Evaluation of the terrestrial carbon cycle, future plant geography …empslocal.ex.ac.uk/people/staff/pmc205/papers/2008/GCB... · 2008. 6. 23. · Dynamic Global Vegetation Models

Evaluation of the terrestrial carbon cycle, future plantgeography and climate-carbon cycle feedbacks using fiveDynamic Global Vegetation Models (DGVMs)

S . S I T C H *, C . H U N T I N G F O R D w , N . G E D N E Y *, P. E . L E V Y z, M . L O M A S § , S . L . P I A O } ,

R . B E T T S k, P. C I A I S } , P. C O X **, P. F R I E D L I N G S T E I N } , C . D . J O N E S k, I . C . P R E N T I C E w wand F. I . W O O D WA R D §

*Met Office Hadley Centre, JCHMR, Maclean Building, Wallingford OX10 8BB, UK, wCentre for Ecology and Hydrology

Wallingford, Maclean Building, Wallingford OX10 8BB, UK, zCentre for Ecology and Hydrology Bush Estate, Penicuik, Midlothian

EH26 0QB, UK, §Department of Animal & Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK, }IPSL/LSCE, Unite

mixte 1572 CEA-CNRS, CE-Saclay, Bat 701, 91191 Gif sur Yvette, France, kMet Office Hadley Centre, Fitzroy Road, Exeter EX1

3PB, UK, **School of Engineering, Computer Science and Mathematics, University of Exeter, Exeter ES4 4QF, UK, wwQUEST,

Department of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, Bristol BS8 1RJ, UK

Abstract

This study tests the ability of five Dynamic Global Vegetation Models (DGVMs), forced

with observed climatology and atmospheric CO2, to model the contemporary global

carbon cycle. The DGVMs are also coupled to a fast ‘climate analogue model’, based on

the Hadley Centre General Circulation Model (GCM), and run into the future for four

Special Report Emission Scenarios (SRES): A1FI, A2, B1, B2. Results show that all

DGVMs are consistent with the contemporary global land carbon budget. Under the

more extreme projections of future environmental change, the responses of the DGVMs

diverge markedly. In particular, large uncertainties are associated with the response of

tropical vegetation to drought and boreal ecosystems to elevated temperatures and

changing soil moisture status. The DGVMs show more divergence in their response to

regional changes in climate than to increases in atmospheric CO2 content. All models

simulate a release of land carbon in response to climate, when physiological effects of

elevated atmospheric CO2 on plant production are not considered, implying a positive

terrestrial climate-carbon cycle feedback. All DGVMs simulate a reduction in global net

primary production (NPP) and a decrease in soil residence time in the tropics and extra-

tropics in response to future climate. When both counteracting effects of climate and

atmospheric CO2 on ecosystem function are considered, all the DGVMs simulate

cumulative net land carbon uptake over the 21st century for the four SRES emission

scenarios. However, for the most extreme A1FI emissions scenario, three out of five

DGVMs simulate an annual net source of CO2 from the land to the atmosphere in the

final decades of the 21st century. For this scenario, cumulative land uptake differs by

494 Pg C among DGVMs over the 21st century. This uncertainty is equivalent to over 50

years of anthropogenic emissions at current levels.

Keywords: carbon cycle feedbacks, biogeography, DGVM

Received 16 March 2007; revised version received 20 December 2007 and accepted 17 January 2008

Introduction

In recent years, much attention has been placed on the

role of terrestrial biosphere dynamics in the climate

system (Cramer et al., 2001), and the possibility of

anthropogenic climate change inducing major altera-

tions in terrestrial ecosystems. Terrestrial ecosystems

may become a source of CO2 under imposed climate

change and, thus, act to accelerate the build-up of

atmospheric CO2 concentrations [see Cox et al. (2000)].Correspondence: S. Sitch, e-mail: [email protected]

Global Change Biology (2008) 14, 1–25, doi: 10.1111/j.1365-2486.2008.01626.x

r 2008 The AuthorsJournal compilation r 2008 Blackwell Publishing Ltd 1

This has major policy implications for climate change

mitigation and reduces ‘permissible’ emissions to

achieve stabilization (Jones et al., 2006).

Cox et al. (2000) ran the TRIFFID Dynamic Global

Vegetation Model (DGVM) coupled with the low ocean

resolution Hadley Centre General Circulation Model

(GCM), HadCM3LC, in a fully interactive carbon cycle

experiment for one future emission scenario (IS92a).

They found a very large climate-carbon feedback,

caused in particular by enhanced midlatitude soil de-

composition in response to future warming, but also

from ‘dieback’ of Amazon forests (Betts et al., 2004; Cox

et al., 2004) in response to both future warming and

drying. Dufresne et al. (2002) used the IPSL GCM and

the simple land carbon cycle model SLAVE to perform a

similar analysis and found a much smaller climate-

carbon feedback.

The C4MIP model intercomparison (Friedlingstein

et al., 2006) has extended this work by quantifying the

uncertainty in future climate-carbon cycle feedbacks

among a large set of 11 climate-carbon cycle models

for the Special Report Emission Scenarios (SRES) A2

emissions scenario. All models predict a reduction in

the combined efficiency of the ocean and land carbon

cycles to absorb anthropogenic carbon emissions due to

future climate change. By 2100, this translates to an

extra 20–200 ppmv of anthropogenic CO2 remaining in

the atmosphere, as compared with CO2 scenarios based

on the assumption that the current fraction of emitted

CO2 drawn down naturally into the oceans and land

biosphere remains constant into the future (Friedling-

stein et al., 2006). The result corresponds to an addi-

tional climate warming of 0.1–1.5 1C (Friedlingstein

et al., 2006). The majority of models attribute these

changes predominantly to the land carbon cycle, and

in particular to reductions in land carbon uptake in

the tropics. However, there was no consensus among

models on the relative roles of changes in net primary

productivity (NPP) and heterotrophic respiration (RH)

(Friedlingstein et al., 2006).

In this study, we make a more controlled comparison

between the responses of five different DGVMs, by

exposing each of them to the same set of climate change

scenarios. It is unfeasible to couple and run multiple

DGVMs within a single GCM, and so we coupled the

five DGVMs to a computationally efficient ‘GCM ana-

logue model’ (AM) and a simple ocean carbon cycle

model, both calibrated against the climate change

simulated by HadCM3LC (Huntingford & Cox, 2000;

Huntingford et al., 2004). Initially, the DGVMs are run

over the historical period 1901–2002 forced with

observed monthly climatology and atmospheric CO2

content (hereafter referred to as ‘Offline simulations’).

Then, using the AM system, a second set of simulations

is conducted over the period, 1860–2100 using four

SRES emission scenarios and a common set of patterns

of climate change from HadCM3LC GCM (hereafter

referred to as ‘Coupled simulations’). This study ac-

counts for biogeochemical feedbacks. Biogeophysical

feedbacks associated with individual DGVMs, although

important, are beyond the scope of the present study.

We address the following questions. Are DGVMs able

to simulate the contemporary global land carbon cycle?

To what extent do the DGVMs agree on their global and

regional responses to future changes in climate and

atmospheric composition? How uncertain is the cli-

mate-carbon cycle feedback? Can specific ecological

processes be identified as the source for the overall

uncertainties in DGVM response? What are the relative

uncertainties in future atmospheric CO2 associated with

different choices of DGVM and anthropogenic emission

scenario?

