and (S7) are combined to derive λ using a control simulation (’ctrl’) and a fully coupled172

simulation (’CT’, see Table S3). Equation (S6) can be rewritten as173

∆T =F

λ0

+r

λ0

∆T , (S8)

illustrating that the feedback arises because a fraction f = rλ0

of the system output ∆T is174

fed back into the input. We can take a different perspective and characterise the effect of a175

feedback with the gain factor G = ∆T∆T ctrl . By combining Equations (S5) and (S6), the gain176

factor becomes177

G =∆T

∆T ctrl=

Fλ0−λ0f

Fλ0

=λ0

λ0 − λ0f=

1

1− f(S9)

Note that f = rλ0

is often referred to as the “feedback factor”, but not here, where the feedback178

factor is λ = −r. The advantage of the formulation of Equation (S6) and (S7) is that individual179

feedbacks can be added to derive their combined effect.180

λ0 ·∆T = F + ∆T∑i

ri , (S10)


13or in the form presented in the paper181

F = (λ0 +∑i

λi) ∆T . (S11)

Note that f = 1λ0

∑i ri and that G 6=

∑iGi.182

Model setups183

So far, we have been looking at feedbacks arising simply “from land”. However, a multitude of184

processes affecting climate are operating in terrestrial ecosystems. One way is to decompose185

the total land feedback into contributions from individual forcing agents (eN2O, eCH4, ∆C,186

and ∆albedo):187

λland = λ∆C + λCH4 + λN2O + λ∆albedo + δ (S12)

δ is a non-linearity term. To isolate individual λs, the model has to be set up, where only the188

respective feedback is operating. In practice, we prescribe the time series of global terrestrial189

emissions from the control run for all non-operating forcing agents. In the case of albedo, we190

prescribe the monthly two-dimensional field from the control run. Table S3 provides a full191

account of all model setups applied.192

A further decomposition of λland can be done by drivers of land feedbacks. Not only193

climate (superscript ’T’) but also atmospheric cCO2 (superscript ’C’) affects terrestrial GHG194

emissions and albedo. We quantify its effects in the same framework.195

λland = λC + λT + δ ≡ λCT (S13)

Simulations where only changes of the respective driver is communicated to the land196

model (see also Figure 1 in the main article) are used to quantify λT and λC. In “climate-197

coupled” simulations, only feedbacks arising from the sensitivity of forcing agents to climate198

are taken into account and lead to a temperature change ∆TT:199

F = (λ0 + λT) ∆TT , (S14)

In “cCO2-coupled” simulation, the land sees only changes in cCO2 and the system attains a200


14temperature ∆TC:201

F = (λ0 + λC) ∆TC , (S15)

Additionally, we quantify the modification of feedbacks by the individual effects of C-202

N interactions, peatland C dynamics27, anthropogenic land use change, and Nr inputs (Figure203

S18). Respective feedback factors are quantified identically but with LPX not simulating re-204

spective features (see Tab. S4). This requires the full set of coupled as well as ’ctrl’ simulations205

for each setup. Due to non-linearities in the system, the “expansion” with respect to the full206

setup (by turning one of each feature off in an individual setup) is preferred over an expansion207

w.r.t. the “null-”setup (by turning only one of each feature on). To quantify the modification208

by C-N interactions, results from a carbon-only version of LPX are used.209

To assess the sensitivity of terrestrial GHG emissions to different drivers and to capture210

the effects of the range of climate projections in different CMIP5 models, we use LPX in211

an offline mode (see Table S2 and Figure S1) and only evaluate emissions (Figure 3 in main212

article).213

Table S2: Offline simulations overview. Climate (∆T) and atmospheric CO2 (∆CO2) and Nr

(Ndep and Nfert) are either prescribed from CMIP5 climate models, RCP data, and other sources

or held constant (’0’). Model features anthropogenic land use change (LU), interactive carbon-

nitrogen cycling (DyN), and C-N dynamics/CH4 emissions on peatlands (peat) are turned on in

all simulations. The lines in main article-figure 2 relate as follows: ’standard’ is r1, ’no CO2’

is r3, ’no climate’ is r5, ’no Nr’ is r2.

simulation ∆T ∆CO2 Ndep Nfert LU DyN peat

r1 CMIP5 RCP 1 1 1 1 1

r2 CMIP5 RCP 0 0 1 1 1

r3 CMIP5 0 1 1 1 1 1

r4 CMIP5 0 0 0 1 1 1

r5 0 RCP 1 1 1 1 1

r6 0 RCP 0 0 1 1 1

r7 0 0 1 1 1 1 1

r8 0 0 0 0 1 1 1


15

Table S3: Couplings overview. Columns ∆CO2 and ∆T indicate which drivers are communi-

cated to the land model LPX. Columns cCO2, cCH4, cN2O, and ∆α indicate whether variations

in the respective forcing agent affect the climate module in Bern3D (X) or if the climate module

responds to variations in respective agents prescribed from the control run (’ctrl’).

name ∆T ∆CO2 cCO2 cCH4 cN2O ∆α

control

ctrl 0 0 X X X X

fully coupled

CT 1 1 X X X X

cCO2-land coupled

C 0 1 X X X X

climate-land coupled

T 1 0 X X X X

fully coupled - single agent

CT-∆CO2 1 1 X ctrl ctrl ctrl

CT-∆CH4 1 1 ctrl X ctrl ctrl

CT-∆N2O 1 1 ctrl ctrl X ctrl

CT-∆α 1 1 ctrl ctrl ctrl X

fully coupled - CO2/albedo only

CT-∆CO2-∆α 1 1 X ctrl ctrl X

Table S4: Features overview. Model features, variably turned on (’1’) and off (’0’) are: an-

thropogenic land use change (LU), interactive carbon-nitrogen cycling (DyN), N-deposition

(Ndep), N-fertilisation (Nfert), and C-N dynamics/CH4 emissions on peatlands (peat). For the

model setup with DyN turned off, the carbon-only version of LPX was used.

name LU DyN Ndep Nfert peat

LuDyNrPt 1 1 1 1 1

LuDyNr 1 1 1 1 0

LuDyNPt 1 1 0 0 1

LuPt 1 0 0 0 1

DyNrPt 0 1 1 1 1


16Equilibrium climate sensitivity214

Climate sensitivity is conventionally defined as the temperature response to a doubling of cCO2,215

thus not involving slowly adjusting biogeochemical feedbacks28. Here, we assess climate sen-216

sitivity to a sustained radiative forcing of 3.7 Wm−2, corresponding to a nominal doubling of217

preindustrial CO2 levels. Note that the climate sensitivity is inversely proportional to the sum218

of all feedbacks 1/(λ0 +λland). Bern3D is tuned to yield a conventionally defined sensitivity of219

∼2.9◦C. We assess results for (i) a simulation with interactive land biosphere and all feedbacks220

operating (setup like ’CT-LuDyNrPt’) (ii) a simulation with interactive land biosphere where221

only feedbacks from albedo and terrestrial C storage are operating (setup like ’CT-∆CO2-∆α’)222

and (iii) a simulation without land-climate interactions (simulation setup like ’ctrl-LuDyNrPt’,223

Tab.S3, see Figure S7). The coupled Bern3D-LPX model is run for 2000 simulation years. All224

boundary conditions (Nr inputs, land use, initial atmospheric CO2, initial climatology) are set225

to preindustrial values. We chose to compare the fully coupled simulation ’CT-LuDyNrPt’ with226

’CT-∆CO2-∆α because the latter represents a setup commonly represented by latest-generation227

Earth system models (e.g., CMIP5 models).228

In our simulations, feedbacks from terrestrial C storage and albedo amplify the equilib-229

rium temperature increase by 0.4-0.5◦C, while the combination of all simulated land-climate230

interactions finally results in 3.4-3.5◦C warming, 0.6-0.7◦C (or 22-27%) above the 2.8◦C warm-231

ing when only non-land climate feedbacks are operating (see Figure S7).232

Values for λland reported here are somewhat higher than derived from the RCP8.5 simula-233

tions as presented in the article. Differences are likely linked to total C in the system. In RCP8.5234

fossil fuel combustion adds C and stimulates C storage on land, acting as a negative feedback.235