Methods

The IMOGEN climate-carbon cycle system

The AM consists of a global thermal two-box model

which calculates both global mean temperature rise

over land and surface oceans, in response to increases

in atmospheric radiative forcing associated with chan-

ging atmospheric greenhouse gas concentrations. The

land value then multiplies a set of patterns across each

land grid-box and each month, for the key variables

determining ‘weather’ and associated land surface re-

sponse (e.g. temperature, humidity, windspeed, short-

wave and longwave radiative fluxes). The AM

capitalizes on the analysis of Hadley GCM output

that, to a good approximation, reveals that many as-

pects of surface climatology vary linearly to changes in

global mean temperature response over land (Hunting-

ford & Cox, 2000) – it is this observation that allows

the possibility to extrapolate existing Hadley GCM

simulations to a range of different pathways in atmo-

spheric greenhouse gas concentrations. For this reason,

the spatial patterns capturing such linearity can be

defined from a small number of HadCM3 simulations

(Huntingford & Cox, 2000); the AM defines ‘patterns of

change per degree of global warming over land’ for

temperature at 1.5 m (K K�1), relative humidity at

1.5 m (%K�1), windspeed at 10.0 m (m s�1 K�1), down-

ward longwave radiation (W m�2 K�1), downward

shortwave radiation (W m�2 K�1), precipitation rate

(mm day�1 K�1), diurnal temperature range (K) and

surface pressure (hPa K�1). The scaling factor for these

patterns (i.e. the change in global mean temperature

over land, calculated using the thermal two-box climate

model) is also calibrated to HadCM3 output. Hourly

2 S . S I T C H et al.

r 2008 The AuthorsJournal compilation r 2008 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2008.01626.x

surface climate is derived by temporal disaggregation

of the monthly means (including conversion of precipi-

tation into either rainfall or snow fall) based on the

diurnal temperature range and observed fraction of wet

days. Interannual variability of the GCM climate is not

presently included in the AM.

Predicted spatial and seasonal changes in surface

climate [for a range of different future trajectories in

atmospheric greenhouse gases (GHGs)] are important

for impacts assessment. For this reason, the AM was

extended by coupling it to the Met Office land surface

model that includes the TRIFFID DGVM for an identical

land grid structure as HadCM3. In addition, an extra

global box model describes the oceanic uptake of atmo-

spheric CO2. The flux is linear in the gradient between

atmospheric and surface oceanic CO2 concentrations;

the latter related to both the global mean oceanic mixed-

layer temperature and concentration of dissolved inor-

ganic carbon in the surface water, itself a function of the

history of CO2 drawdown. The dependence on previous

fluxes of atmospheric-ocean carbon dioxide is based on

the model of Joos et al. (1996), with modelled depen-

dence on oceanic temperature changes given by Taka-

hashi et al. (1993); the equations are described in full

in the Appendix of Huntingford et al. (2004). The

resultant model structure is called IMOGEN (Integrated

Model Of Global Effects of climatic aNomalies); see

Huntingford et al. (2004).

IMOGEN is forced by a prescribed emissions scenario

of CO2. Annual atmospheric CO2 concentrations are

updated each year accounting for annual anthropogenic

CO2 emissions and changes in global land and ocean

carbon storage as calculated by TRIFFID and the ocean

box model, respectively. The concentration of non-CO2

GHGs are prescribed as a function of time for each

emission scenario.

IMOGEN provides an impacts modelling system for a

broad range of different emission trajectories, based on

changes in surface climate predicted by HadGCM3LC,

but without the need to rerun the full GCM. Here, we

use this system to investigate the influence of different

land carbon cycle descriptions on the global carbon

cycle by inserting alternative DGVMs into the IMOGEN

structure.

Land carbon cycle models (DGVMs)

There is now a range of well-established DGVMs oper-

ated by different ecosystem research groups, but with

alternative parameterizations and diverse inclusion of

processes (Prentice et al., 2007). Five DGVMs are applied

here: the HyLand (HYL) model is based on the Hybrid

DGVM (Friend et al., 1997; Friend & White, 2000) with

modifications as documented in Levy et al. (2004);

the Lund–Potsdam–Jena DGVM (LPJ) (Sitch et al.,

2003), with the updated hydrology of Gerten et al.

(2004); ORCHIDEE (ORC) as described in Krinner

et al. (2005); Sheffield-DGVM (SHE) (Woodward et al.,

1995; Woodward & Lomas, 2004) and TRIFFID (TRI)

(Cox, 2001). A description of the DGVMs used in this

intercomparison is given in Table 1. In this study,

we focus on two aspects of land surface modelling:

vegetation dynamics and the carbon cycle. However,

these models have also been developed to simulate

soil hydrological processes and the exchange of water

between the land and the atmosphere. In the case

of land-surface models coupled to GCMs, energy

exchange between the land surface and atmosphere is

also simulated.

Datasets

Atmospheric composition and climate. In the offline

historical simulations (i.e. forcing the DGVMs with

specified surface conditions), we use annual global

atmospheric CO2 concentrations for the period 1901–

2002 based on data from ice-core records and

atmospheric observations (Keeling & Whorf, 2005).

These simulations use monthly climatology for the

period 1901–2002 from the University of East Anglia

Climate Research Unit (CRU) gridded dataset (New et al.,

2000), based on global collection of measurements. These

measurements are aggregated to a resolution of 3.751

longitude� 2.51 latitude, in keeping with output from

HadCM3LC and associated patterns of the GCM AM.

For our coupled climate-carbon cycle simulations,

IMOGEN instead requires prescribed fossil fuel and

land-use emissions. Such emissions of CO2 are based on

historical records of fossil fuel burning and land-use

emissions from Marland et al. (2003) and Houghton

(2003), respectively, for the period 1860–1999. The

Intergovernmental Panel on Climate Change (IPCC)

emission scenarios of A1FI, A2, B1, B2 (Nakicenovic

et al., 2000) are used for the period 2000–2100. Radiative

forcing from non-CO2 GHGs, as defined for the A1FI, A2,

B1, B2 scenarios (Nakicenovic et al., 2000) are added to the

forcing due to raised atmospheric carbon dioxide

concentrations within IMOGEN. At present, the effect

of historical and future sulphate aerosols on climate is

not considered.

In these coupled climate-carbon cycle simulations, the

AM climate uses the ‘monthly persistent anomaly patterns’

multiplied by the calculation of DTl, and then such

anomalies added to an initial climatology. The initial

climatology for these simulations is based on an updated

version of the Leemans & Cramer (1991) monthly means

for period 1931–1960, as modified by Friend (1998) (known

hereafter as the ‘CL dataset’).

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Con

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The AM climate patterns are derived from the

HadCM3LC coupled ocean–atmosphere GCM [see

Gordon et al. (2000) for summary details], with interactive

ocean and land carbon cycles (Cox et al., 2000).

The AM patterns of seasonal temperature and

precipitation change are shown in Figs 1 and 2. For a unit

change in future global mean temperature over land DTl,

HadCM3LC simulates large temperature increases: year-

round across Amazonia; during the nongrowing season

across northern hemisphere tundra ecosystems and during

the growing season across the North American and Asian

boreal forests, temperate North America and northern

hemisphere Mediterranean ecosystems. HadCM3LC

simulates a large year-round decrease in rainfall rate

across the Amazonian rainforest and seasonal forests of

North-East Brazil (Fig. 2) in the future (Cox et al., 2004).

HadCM3LC simulates decreases in summer rainfall across

temperate, boreal and Mediterranean ecosystems in North

America and Eurasia. The rainfall rate increases across

many of these ecosystems during the rest of the year. Year-

round decreases in rainfall are simulated across the water-

limited ecosystems of Australia and Southern Africa.

Rainfall decreases across the western Sahel during the

northern-hemisphere summer, and year-round increases

are simulated for the tropical rainforests of Central Africa.

Many of these changes can be expected to alter present day

terrestrial ecosystem structure and function.

Nino-3 and ocean flux data. Simulated interannual

variation (IAV) in CO2 by DGVMs is correlated

against the observed Nino-3 index, a measure of the

ENSO cycle. The Nino-3 index is the mean sea surface

temperature (SST) anomaly in the region 51N to 51S,

150–901W derived from a climate dataset of SST (Rayner

et al., 2000). To estimate the IAV in ‘natural CO2’ in

response to ENSO, a dataset of modelled monthly ocean

CO2 fluxes (Buitenhuis et al., 2006) for the period 1955–

2003 were added to the land fluxes from the DGVMs. In

the absence of available data, IAV in ocean fluxes was

set to zero between 1901 and 1954.