Applying present-day boundary conditions would enhance the positive feedback from N2O due236

to higher Nr loads in soils. Assumptions regarding the state of land use used for the equilibrium237

assessment further influence results. This scenario-dependence of any feedback quantification238

may be interpreted as favouring the use of scenarios with consistent future developments in239

land use and emissions of GHGs and other forcing agents.240


17

∆T [o C

]

0

1

2

3

4a ∆T (3.7 Wm−2)

ctrl2 × cCO2

CT−∆C−∆αCT−all agents

simulation year

Wm

−2K

−1

0 500 1000 1500 2000

0.0

0.1

0.2

0.3

0.4

0.5b feedback factor rCT

Figure S7: Upper panel: Global mean temperature increase in response to a radiative forcing

of 3.7 Wm−2. The black curve represents the ’ctrl’ simulation, where no feedbacks from land

are accounted for. The dotted black curve represents the temperature change in response to a

doubling of atmospheric cCO2, the “conventionally defined” climate sensitivity as referred to

in the main article. The blue range represents a setup where only terrestrial feedbacks from ∆C

and albedo are accounted for. The red range represents a setup where also eN2O and eCH4 are

operating (see also Table S3). The difference between the red and blue range is due to effects

from terrestrial eN2O and eCH4. The range of temperature response arises from the sensitiv-

ity to different climate change patterns. Abrupt temperature changes (e.g., ’ctrl’ in simulation

year 1100) are due to abrupt transitions in ocean convection and associated temperature mix-

ing. Lower panel: Total land-climate feedback factor (λland) with colors representing the same

setups as in the upper panel. All external forcings of the land (land use, Nr) and initial state

(cCO2) are preindustrial conditions.


18Carbon cycle sensitivities241

Feedback factors rC∆C and rT

∆C are directly linked to the sensitivities of C stocks to temperature242

(γ) and atmospheric cCO2 (β) and linear relationships can be established as an approximation243

26.244

rC∆C ∼ β (S16)

245

rT∆C ∼ γ (S17)

Sensitivities of C storage are commonly quantified, e.g., in model intercomparison projects29,246

and are computationally less expensive to derive as values can be calculated from offline simu-247

lations by regressing C storage changes to prescribed temperature or atmospheric cCO2. Here,248

β and γ are presented for comparison with other studies and are derived from the cCO2-land249

coupled and the climate-land coupled experiments (online, RCP8.5) as follows:250

∆CC = β ·∆cCO2 (S18)251

∆CT = γ ·∆TT (S19)

∆CC is the change in terrestrial C storage in the respective experiment (again, superscript ’C’252

represents the cCO2-land coupled and ’T’ the climate-land coupled experiment). ∆cCO2 is the253

change in atmospheric concentration of CO2 and ∆T the change in global mean temperature.254

Both sensitivities exhibit non-linearity pointing to a stronger positive feedback from land255

under high cCO2 and temperatures (Figure S8). The cCO2 sensitivity (β) is flatening out to-256

wards high cCO2 levels, while the temperature sensitivity (γ) is increasing with the magnitude257

of warming. The single most important model feature reducing β is anthropogenic land use258

change. The replacement of natural vegetation by agricultural land reduces the the ecosystem’s259

sink capacity due to shorter life time of C in grass and crop vegetation as opposed to forests. At260

the same time, land use change implies a reduction of γ due to generally smaller C stocks prone261

to temperature-driven reduction. To derive the net effect of land use change in a scenario for262

future temperature and cCO2 change, one has to turn to the feedback factor as derived above.263

For changes in cCO2 of less than 200 ppm, C-N interactions is the most important feature264

reducing β. This is likely a transient effect of initial N limitation, relieved by higher N reminer-265


19alization after the system has adopted to high cCO2 levels and increased the size of total soil266

organic N. Accounting for C-N interactions reduces the value of γ due to higher N availability267

at warmer soil temperatures. Nr inputs have minor impacts on β and γ in our model. Additional268

100 PgC are lost from peatlands under high temperatures and on long time scales leading to an269

increase in γ .