Experimental design

Model initialization. For initialization of the forced

contemporary carbon cycle simulation, we used the

mean monthly fields over the first decade from the

CRU dataset. The mean observed climate (the CL

dataset) was used to initialize terrestrial carbon pools

and vegetation structure at their preindustrial

equilibrium states for the coupled simulations. In both

sets of simulations, LPJ used monthly climatology

selected from a random sequence of years between

1901 and 1930 from the CRU dataset (New et al., 2000)Tab

le1.

(Con

td.)

Hy

Lan

d(H

YL

)L

un

d–P

ots

dam

–Jen

a(L

PJ)

OR

CH

IDE

E(O

RC

)S

hef

fiel

d-D

GV

M(S

HE

)T

RIF

FID

(TR

I)

Veg

etat

ion

dy

nam

ics

Co

mp

etit

ion

Co

mp

etit

ion

bet

wee

n

PF

Ts

for

lig

ht

No

nh

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og

eneo

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edco

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etit

ion

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ayer

),H

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(2la

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s)

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nh

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-

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etit

ion

for

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(1-l

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),H

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(2la

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s)

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s

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on

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Ts

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rtal

ity

Dep

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ent

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carb

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po

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erm

inis

tic

bas

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Pre

scri

bed

dis

turb

ance

rate

for

each

PF

T



for model initialization. Interannual varying climate is

required by LPJ to simulate realistic fire dynamics. Each

DGVM is allowed to calculate its own vegetation

distribution. SHE adopted the Global Land Cover

map to describe PFT fractions (GLC 2000, Bartholome

et al., 2002) and assumed fixed vegetation throughout

the transient simulations. Three experiments were

conducted.

Offline historical carbon cycle. In the first set of

experiments, each DGVM is run from its preindustrial

equilibrium at 1901 over the historical period 1901–2002

using observed fields of monthly climatology and

annual global atmospheric CO2 concentration, at the

GCM grid resolution of 3.751 longitude� 2.51 latitude.

No land or ocean carbon cycle feedbacks are included.

Coupled climate-carbon cycle. Each DGVM is run from its

preindustrial equilibrium at 1860 over the historical and

future period 1860–2100 at the GCM grid resolution.

Once in their equilibrium state, the DGVMs are then

driven within the IMOGEN framework using climate

anomalies consistent with HadCM3LC. This is

undertaken for four IPCC SRES fossil fuel and land-

use emission scenarios (A1FI, A2, B1, B2) and radiative

forcing from non-CO2 GHGs. For LPJ, climate

anomalies were added to a random sequence of 30

years baseline climatology throughout the transient

simulation. Land-use emissions are treated as external,

and do not affect directly vegetation area and carbon

pools. Although land-cover change is important for

both climate and the global carbon cycle (Brovkin

et al., 1999; Betts, 2000; Gitz & Ciais, 2003; Brovkin

et al., 2004; Sitch et al., 2005), inclusion of explicit land-

use and land cover changes are beyond the scope of the

current study. However, we go beyond the ground-

breaking intercomparison of Cramer et al. (2001) by

including and diagnosing climate-carbon cycle

feedbacks, and by spanning a wider range of emission

scenarios.

Prescribed climate. In order to quantify future climate-

carbon cycle feedbacks an additional ‘prescribed-climate’

experiment is needed. Here, the ‘Coupled Climate-

Carbon Cycle’ simulations are repeated assuming a

prescribed climate. The observed climate dataset used

in the spin-up is prescribed throughout the transient

period, 1860–2099 (i.e. radiative forcing of GHGs, both

CO2 and non-CO2, are kept constant at 1860 levels), but

the vegetation does respond directly to CO2 increases,

0.6 0.8 0.95 1.05 1.2 1.4

180° 90°W 0° 90°E

90°N

60°N

30°N

0°

30°S

0.6 0.8 0.95 1.05 1.2 1.4

180° 90°W 0° 90°E

90°N

60°N

30°N

0°

30°S

JJA 1.5 m temperature change patterns

DJF 1.5 m temperature change patterns

0.6 0.8 0.95 1.05 1.2 1.4

180° 90°W 0° 90°E

0°

90°N

60°N

30°N

30°S

MAM 1.5 m temperature change patterns

0.6 0.8 0.95 1.05 1.2 1.4

180° 90°W 0° 90°E

90°N

60°N

30°N

0°

30°S

SON 1.5 m temperature change patterns

Fig. 1 Mean seasonal patterns of 1.5 m land temperature change per unit increase in global land temperature (winter – DJF, spring –

MAM, summer – JJA, autumn – SON).



through ‘fertilization’ effects. The difference in CO2

concentrations between the ‘Coupled’ and ‘Prescribed

Climate’ experiments gives the magnitude of the climate-

carbon cycle feedback.

Climate-carbon cycle feedback analysis

Following the methodology of Friedlingstein et al.

(2003), the carbon cycle feedback gain, g, can be defined

as follows:

g ¼ 1� DCOp

2

DCOc

2

� �; ð1Þ

where DCOc2 and DCO

p2 are the changes in atmospheric

CO2 mixing ratios between 2099 and 1860 for the

coupled climate-carbon cycle and the ‘prescribed

climate’ simulations, respectively. Hence, a positive

value of g indicates a positive feedback of the climate

system, i.e. the coupled system results in more atmo-

spheric CO2.

A second metric of climate feedback strength can also

be defined following Friedlingstein et al. (2003, 2006),

whereby the change in land carbon storage can be

defined as a dependence on direct CO2 forcing and

climate change, here taken as global temperature

change, thus

DCcL ¼ bLDCOc

2 þ gLDTc; ð2Þ

where DCcL (Pg C) is the change in land carbon storage

due to an increase in atmospheric CO2 concentration of

DCOc2 (ppmv) in the coupled simulation and a tempera-

ture increase of DTc (K), bL is the global land carbon

sensitivity to atmospheric CO2 and gL is the global land

carbon sensitivity to climate change.

For the ‘prescribed climate’ simulation, it follows

that:

DCpL ¼ bLDCO

p2 ; ð3Þ

where DCpL (Pg C) is the change in land carbon storage

due to an increase in atmospheric CO2 concentration of

DCOp2 (ppmv) in the ‘prescribed climate’ simulation.

From Eqns (1) & (2),

gL ¼DCc

L � DCpL DCOc

2=DCOp2

� �DTc

; ð4Þ

where the numerator represents the ‘climate alone’

impact on land carbon uptake (Friedlingstein et al.,

2006).

180° 90°W 0° 90°E

90°N

60°N

30°N

0°

30°S

−0.2 −0.1 −0.02 0.02 0.1 0.2

180° 90°W 0° 90°E

90°N

60°N

30°N

0°

30°S

−0.2 −0.1 −0.02 0.02 0.1 0.2

180° 90°W 0° 90°E

0°

90°N

60°N

30°N

30°S

−0.2 −0.1 −0.02 0.02 0.1 0.2

180° 90°W 0° 90°E

0°

90°N

60°N

30°N

30°S

−0.2 −0.1 −0.02 0.02 0.1 0.2

Fig. 2 Mean seasonal patterns of rainfall change on land, units are in mm day�1 K�1 (winter – DJF, spring – MAM, summer – JJA,

autumn – SON).



Results

Contemporary carbon cycle

The results show all DGVMs to be broadly consistent

with decadal budgets of the global land carbon cycle

(Prentice et al., 2001) when forced with observed

monthly climatology (Table 2).

Over the 1980s, DGVMs simulate global mean land–

atmosphere fluxes, also known as net ecosystem ex-

change, NEE (RH�NPP; a negative sign indicates a

land uptake of carbon), of between �1.32 and

�1.80 Pg C yr�1, both close to the IPCC mean value of

�1.9 and within the range of �3.8 to 0.3 Pg C yr�1

(Prentice et al., 2001).

Likewise for the 1990s, simulated land–atmosphere

fluxes of between �1.52 and �2.75 Pg C yr�1 are close

to the IPCC mean value of �2.6 and range of �4.3 to

�1.0 Pg C yr�1 (Prentice et al., 2001). Also, DGVMs simu-

late a greater land carbon uptake in the 1990s than

during the 1980s, in agreement with IPCC estimates,

with global land uptake shared between tropical and

extra-tropical regions. DGVM estimates of land–atmo-

sphere fluxes do not span the whole IPCC range, and are

generally less negative compared with IPCC. This may

be due to sinks related to northern forest regrowth and

nitrogen deposition that are not included in this study.

The DGVMs are also able to simulate the correct

global response to ENSO-driven interannual climate

variability (Fig. 3; lower right panel), in agreement with

earlier studies (Tian et al., 1998; Jones et al., 2001; Peylin

et al., 2005). Years with anomalous increases in the

atmospheric CO2 growth rate are synonymous with

the El Nino phenomenon (e.g. 1983, 1987, early 1990s,

1998), and correspond to peaks in global land–atmo-

sphere exchange, and visa versa during La Nina years

(e.g. 1985, 1989, 1996, 1999) (Fig. 3; lower right panel).

For each DGVM, the regression of interannual

anomalies in ‘natural CO2’ against annual mean Nino-

3 index are plotted in Fig. 4, where anomalies in the

‘natural CO2’ flux are calculated as the sum of the

annual land (from DGVMs) and ocean carbon fluxes

(Buitenhuis et al., 2006) subtracting the mean annual

flux over the previous decade.

Correlations are significant at the 95% confidence

level. The slope of the regression represents the sensi-

tivity of the biosphere to IAV in climate (see Table 2).

Gradients range from 0.27 � 0.06 ppm yr�11C�1 for

HYL to 0.81 � 0.14 ppm yr�11C�1 for SHE, with inter-

mediate values of 0.32 � 0.09, 0.42 � 0.09 and

0.56 � 0.10 ppm yr�11C�1 for LPJ, ORC and TRI, re-

spectively. The error is calculated as the 95% confidence

interval of the regression and is shown in the figure as

dotted lines. For the period 1966–1996, excluding years

1983, 1992 and 1993 (years strongly affected by volcanic

eruption), Jones et al. (2001), estimated an observed

slope in the regression between observed CO2 anoma-

lies at Mauna Loa and Nino-3 index of

0.51 � 0.09 ppm yr�11C�1. However, model slopes do

not account for atmospheric transport and, therefore,

may not be comparable with the observed slope as there

is evidence that IAV in winds is non-negligible (Darga-

ville et al., 2000).

Future atmospheric CO2

Results indicate large variations in projected future

atmospheric CO2 concentration associated with uncer-

tainties in the terrestrial biosphere response to changing

climatic conditions (Fig. 5, Table 3). By 2100, atmo-

spheric CO2 concentrations differ by up to 246 ppmv

among DGVMs for the coupled simulations with the

A1FI scenario (Table 3). The LPJ and TRI simulate the

highest future CO2 concentrations across all four SRES

scenarios.

With prescribed climate (Fig. 5; top panel), the inter-

model spread is relatively small, and smaller than the

differences between SRES scenarios. This indicates a

robust behaviour of DGVMs in the way they depict CO2

fertilization and turnover rates. CO2 is lower for each

scenario than in the coupled simulations. In the coupled

climate-carbon cycle simulation (Fig. 5; bottom panel),

a larger spread among DGVMs is seen compared with

the prescribed climate simulations (Fig. 5; top panel),

illustrating that DGVMs are less robust in the way

they respond to climate. However, although the inter-

model spread increases, the CO2 range is still domi-

nated by the scenario differences (i.e. there is little

overlap between the spread bars on the right of the

bottom panel).

Table 2 Global land carbon budgets for the 1980s and 1990s,

expressed as decadal mean land–atmosphere exchange (Rh-

NPP), units are Pg C yr�1, and the simulated cumulative land

uptake from 1958 to 2002 in Pg C

1980s 1990s

1958–

2002

IPCC Residual Land Sink

Prentice et al. (2001)

�1.9 (�3.8

to 0.3)

�2.6 (�4.3

to �1.0)

Model

HyLand (HYL) �1.67 �2.39 71.5

Lund–Potsdam–Jena (LPJ) �1.32 �1.52 67.7

ORCHIDEE (ORC) �1.58 �2.21 81.4

Sheffield-DGVM (SHE) �1.80 �2.75 85.3

TRIFFID (TRI) �1.62 �2.47 110.1

IPCC, Intergovernmental Panel on Climate Change.



−3 −2 −1 0 1 2 3

3210

−1−2−3

HYL

−3 −2 −1 0 1 2 3

3210

−1−2−3

LPJ

Mean Nino-3 (°C)

−3 −2 −1 0 1 2 3

3

21

0−1−2−3

TRI

Mean Nino-3 (°C)

Mean Nino-3 (°C)

−3 −2 −1 0 1 2 3

3

21

0−1−2−3

ORC

Mean Nino-3 (°C)

Mean Nino-3 (°C)

−3 −2 −1 0 1 2 3

3210

−1−2−3

SHE

~

~

~

~

~

∆ C

O

(p.p

.m.v

. yr

)

∆ C

O

(p.p

.m.v

. yr

)∆

CO

(p

.p.m

.v. y

r )

∆ C

O

(p.p

.m.v

. yr

)∆

CO

(p

.p.m

.v. y

r )

Fig. 4 Regression of simulated interannual variability (IAV) in ‘Natural CO2’ (ppm) against annual mean Nino-3 temperature anomaly

( 1C) for HyLand (HYL), Lund–Potsdam–Jena (LPJ), ORCHIDEE (ORC), Sheffield (SHE) and TRIFFID (TRI).

1890 1910 1930 1950 1970 1990 2010

Year

1890 1910 1930 1950 1970 1990 2010

Year

280

300

320

340

360

380

Glo

bal m

ean

land

tem

pera

ture

(°C

)

12.0

12.5

13.0

13.5

14.0

1890 1910 1930 1950 1970 1990 2010Year

Mea

n la

nd p

reci

pita

tion

(mm

yr

)

850

800

750

700

1890 1910 1930 1950 1970 1990 2010

YearLa

nd-a

tmos

pher

eex

chan

ge (

Pg

C y

r )

−10

−5

0

5

HYLLPJ

ORCSHETRI

CO

con

cent

ratio

n (p

.p.m

.v)

Fig. 3 Global mean land climatology (temperature, 1C, red; precipitation, mm yr�1, blue), atmospheric CO2 content (black) and

simulated land–atmosphere exchange over the 20th century by HyLand (HYL, black), Lund–Potsdam–Jena (LPJ, yellow), ORCHIDEE

(ORC, blue), Sheffield (SHE, green), and TRIFFID (TRI, red). Red and blue dashes represent periods of strong El Nino (red) and La Nina

(blue), respectively. Linear regressions are also plotted through the temperature and precipitation data.



HYL simulates the lowest CO2 concentrations for SRES

B1 (Table 3); however, the model simulates the median

concentration for the extreme emissions scenario, A1FI.

This points to the potential for a highly nonlinear re-

sponse, and possible tipping points in terrestrial bio-

sphere function to extreme climate change.

Future global land carbon cycle

The magnitude of future land uptake varies markedly

among DGVMs (Fig. 6). Note, only LPJ is run with IAVs

in climate (see ‘Methods’).

All DGVMs simulate a positive cumulative net car-

bon uptake by 2099 in response to changes in future

climate and atmospheric CO2 composition. All the

models simulate peak annual carbon uptake in the

mid-2050s and drop thereafter. This general shape

seems common to all DGVMs and scenarios.

Two models, ORC and SHE, simulate large increases

in vegetation biomass and moderate increases in soil

stocks (Fig. 6; middle and lower panels), whereas HYL

and TRI simulate increases in only vegetation and soil,

respectively. HYL simulates the largest gain in vegeta-

tion carbon in all SRES scenarios (367 Pg C for A1FI

scenario; Table 3), but alongside LPJ, the lowest soil

carbon gains among DGVMs. Indeed, LPJ simulates net

losses in soil carbon under all scenarios, whereas HYL

simulates net losses only in the two more extreme SRES

scenarios, A1FI and A2. This compares with TRI that

simulates lowest gains in vegetation carbon (and net

losses for A1FI and A2) and only moderate gains in soil

carbon over the period.