−500 0 500 1000 1500 20000

100

200

300

400

500

600

700

800

∆ Catm

[ppm]

∆ C

terr [

Pg

C]

no DyN

no LU

no peat

no Nr

standard

0 500 1000 15000.3

0.4

0.5

0.6

0.7

0.8

0.9

1

∆ Catm

[ppm]

β [

Pg

C/p

pm

]

no DyN

no LU

no peat

no Nr

standard

0 2 4 6 8 10−1200

−1000

−800

−600

−400

−200

0

200

∆ T [K]

∆ C

terr [P

gC

]

no DyN

no LU

no peat

no Nr

standard

0 2 4 6 8 10−110

−100

−90

−80

−70

−60

−50

−40

−30

−20

−10

0

∆ T [K]

γ [P

gC

/K]

no DyN

no LU

no peat

no Nr

standard

Figure S8: Upper left: Change in terrestrial C storage vs. atmospheric C (CO2). Upper right:

β vs. atmospheric C. Lower left: Change in terrestrial C storage vs. global mean temperature

change. Lower right: γ vs. global mean temperature change.270


20S4 Supplementary results271

In the following sections we provide additional documentation of the results from the offline272

simulations. Maps for changes in N2O (Figure S11) and CH4 (Figure S14) emissions and273

terrrestrial C storage (Figures S9 and S10) provide spatial information, given separately for274

each CMIP5 model’s climate input (mean over available ensemble members).275

Terrestrial C balance276

Changes in terrestrial C storage (∆C) as illustrated in Figure S9 are the result of external forc-277

ings (land use change, Nr) and the impact of changes in climate and cCO2. Figure S10 shows278

separated effects of external forcings only (top left) and changes in climate and cCO2 as the279

difference to the total effect (’CT-ctrl’). At high northern latitudes, temperature increase acts280

to relieve the limitation of plant growth by temperature and low nutrient availability and leads281

increased C storage, while at lower latitudes, warmer temperatures generally reduce soil C stor-282

age by enhancing soil C decomposition. The C balance of forest biomes (boreal and tropical) is283

sensibly affected by vegetation dynamics responding to water stress, exceedance of bioclimatic284

limits, etc. and exhibits abrupt transitions (collapse of vegetation) leading to a sharp decline in285

primary productivity and C storage.286


21

Figure S9: ∆Ctot, change in total (vegetation, litter, soil) terrestrial C storage [gC/m2], 2100-

2000 AD, in RCP8.5, from offline simulation ’CT’, and based on different CMIP5 climates.

Differences are taken from the means of the years 2006 to 2011 AD and 2095 to 2100 AD.

Brown colors represent C release from the terrestrial biosphere.


22

Figure S10: ∆Ctot, change in total (vegetation, litter, soil) terrestrial C storage [gC/m2], 2100-

2000 AD, in RCP8.5 and based on different CMIP5 climates. upper left: from offline sim-

ulation ’ctrl’ where the land model “sees” no changes in climate or CO2 and is affected only

by external forcings (land use, N-deposition, N-fertiliser). rest: difference between ’CT’ and

’ctrl’ simulation; represents effects due to changes in climate and CO2. Differences are taken

from the means of the years 2006 to 2011 AD and 2095 to 2100 AD. Brown colors represent C

release from the terrestrial biosphere.