LPJ and SHE provided fields of simulated natural

biomass burning. Emissions from wildfires are simu-

lated to increase from 1.6 and 4.2 Pg C yr�1 for SHE and

LPJ, respectively, to 6.3 and 7.5 Pg C yr�1 by 2100 in

response to changing environmental conditions for the

A1FI scenario. These increases are largely attributed to

an increase in standing biomass and an increase in

wildfire frequency over Amazonia in response to future

warming and drought.

Regional land carbon cycle and vegetation dynamics

There is a general consensus among the DGVMs in

terms of the qualitative regional response of vegetation

stocks to changing climate and atmospheric composi-

tion (Fig. 7).

All models simulate a decrease in vegetation carbon

over Amazonia (Fig. 7), in response to the reduction in

precipitation predicted by HadCM3LC. TRI simulates

the strongest Amazon dieback, with woody vegetation

replaced by herbaceous plants (Fig. 8).

1400

1200

1000

800

600

400

CO

2 (p

.p.m

)

1400

1200

1000

800

600

400C

O2

(p.p

.m)

1900 1950 2000 2050 2100Year

1900 1950 2000 2050 2100Year

Prescribed climate

Coupled All DGVMall SRES envelop(light grey)

DGVM mean all SRES envelop(dark grey)

Bars shows therange in 2100produced byseveral DGVM

Fig. 5 Global atmospheric CO2 mixing ratios (ppmv) for the ‘Prescribed Climate’ (top panel) and ‘Coupled’ (bottom panel) simulation,

respectively. Coloured lines represent the mean across all five Dynamic Global Vegetation Models (DGVMs) for each Special Report Emission

Scenarios (SRES) scenario (yellow – A1FI, red – A2, green – B1, blue – B2). The bars show the range among five DGVMs for each scenario.



All DGVMs simulate increases in vegetation carbon

over tundra ecosystems, in response to climate warm-

ing, with longer growing seasons and elevated ambient

CO2 levels all of which stimulate plant production.

ORC, TRI and LPJ simulate increasing woody coverage

in the tundra, in agreement with observational trends in

Alaska (Silapaswan et al., 2001; Sturm et al., 2001; Stow

et al., 2004; Sitch et al., 2007). LPJ simulates a marked

decrease in vegetation cover over boreal regions, with

boreal evergreen forest replaced by deciduous woody

and herbaceous plants by 2099 in the A1FI SRES sce-

nario (Fig. 8). There is less agreement in simulated

changes in soil carbon stocks (Fig. 7). ORC and TRI

simulate large increases in soil carbon storage in high-

northern latitudes, whereas SHE and HYL simulate

only moderate increases, and LPJ a strong decrease.

HYL, LPJ and TRI simulate decreases in soil carbon

across Amazonia, whereas ORC and SHE simulate

small increases.

Although the global responses of TRI and LPJ in

terms of land uptake are similar (Fig. 6), the underlying

regional responses are markedly different. The TRI

global response is due to large decreases in vegetation

and soil carbon in the tropics, counter-balanced by large

carbon uptake in high-latitude ecosystems. Midlati-

tudes see a reduction in soil stocks.

LPJ simulates only a moderate Amazon dieback, and

a large reduction in boreal forest coverage and large

high-latitude losses in soil carbon. The high initial

estimates of boreal forest carbon stocks in LPJ can partly

explain the strong reduction in storage under very

strong warming accompanied by severe summer

drought. ORC simulates a reduction in vegetation car-

bon in the temperate–boreal ecotone in Europe with

replacement of evergreen forests by deciduous vegeta-

tion. HYL simulates large carbon uptake in all ecosys-

tems except over Amazonia, where, similar to TRI, the

DGVM simulates a reduction in both vegetation and

soil stocks. ORC and SHE both simulate only moderate

decreases in vegetation biomass across Amazonia and

small increases in soil carbon, the latter being a qualita-

tively different response to TRI, HYL and LPJ. Note,

the SHE model has fixed vegetation, and does not

simulate changes in the coverage of plant functional

types (PFTs).

Terrestrial climate-carbon cycle feedbacks

Terrestrial climate-carbon cycle feedbacks are all posi-

tive and range between 40 and 319 ppmv for all DGVMs

and four SRES emission scenario combinations (Table

Table 3 Simulated atmospheric CO2 mixing ratio in 2099 for each Dynamic Global Vegetation Model (DGVM) and Special Report

Emission Scenario (SRES) combination (units are in ppmv) for the S2 simulation

Atmospheric CO2 content 2099 (ppmv) (S2)

HyLand (HYL)

Lund–Potsdam–

Jena (LPJ)

ORCHIDEE

(ORC)

Sheffield-DGVM

(SHE) TRIFFID (TRI)

A1FI 1019 1184 994 938 1162

A2 894 1050 878 836 1031

B1 535 669 553 571 656

B2 611 754 627 628 739

Cumulative land uptake, 2000–2099 (Pg C)

A1FI 320 11 413 505 63

A2 302 9 374 438 53

B1 320 53 308 257 85

B2 315 33 309 290 69

Change in vegetation carbon (Pg C)

A1FI 367 69 306 336 �8

A2 344 60 278 294 �8

B1 277 66 217 168 7

B2 296 60 223 194 1

Change in soil carbon (Pg C)

A1FI �48 �58 107 169 70

A2 �41 �52 97 144 62

B1 43 �13 91 89 78

B2 20 �27 85 97 68

Change in terrestrial carbon stocks between 2000 and 2099 for each combination of DGVM and SRES scenario (units are in Pg C).



4). The maximum range associated with the choice of a

DGVM is 227 ppmv. LPJ and TRI have the largest

climate-carbon cycle feedbacks, SHE the lowest with

HYL and ORC being intermediate.

From Table 4, feedback gains for the DGVMs range

between 0.14 and 0.36 for the A1FI SRES scenario

and between 0.16 and 0.43 for the B1 SRES scenario.

LPJ and TRI have the highest feedback gains at 0.36

(0.43) and 0.35 (0.39) for the A1FI (B1) SRES scenario,

respectively, ORC moderate at 0.25 (0.31). SHE and

HYL have the lowest average feedback gains at 0.14

(0.20) and 0.23 (0.16) for the A1FI (B1) SRES scenario,

respectively. For a given DGVM, bL varies a great deal

among SRES scenarios, whereas gL is more robust.

2000 2020 2040 2060 2080 2100Year

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HYL A1FILPJORCSHETRI

HYL B1LPJORCSHETRI

Fig. 6 Change in land carbon uptake, Pg C yr�1, (top panels) relative to the present day (mean 1980–1999) for five Dynamic Global

Vegetation Models (DGVMs) from coupled climate-carbon cycle simulations with two Special Report Emission Scenarios (SRES)

emission scenarios, A1FI (solid lines), B1 (dashed lines), bracketing the range in emissions. Change in global vegetation (middle panels)

and soil carbon (top panels), Pg C, between 2100 and 2000 under scenarios A1FI (solid lines) and B1 (dashed lines) for HyLand (HYL,

black), Lund–Potsdam–Jena (LPJ, yellow), ORCHIDEE (ORC, blue), Sheffield (SHE, green), and TRIFFID (TRI, red). Note: only LPJ is run

with interannual variations in climate (see ‘Methods’).

U N C E R TA I N T Y I N L A N D C A R B O N C Y C L E F E E D B A C K S 13


180° 90°W 0° 90°E

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−6 −3 −0.1 0.1 3 6 −6 −3 −0.1 0.1 3 6 −6 −3 −0.1 0.1 3 6

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LPJ CV LPJ CS LPJ TotC

TRI CV TRI CS TRI Totc

SHE CV SHE CS SHE TotC

ORC CV ORC CS ORC TotC

HYL CV HYL CS HYL TotC

Fig. 7 Change in land carbon storage (TotC) and component vegetation (CV) and soils (CS) carbon stocks between 1860 and 2099 from

the coupled climate-carbon cycle simulation under Special Report Emission Scenarios (SRES) emission scenario A1F1 (units are Pg C) for

HyLand (HYL), Lund–Potsdam–Jena (LPJ), ORCHIDEE (ORC), Sheffield (SHE) and TRIFFID (TRI).



90°N

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Fig. 8 Change in vegetation coverage (%) for aggregated plant functional types, tree (TREE) and herbaceous (HER) between 1860 and

2099 for the five Dynamic Global Vegetation Models (DGVMs) from the coupled climate-carbon cycle simulation under Special Report

Emission Scenarios (SRES) emission scenario A1FI.



However, all the models produce significant positive

feedbacks, implying an acceleration of the rate of CO2

increase via the response of the land carbon cycle to

climate change.

Figure 9 shows the ‘climate alone’ changes in Land

Uptake (Pg C), NPP (Pg C yr�1) and soil carbon residence

time (year) plotted against global temperature change

(K) from the coupled simulation. Values for bL, gL and

gain, g, for the A1FI and B1 model simulations are given

in Table 4, and compared against literature data (Norby

et al., 2005; Friedlingstein et al., 2006).

All DGVMs agree on a reduction in land uptake with

climate change, which implies a consensus on a positive

land climate-carbon cycle feedback. LPJ and TRI have

the largest climate-land carbon storage sensitivity (i.e.

the largest gL values). For moderate changes in global

temperature, HYL is least sensitive, although at global

temperature changes exceeding � 3 1C, HYL exhibits

the strongest sensitivity to further warming. All

DGVMs agree on a decrease in global NPP, RH and

soil carbon residence time (Cs/RH) with climate warm-

ing. Changes in RH are a composite response of decom-

position rate and litter inputs to climate warming.

Although the decomposition rates increase with climate

change, seen here as a decrease in soil carbon residence

time, soil carbon stocks decline, in particular due to

declining litter input, via reductions in NPP.

The extra-tropical response of land carbon to climate

warming differs among models, with LPJ, TRI and ORC

simulating reductions in uptake (Fig. 10), and simulated

uptake by SHE and HYL remaining unchanged. The

latter is a result of the counterbalancing effects of an

Table 4 Carbon cycle gain, g, along with component sensitivities of land carbon storage to CO2 (bL) and to climate (gL)

Model bL (Pg C ppm�1) b550 (%) gL (Pg C K�1) Gain, g

Climate-C-cycle

feedback (ppmv)

Norby et al. (2005) 23

C4MIP, A2 Friedlingstein et al. (2006)

HadCM3LC (TRI) 1.3 �177

IPSL-CM4-LOOP (ORC) 1.3 �20

CLIMBER2-LPJ 1.1 �57

C4MIP Model Range 0.2–2.8 �20 to �177

C4MIP Model Avg 1.35 �79

This study, A2

HyLand (HYL) 1.58 22 �103 0.22 136

Lund–Potsdam–Jena (LPJ) 1.48 18 �198 0.37 282

ORCHIDEE (ORC) 1.94 34 �137 0.27 158

Sheffield-DGVM (SHE) 1.50 23 �60 0.15 81

TRIFFID (TRI) 1.49 31 �188 0.36 265

This study, A1FI

HYL 1.45 22 �112 0.23 165

LPJ 1.36 18 �203 0.36 319

ORC 1.75 34 �138 0.25 180

SHE 1.41 24 �61 0.14 92

TRI 1.40 31 �195 0.35 303

This study, B1

HYL 2.64 – �62 0.16 40

LPJ 2.4 – �229 0.43 166

ORC 3.36 – �161 0.31 81

SHE 2.13 – �79 0.20 58

TRI 2.21 – �194 0.39 144

This Study, B2

HYL 2.16 23 �67 0.17 56

LPJ 1.98 15 �208 0.41 190

ORC 2.70 – �143 0.29 98

SHE 1.87 23 �69 0.18 62

TRI 1.90 31 �185 0.38 170

Calculations are made for the year 2099 relative to 1860 for the A1FI and B1 SRES scenarios. b550 represents the percentage increase

in global NPP from present day (year 2000) to future atmospheric CO2 concentrations of 550 ppm (taken from the prescribed climate

simulation). Magnitude of the climate-carbon cycle feedback between 2000 and 2099 for each combination of DGVM and SRES

scenario, coupled-prescribed climate simulations (units are in ppmv).



increase in extra-tropical NPP and a decrease in soil

carbon residence time with warming. For NPP, ORC

and TRI are fairly insensitive to warming in the extra-

tropics. Despite a reduction in boreal evergreen forests

in LPJ, caused by a heat and summer drought induced

reduction in boreal forest NPP, the deciduous and

herbaceous PFTs, which are better suited to this new

environment, have high NPP. Hence, the reductions in

boreal vegetation carbon simulated by LPJ may be a

transitory effect, and a new equilibrium may be ap-

proached after 2100 in which vegetation stocks recover

(Smith & Shugart, 1993).

As a test, the LPJ A1FI coupled simulation was

extended after 2100, with fixed environmental condi-

tions of 2100, until a new equilibrium in terms of

vegetation distribution and land carbon pools was

reached. Equilibrium land carbon for year 2100 was

simulated at 2263 Pg C compared with 2412 Pg C at the

end of the transient simulation at 2100. Vegetation

biomass was higher in the equilibrium for year 2100

at 1092 Pg C compared with 956 Pg C from the transient

simulation, although this hides large regional differ-

ences in both sign and magnitude (e.g. the ‘Boreal’ and

Amazonian forests continue to loose biomass as the

new equilibrium at 2100 conditions is approached).

Nevertheless, in time boreal evergreen forests are re-

placed by open deciduous woodland, with lower woo-

dy coverage and biomass than the original forest. The

soils are far from equilibrium by the end of the transient

simulation at 2100. LPJ simulates an equilibrium global

soil carbon stock of 1171 Pg C for year 2100 compared

with 1456 Pg C at the end of the transient. In general,

the disequilibrium in carbon stocks north of 301N are

controlled by soil carbon (whose decomposition is slow

under ambient conditions), whereas in the tropics,

vegetation carbon is more important.

All DGVMs simulate large reductions in NPP over

the tropics with climate warming. NPP of TRI is most

Surface temperature change (K)0 1 2 3 4 5 6

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Fig. 9 Simulated net primary productivity (NPP) sensitivity to atmospheric CO2 (prescribed climate simulation). Simulated land

uptake sensitivity, net primary productivity (NPP), heterotrophic respiration (RH) (coupled-prescribed climate) and soil residence time

(from the coupled simulation) to global mean temperature change for two Special Report Emission Scenarios (SRES) emission scenarios,

A1FI (solid line) and B1 (dashed line) for five Dynamic Global Vegetation Models (DGVMs), HyLand (HYL, black), Lund–Potsdam–Jena

(LPJ, yellow), ORCHIDEE (ORC, blue), Sheffield (SHE, green) and TRIFFID (TRI, red).



sensitive to climate change. DGVMs agree on an in-

crease in extra-tropical RH and decreases in tropical RH

with global warming. The RH response is a composite

of changes in litter input (NPP and vegetation mortal-

ity) and changes in the soil C decomposition rate with

climate change. Despite little change in extra-tropical

NPP in LPJ, RH increases due to a large decrease in soil

residence time with warming. All DGVMs simulate

reductions in soil carbon residence time across the ex-

tra-tropics. In the tropics, soil carbon residence time is

insensitive to global climate change with large reduc-

tions in NPP matched by equally large reductions in RH,

implying a possible substrate limitation to tropical RH.

Discussion

Major features of the modelled carbon cycle

Results show that all DGVMs are consistent with global

land carbon budgets for the 1980s and 1990s, and are in

agreement with other modelling studies on cumulative

land uptake over the last 50 years (McGuire et al., 2001).

DGVMs are also able to simulate the correct sign of the

global land carbon response to ENSO, but with differ-

ing magnitudes of response.

Although all five DGVMs simulate cumulative net

carbon uptake by 2099 in response to changes in future

climate and atmospheric composition, the magnitude of

land uptake varies markedly among DGVMs. Results

indicate large uncertainties in future atmospheric CO2

concentration associated with uncertainties in the ter-

restrial biosphere response to changing climatic

conditions.