23N2O emissions287

Simulated present-day N2O emissions from terrestrial ecosystems of 9.1 TgN2O-N/yr are within288

the range of other studies2, 20, 21, 30, 31. The spatial pattern of the N2O increase in the 21st century289

is similar for all prescribed CMIP5 climates (Figure S11). CCSM4 shows the smallest increase290

across different regions. Most of the increase in N2O emissions is from agricultural land (crop-291

land and pastures, Figure S12). The amplification of the N2O source from agricultural soils292

(from 1.4 in 1900 AD to 4.9 TgN2O-N/yr in 2005 AD) is a combination of expansion of areas293

under anthropogenic land use and an increase in fertiliser-N inputs and N-deposition per unit294

area. Due to the continuous increase of Nr inputs in RCP8.5 throughout the 21st century, N2O295

emissions from agricultural soils reach 9-11 TgN2O-N/yr by 2100 AD.296

Interestingly, the relative increase in agricultural N2O emissions is larger than the relative297

increase in anthropogenic Nr inputs to agricultural land. We define here a dimensionless N2O298

emission factor as the ratio of N2O emissions from agricultural land divided by anthropogenic299

Nr inputs on agricultural land (N-fertilisation plus N-deposition). Thus the values of the emis-300

sion factor quantified here cannot be compared directly to results of Davidson (2009)19 and301

Crutzen et al. (2008)32. Note, that other inputs of fixed N which did not see a magnitude in the302

relative increase as for N-deposition and fertiliser-N (biological N fixation or manure), are not303

accounted for here. The temporal evolution of this emission factor for simulations with climate304

change (r1, red) and a simulation without climate change (r5, red) is illustrated by Figure S13.305

Two important features of this evolution emerge: (i) A drop of the emission factor from 0.24 to306

0.04 from pre-industrial times to present. This is due to the drastic increase in anthropogenic Nr307

inputs. Nr inputs on agricultural land (N-deposition plus N-fertiliser) increase from 3 TgN/yr308

in 1850 AD to 131 at present and to 239 TgN/yr in 2100 AD in RCP8.5. Its relative increase309

is much stronger than the relative increase in N2O emissions. (ii) The divergence of emission310

factors in the 21th century for the RCP8.5 scenario, as climate change is responsible for an311

increase in the share of reactive N input lost as N2O.312


24

Figure S11: ∆eN2O[gN2O-N/m2/yr], 2100-2000 AD, in RCP8.5, from offline simulation ’CT’,

and based on different CMIP5 climates. Differences are taken from the means of the years 2006

to 2011 AD and 2095 to 2100 AD.

Figure S12: Global total N2O emissions on natural (left) and agricultural (right) land.


25

Figure S13: N2O emission factor. Defined as the ratio of N2O emissions from agricultural land

divided by Nr inputs on agricultural land. For a simulation with climate change (r1, black) and

a simulation without climate change and cCO2 changes (r7, red). RCP8.5 climates change from

all CMIP5 models is prescribed for the 21st century.


26CH4 emissions313

Modelled CH4 emissions from natural ecosystems increase from 195 at preindustrial to 219314

TgCH4/yr at present and further to 228-241 in RCP2.6 and 304-343 TgCH4/yr in RCP8.5 (Fig-315

ure 2b). Increased substrate availability for methanogenesis due to a strong stimulation of NPP,316

and faster soil turnover leads to an amplification of CH4 emissions with the sharpest increase317

in peatlands (plus 120-200%). Other CH4-related analyses with the same model are presented318

in Spahni et al. (2011)33 and Zurcher et al. (2012)34. Changes in tropical inundated wetland319

emissions are less pronounced, and perhaps underestimated in our model that does not account320

for wetland expansion under future climate change35, 36. The additional CH4 release from nat-321

ural land ecosystems is not accounted for in the climate projections in preparation of the Fifth322

Assessment Report of the Intergovernmental Panel on Climate Change24, 37. In our simulations323

cCH4 rises up to 4500 ppb in RCP8.5 by 2100 AD (see online simulation, below), about 800324

ppb more than in the RCP data.325


27

Figure S14: ∆eCH4LPX [gCH4/m2/yr], 2100-2000 AD, in RCP8.5, from offline simulation

’CT’, and based on different CMIP5 climates. Based on different CMIP5 climates. Differences

are taken from the means of the years 2006 to 2011 AD and 2095 to 2100 AD.


28Albedo326

Figure S15: ∆albedo, 2100-2000 AD, in RCP8.5. Combined effect of climate and cCO2 (upper

left) , effect of climate only (upper right), effect of cCO2 (lower left); on natural land only (no

land use). Map in lower right illustrates effect of external forcings (land use, Nr) for RCP8.5.

Differences are taken from the means of the years 2000 to 2010 AD and 2090 to 2100 AD.

Negative values (red, decreasing albedo) imply more absorbtion of shortwav radiation at the

surface, which leads to warming.


29Supplementary References327

3281. Lamarque, J.-F. et al. Global and regional evolution of short-lived radiatively-active gases329

and aerosols in the Representative Concentration Pathways. Clim. Change 109, 191–212330

(2011).331

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nitrogen offset by nitrous oxide emissions. Nature Geoscience 4, 601–605 (2011).333

3. Hurtt, G. C. et al. The underpinnings of land-use history: three centuries of global gridded334

land-use transitions, wood-harvest activity, and resulting secondary lands. Global Change335

Biol. 12, 1208–1229 (2006).336

4. Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region.337

Global Biogeochem. Cycles 23, GB2023+ (2009).338

5. Joos, F. et al. Global warming feedbacks on terrestrial carbon uptake under the Inter-339

governmental Panel on Climate Change (IPCC) emission scenarios. Global Biogeochem.340

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climate projections for twenty-first century mitigation scenarios. Climate Dynamics 33,343

737–749 (2009).344

7. Ritz, S. P., Stocker, T. F. & Joos, F. A coupled dynamical oceanenergy balance atmosphere345

model for paleoclimate studies. J. Climate 24, 349–75 (2011).346

8. Muller, S. A., Joos, F., Edwards, N. R. & Stocker, T. F. Water mass distribution and347

ventilation time scales in a cost-efficient, three-dimensional ocean model. J. Clim. 19,348

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10. Steinacher, M. Modeling changes in the global carbon cycle-climate system. Ph.D. thesis,353

University of Bern (2011).354


30

cCO2-land coupling

cN2O

cCH4

albedo

ΔC

eN2O

eCH4

Δalbedo

RF ΔTC

ΔC

eN2O eCH4

Δalbedo

cCO2

cN2O

cCH4

albedo

RF

land use N-deposition N-fertiliser

climate-land coupling

LAND

cCO2

eGHGEXT

eGHGEXT

ΔTT

Figure S16: Schematic illustration of feedbacks loops in a climate-land coupled (red back-

ground, superscript ’T’) and a cCO2-land coupled (blue background, superscript ’C’) model

setup. The land model simulates terrestrial emissions of GHGs (∆C , eN2O, eCH4) and land

surface albedo changes. Here, GHGs and albedo are referred to as forcing agents. In combi-

nation with other non-soil GHG emissions not simulated by the land model (eGHGEXT), these

determine the atmospheric GHG concentrations (cGHG) and the surface albedo, cause a radia-

tive forcing (RF) and affect climate, here represented by the global mean temperature (∆T).

In all setups, external forcings are prescribed to the land model (land use, N deposition+N fer-

tiliser=Nr) or are added directly into the atmosphere (eGHGEXT). Note that even in the ’ctrl’

setup, land use, N-deposition, and N-fertiliser affect terrestrial GHG emissions, and eGHGEXT

is added and ∆Tctrl is not zero. In cCO2-land coupled (’C’) and climate-land coupled (’T’)

experiments only changes in one variable (cCO2 in the case of ’C’ and climate/temperature in

the case of ’T’) are communicated to the land model (not shown).


31

observed/RCP cCO2

CRU/CMIP5 climate

ΔC

eN2O

eCH4

land use N-deposition N-fertiliser

LAND

eEXT

T C

cCH4

cN2O

eEXT

Figure S17: Schematic illustration of the model setup used for offline simulations. Prescribed

inputs to the land model are the evolution of land use areas, reactive nitrogen addition (N

deposition and N fertilizer), historical observed cCO224 and climate11. The land response in

forcing agents (∆C, eN2O, eCH4) are presented in Figure 3 in the main article. Letters ’C’ and

’T’ indicate which changes in drivers (cCO2 and climate) are communicated to the land model.