All five DGVMs have similar response of productiv-

ity to elevated atmospheric CO2. NPP is stimulated by

between 18% and 34% when atmospheric CO2 (from the

prescribed climate simulation) is elevated from ambient

concentrations to 550 ppmv. This is in good agreement

with a median stimulation of 23% for the forest sites in

the Free-Air-CO2 Enrichment experiments (Norby et al.,

2005). DGVMs agree much less in the way they respond

regionally to changing climate, confirmed by the large

range in gL (the sensitivity of land carbon storage to

climate) among DGVMs (Table 4).

Dependence on modelled climate change

The DGVM response is very much linked to the GCM

climatology applied (Berthelot et al., 2005; Schaphoff

et al., 2006; Scholze et al., 2006). LPJ run with HadCM2

simulates large land carbon uptake, whereas with

HadCM3, from Schaphoff et al. (2006), the model simu-

lates a land source of carbon after 2050. Only LPJ run

with CGCM and CSIRO GCM climatology project a

0 1 2 3 4 5 6Surface temperature change (K)

20

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Fig. 10 Simulated regional sensitivity of land uptake, net primary productivity (NPP), heterotrophic respiration (RH) (coupled-

prescribed simulations) and soil residence time (from the coupled simulation) to global mean temperature change for the A1FI Special

Report Emission Scenarios (SRES) emission scenario for five Dynamic Global Vegetation Models (DGVMs), HyLand (HYL, black), Lund–

Potsdam–Jena (LPJ, yellow), ORCHIDEE (ORC, blue), Sheffield (SHE, green) and TRIFFID (TRI, red). Extra-tropics are defined as land

north of 301N and south of 301S, solid lines; and tropics as land between 301S and 301N, dashed lines.



greater land carbon source and sink, respectively

(Schaphoff et al., 2006). LPJ had a very moderate re-

sponse from Cramer et al. (2001) study using the more

‘moderate’ HadCM2 climatology, as well as from C4MIP

study (Friedlingstein et al., 2006) where it was coupled to

the CLIMBER-2 Earth System Model of Intermediate

Complexity (EMIC) (Petoukhov et al., 2000).

A warming over land in CLIMBER-2 is much weaker

than in the HadCM3. In the C4MIP run, land tempera-

tures in the latitudinal zone 30–601N increase by 4 1C in

CLIMBER-LPJ, while in the HadCM3 run warming in

this region is 6–9 1C for IS92. In the A1FI scenario

applied in this study, the warming is even stronger.

Also, the precipitation increase with warming (dP/dT)

in CLIMBER is stronger than in the HadCM3.

Indeed, CLIMBER-LPJ in C4MIP has a gL of

�57 Pg C K�1 compared with �198 Pg C K�1 for IMO-

GEN-LPJ. As expected, the gL from HadCM3LC-TRI in

C4MIP (�177 Pg C K�1) is similar to IMOGEN-TRI

(�188 Pg C K�1) in this study. However, this is clearly

not the case for ORC and LPJ, with a gL for IPSL-ORC in

C4MIP of �20 Pg C K�1 compared with �137 Pg C K�1

for IMOGEN-ORC in this study. The implication is that

the climate-carbon cycle feedback is also highly depen-

dent on the nature of the simulated climate change.

The IMOGEN climate simulation did not include the

cooling effect of sulphate aerosols, and as a result, the

rate of warming over the historical period in the

coupled-climate cycle experiment is greater than ob-

served (Jones et al., 2003). For the original Cox et al.

(2000) runs, the land temperature for 1991–2000 is about

1.8 1C warmer than the 1860–1890 average (global

mean is about 1.2 1C warmer), whereas observations

for land, indicate a 1 1C warming with a global mean

of 0.7–0.8 1C. In the ‘sulphates 1 natural’ runs of

Jones et al. (2003), the land temperature for the period

1991–2000 was 0.8 1C warmer than the preindustrial,

with a global mean of 0.6 1C. Unsurprisingly, the

DGVMs with the largest climate-carbon cycle feedbacks

(LPJ and TRI) also simulate the smallest contemporary

land uptake under the excessive historical climate

warming simulated in our coupled climate-carbon

cycle experiment (results not shown). LPJ driven

with anomalies from a HadCM3 climatology which

includes sulphate aerosols simulated a larger contem-

porary land carbon uptake (Schaphoff et al., 2006;

Scholze et al., 2006), just as HadCM3LC did when

aerosol effects were included (Jones et al., 2003). This

does indicate, however, that according to the more

‘pessimistic’ DGVMs, terrestrial ecosystems have the

potential to become a net global source of carbon in the

coming decades if the cooling effect of sulphates has

been underestimated, and drops off as anticipated

(Andreae et al., 2005).

Responses of ecosystem processes to heat and drought

All DGVMs simulate a reduction in soil carbon in

response to climate forcing. Three DGVMs simulate

reductions in soil carbon across tropical ecosystems,

and four models simulate reductions across northern

midlatitudes, the latter in broad agreement with Cox

et al. (2000). This is despite rather different soil model

formulations. Nevertheless, there remains a large ‘pro-

cess’ uncertainty among models due to differential

decomposition-moisture responses (Peylin et al., 2005).

There has been much discussion in literature about

the magnitude of soil decomposition sensitivity to

temperature and whether this response would be sus-

tained over the coming decades or if it is a short-lived

phenomenon (Davidson et al., 2000; Giardina & Ryan,

2000; Knorr et al., 2005). Based on experimental data

synthesis, Giardina & Ryan (2000) argue that readily

decomposable soil organic matter (SOM) is mainly

responsible for the observed temperature sensitivity,

implying long-term soil respiration to be governed by

substrate availability and litter quality. Davidson et al.

(2000) refuted these conclusions, and argued that tem-

perature sensitivity is just one of the many uncertain

factors difficult to ascertain in isolation. Knorr et al.

(2005) have shown how these conflicting opinions are

compatible with long-term temperature sensitivity of

soil respiration, with the experimental findings ex-

plained by a rapid depletion of labile SOM with negli-

gible response of nonlabile SOM on experimental

timescales. The review of Davidson & Janssens (2006)

identified the need for decomposition to be seen as

dependent on many factors simultaneously, such as soil

temperature, moisture structure and litter quality.

The quantitative response of DGVMs to drought

differs among DGVMs, with TRI and HYL most sensi-

tive to reduced rainfall and elevated temperatures

across Amazonia. LPJ and ORC simulate moderate

forest-dieback. Drought-induced plant mortality results

from decreased photosynthesis, leading to resource

limitations, and/or to plant-hydraulic failure (Van

Nieuwstadt & Sheil, 2005).

In a drought experiment in an east-central Amazo-

nian rainforest at Tapajos, a � 50% reduction in pre-

cipitation led to a � 25% reduction in NPP over the

first 2 years of the experiment (Nepstad et al., 2002).

Despite reductions in NPP and leaf area, there was no

immediate drought response of trees (e.g. leaf senes-

cence). With deep roots, trees can access soil moisture

reserves and are able to withstand several years of

drought. Nevertheless, the ecosystem response to per-

sistent, prolonged drought may lead to increased forest

mortality, as appears to be the case at Tapajos (Saleska

et al., 2003).



Several studies have recorded increased rates of tree

mortality during severe El Nino years across neo-tropi-

cal forests (Condit et al., 1995; Williamson et al., 2000).

During the 1997 drought, plots in the central Amazon

near Manaus, received only 32% of the normal dry

season rainfall, leading to a 70% increase in tree mor-

tality to 1.91% yr�1 (Williamson et al., 2000). However,

mortality rates returned to near-normal levels in sub-

sequent years, and therefore a single drought appears to

have only a modest impact of ecosystem structure

(Williamson et al., 2000).

In addition to the direct effect of drought on plant

mortality, an indirect effect via increased fire risk is

likely to exacerbate matters (Nepstad et al., 1999, 2002;

Van Nieuwstadt & Sheil, 2005). The resilience of tropical

forests to more frequent and severe droughts, both in

terms of the direct drought effect and indirect, via

biomass burning, is key to understanding the potential

for large-scale tropical forest dieback and its implica-

tions for the global carbon cycle. Existing DGVMs show

a range of responses to reductions in precipitation, from

a resilient forest (LPJ, ORC) to a vulnerable forest (TRI,

HYL). Several studies (Cox et al., 2004; Huntingford

et al., 2004) indicate a degree of uncertainty in the onset

of Amazonian forest-dieback with HadCM3 climatol-

ogy, relating to the choice of DGVM parameterization

and driving climatology.