Simulated emissions are complemented with other sources not simulated by LPX (eEXT) to

derive cN2O and cCH4 as presented in Figure 2 in the main article. Note that the prescribed

emissions of reactive gases (CO, VOC,NOX) affect cCH4. cCO2 cannot be derived from offline

simulations as no ocean carbon cycle model is operating in this setup. Note also that in offline

simulations, the ’ctrl’ simulation sees no changes in cCO2 and climate with respect to their

preindustrial state.


32−0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4

present2100

2300

a

clim

ate

coup

led

Tfull featuresno Nrno C−N interactionsno peatlandno landuse

present2100

2300

b

cCO

2 −

land

cou

pled

C

feedback parameter r [Wm−2K−1]−0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4

present2100

2300

c

fully

cou

pled

CT

Figure S18: Feedback factor rT (a), rC (b), rCT (c) evaluated by model features. Feedback

factors represent the combined feedbacks from all forcing agents (eCO2, eN2O, eCH4, and

albedo). Quantified at three time periods: present (mean of 2000-2010 AD), 2100 (mean of

2095-2105), and 2300 (mean of 2290-2300). Rectangles represents minimum (left edge), max-

imum (right edge), and mean (middle line) of values derived from simulations with different

climate change anomaly patterns from the five CMIP5 models applied. Results are from online

simulations. © 2013 Macmillan Publishers Limited. All rights reserved.

33R

F [W

m−2

]

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

a ∆RF(N2O)

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

RF

[Wm

−2]

b ∆RF(CH4)

RF

[Wm

−2]

1900 2000 2100 2200 2300

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

c ∆RF(CO2)

1900 2000 2100 2200 2300

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

RF

[Wm

−2]

d ∆RF(albedo)

RCP 2.6RCP 8.5

Figure S19: Additional radiative forcing due the higher N2O (a), CH4 (b), CO2 (c), and albedo

(d) in the fully coupled simulation compared to the ctrl simulation. ∆RF is caused by terres-

trial feedbacks. Note that changes in cCH4concentrations affect stratospheric cH2O and cO3

and hence invoke an indirect radiative forcing. Resulting radiative forcings are included in

RF(CH4). The sum ∆RF(N2O+CH4) is presented in Figure 4 in the main article (bottom left).


34

year AD

cCO

2 [p

pmv]

1900 2000 2100 2200 2300

0

500

1000

1500

2000

2500

cCO2

RCP 2.6RCP 8.5

Figure S20: Simulated CO2 concentrations in the fully coupled (range) and the ctrl (dashed

lines) simulations for RCP2.6 (blue) and RCP8.5 (red). Higher (lower) concentrations in the

fully coupled simulation RCP8.5 (RCP2.6) are due to enhanced C sources/reduced C sinks in

response to changes in both climate and CO2.


35−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

a

fully

cou

pled

CT

present2100

2300

b

clim

ate

− la

nd c

oupl

edT

present2100

2300

albedo∆CN2OCH4

feedback factor r [Wm−2K−1]−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

c

cCO

2 −

land

cou

pled

Cpresent

21002300

Figure S21: Feedbacks factors evaluated from RCP2.6. (a) Feedback factors in fully coupled

simulations rCTi , with i = CO2,N2O,CH4, albedo. (b) Feedback factors in climate-land cou-

pled simulations rTi . (c) Feedback factors in cCO2-land coupled simulations rC

i . Quantified at

three time periods: present (mean of 2000-2010 AD), 2100 (mean of 2095-2105), and 2300

(mean of 2290-2300). Rectangles represents minimum (left edge), maximum (right edge), and

mean (middle line) of values derived from simulations with different climate change anomaly

patterns from the five CMIP5 models applied. Results are from online simulations.© 2013 Macmillan Publishers Limited. All rights reserved.

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multiple greenhouse-gas feedbacks from the land biosphere under future climate change scenarios

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