Also important for the carbon balance of tropical

forests is deforestation (Cramer et al., 2004). Further,

the impoverished secondary ecosystems and primary

forests bordering deforested land are likely to be more

susceptible to wildfire (Cochrane et al., 1999; Nepstad

et al., 1999), and frequent disturbance is likely to affect

the ability of forests to regenerate. In general, a greater

process-based understanding of large-scale plant-

drought responses and interaction with wild-fire and

land-use, is needed, and this should filter into the next

generation of DGVMs. Indeed, although the effects of

land-use and land cover changes are very important for

future biogeography and biogeochemistry, inclusion is

beyond the scope of the present study. This will be a

major focus of development in the next generation of

DGVMs.

Another interesting finding is the differential re-

sponse of LPJ and ORC vegetation dynamics in the

boreal forests. Despite ORC vegetation dynamics being

closely related to that of LPJ, their response is qualita-

tively different. LPJ simulates a boreal forest dieback in

response to strong climate warming (Joos et al., 2001;

Lucht et al., 2006), a combined result of suboptimal

photosynthesis at high temperatures (related to the

PFT-specific photosynthesis–temperature response

curves), and plant response to a reduction in summer

precipitation (i.e. summer drought). For LPJ, temperate

trees and herbaceous vegetation are favoured in the

future climatic conditions of HadCM3. The temperature

optima and high temperature limits for photosynthesis

of evergreen conifers used in LPJ range between 10 and

25 1C, and 35 and 42 1C, respectively (Larcher, 1983).

Temperate deciduous trees, on the other hand, have

optima at 15–25 1C, and high temperature limits at 40–

45 1C. Because the temperate PFTs are assigned higher

temperature ranges, they gradually replace the boreal

types and hence, LPJ has a seemingly paradoxical over-

all increase in NPP over the ‘boreal’ zone, but a reduc-

tion in boreal forest coverage. The optimal temperature

ranges for photosynthesis among PFTs in ORC accli-

mate to recent climate conditions, and also ORC em-

ploys a different photosynthesis scheme. Given the

importance of NPP in driving vegetation dynamics

among DGVMs, it is not surprising the response

of the two DGVMs diverge. In a sensitivity study,

Matthews et al. (2007) show the importance of the repre-

sentation of the photosynthesis–temperature response for

the strength of the climate-carbon cycle feedback.

The prospect of Europe’s climate becoming more

Mediterranean, with warmer summers, reduced rainfall

and more frequent and severe droughts, will likely

impact vegetation production, carbon sequestration,

vegetation structure and disturbance regimes, favour-

ing more high-temperature tolerant and drought-resis-

tant species. The potential for such changes in

biogeochemistry is evident from the 2003 summer

drought (Ciais et al., 2005) and the recent drier summers

in mid- and high northern latitudes (Angert et al., 2005).

Together, this points to a critical element in modelling

dynamic global vegetation; the number of PFTs defined,

their respective optimal ranges, and ability of plant

species within PFT groups to adapt and plant processes

to acclimate to new environmental conditions.

Role of nutrient constraints

DGVMs have been criticized for disregarding the po-

tential effects of nutrient (especially nitrogen) limitation

on the ability of ecosystems to sequester CO2. Hungate

et al. (2003) suggested that the CO2 responses projected

by Cramer et al. (2001) were much too large and that

future modelling work must include interactive nitro-

gen cycling. This is indeed a focus of much current

DGVM development (Prentice et al., 2007). However,

the issue is more complex than indicated by Hungate

et al. (2003) for several reasons. First, multiyear free-air

carbon dioxide enrichment (FACE) experiments in tem-

perate forests have not supported the preliminary in-

dications (Oren et al., 2001) of a rapid decline in the

stimulation of NPP due to nitrogen shortage [see e.g.

Moore et al. (2006)]. Second, biogeochemistry models



with interactive nitrogen cycling have shown an addi-

tional stimulation of NPP due to increased rates of

mineralization in a warmer climate (Melillo et al.,

1993; VEMAP, 1995). In this study, SHE includes inter-

active nitrogen cycling. This model shows overall one of

the smallest reductions of NPP in response to warming

among DGVMs, and shows a strong positive response

of NPP to CO2. Thus, it is not clear what impact a

realistic representation of carbon–nutrient interactions

would have on the tendency of the land to act as a

carbon source or sink.

Benchmarking

Benchmarking global models is a key procedure, to

enable confidence in their future projections (Dargaville

et al., 2002; Morales et al., 2005). This study has shown

the ability of models to satisfy contemporary global

carbon cycle constraints, while future projections di-

verge markedly. Jones et al. (2006) noted a similar

phenomenon with a simple carbon cycle model. Many

different parameter combinations were able to recreate

the historical record, but their behaviour diverged in the

future. Process-based, local observations are required to

constrain models, as well as large-scale observations

that hide cancellation of processes. An expanded set of

data for evaluation of short-timescale dynamics for

benchmarking (e.g. Morales et al., 2005; Friend et al.,

2006) is useful. However, DGVMs should also be eval-

uated for longer time-scale dynamics, e.g. future

drought-induced and extreme heat stress mortality.

Information is needed from drought experiments (e.g.

in tropical rainforests, Nepstad et al., 2002; Asner et al.,

2004; Meir & Grace, 2004) and extra-tropical ecosystems

(Hanson et al., 2001; Moorcroft et al., 2004), from actual

large-scale regional droughts, (e.g. the European

drought 2003, Ciais et al., 2005), from Paleo data (Schef-

fer et al., 2006), including tree-rings, and from warming

experiments in boreal forests (Smith & Shugart, 1993;

Marchand et al., 2005).

Conclusion

This study indicates large uncertainties in future atmo-

spheric CO2 concentrations associated with uncertain-

ties in the terrestrial biosphere response to changing

climatic conditions. All DGVMs simulate cumulative

net carbon uptake by 2099 in response to changes in

future climate and atmospheric composition for all

SRES scenarios; however, the magnitude of this uptake

varies markedly among DGVMs. All five DGVMs have

similar response of productivity to elevated atmo-

spheric CO2 in agreement with field observations (Nor-

by et al., 2005).

The DGVMs are in less agreement in the way they

respond to changing climate. However, consistent

among DGVMs is a release of land carbon in response

to climate, implying a significant positive climate-car-

bon cycle feedback in each case. This response is mainly

due to a reduction in NPP and a decrease in soil

residence time in the tropics and extra-tropics, respec-

tively. Major DGVM uncertainties include the follow-

ing: NPP response to climate in the tropics; soil

respiration response to climate in the extra-tropics.

Uncertainty in future cumulative land uptake

(494 Pg C) associated with land processes is equivalent

to over 50 years of anthropogenic emissions at current

levels. Therefore, improving our ability to model ter-

restrial biosphere processes (e.g. plant response to

drought/heat stress) is paramount if we are to enhance

our ability to predict future climate change.

Acknowledgements

The authors wish to thank the following for their contribution tothis study: Martin Best and Ben Booth for advice on IMOGEN,Werner von Bloh and Sibyll Schaphoff for technical advice onLPJ, Olivier Boucher for comments on the manuscript, VictorBrovkin for helpful comments on LPJ results, Andrew Everitt forcomputational support at CEH Wallingford and Andrew Friendfor assistance with the climate data. The contribution of R. A. B.,C. D. J., N. G., S. S. was supported by the UK DEFRA ClimatePrediction Programme under Contract PECD 7/12/37. Thisstudy was also supported by the ENSEMBLES FP6 and theNERC QUEST programmes.

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Evaluation of the terrestrial carbon cycle, future plant geography …empslocal.ex.ac.uk/people/staff/pmc205/papers/2008/GCB... · 2008. 6. 23. · Dynamic Global Vegetation Models