New
Phytologist
Research
Global vegetation and terrestrial carbon cycle changes
after the last ice age
I. C. Prentice1,2, S. P. Harrison1,3 and P. J. Bartlein4
1
Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia; 2Grantham Institute for Climate Change, and Division of
Biology, Imperial College, Ascot SL5 7PY, UK; 3School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, UK;
4
Department of Geography, University of Oregon, Eugene, OR 97403-1251, USA
Summary
Author for correspondence:
I. C. Prentice
Tel: +61 2 9850 4227
Email: [email protected]
Received: 20 September 2010
Accepted: 1 December 2010
New Phytologist (2011) 189: 988–998
doi: 10.1111/j.1469-8137.2010.03620.x
Key words: CO2, dynamic global vegetation
model (DGVM), forest, last glacial maximum
(LGM), productivity, sink, stable isotopes,
woody thickening.
• In current models, the ecophysiological effects of CO2 create both woody thickening and terrestrial carbon uptake, as observed now, and forest cover and
terrestrial carbon storage increases that took place after the last glacial maximum
(LGM). Here, we aimed to assess the realism of modelled vegetation and carbon
storage changes between LGM and the pre-industrial Holocene (PIH).
• We applied Land Processes and eXchanges (LPX), a dynamic global vegetation
model (DGVM), with lowered CO2 and LGM climate anomalies from the
Palaeoclimate Modelling Intercomparison Project (PMIP II), and compared the
model results with palaeodata.
• Modelled global gross primary production was reduced by 27–36% and carbon
storage by 550–694 Pg C compared with PIH. Comparable reductions have been
estimated from stable isotopes. The modelled areal reduction of forests is broadly
consistent with pollen records. Despite reduced productivity and biomass, tropical
forests accounted for a greater proportion of modelled land carbon storage at
LGM (28–32%) than at PIH (25%).
• The agreement between palaeodata and model results for LGM is consistent
with the hypothesis that the ecophysiological effects of CO2 influence tree–grass
competition and vegetation productivity, and suggests that these effects are also
at work today.
Introduction
Glacial periods have consistently been associated with low
CO2 concentrations (Siegenthaler et al., 2005; Lüthi et al.,
2008). They are not the cause of glaciations: glaciations are
paced by orbital changes (Hays et al., 1976; Edwards, 2010),
which produce changes in the seasonal timing and latitudinal
distribution of insolation. However, CO2 concentrations of
170–200 ppm were repeatedly reached during the glacial
maxima, and helped to ‘lock’ the Earth system – including
tropical and Southern Hemisphere regions, remote from the
climatic influence of the northern ice sheets – into a glacial
mode. Although the mechanisms that lowered CO2 concentration during glaciations are still not established, there is
some support for the hypothesis that increased dust transport
from the continents reduced the CO2 concentration from
early ice age intermediate levels to late ice age minimum
levels by stimulating export production by phytoplankton in
988 New Phytologist (2011) 189: 988–998
www.newphytologist.com
high-nitrogen, low-chlorophyll regions of the global ocean,
particularly the Southern Ocean (Bopp et al., 2003; Kohfeld
et al., 2005).
Whatever their causes, CO2 concentration changes
during well-documented periods of recent Earth history
provide opportunities to investigate the effects of CO2 on
the biosphere. The increase in CO2 concentration during
deglaciations encompassed all types of terrestrial vegetation,
including tropical ecosystems, where direct experimental
evidence for CO2 effects is limited or nonexistent. The most
comprehensive palaeodata for any cold period are for the last
glacial maximum (LGM): 20–26.5 ka (Clark et al., 2009).
The Palaeoclimate Modelling Intercomparison Project
(PMIP) Phase II (http://pmip2.lsce.ipsl.fr/) has driven stateof-the-art climate models (as used in future projections for
the Intergovernmental Panel on Climate Change, IPCC)
with changes in ice sheet extent and orography, relative sea
level, orbital parameters and glasshouse gas concentrations
2011 The Authors
New Phytologist 2011 New Phytologist Trust
New
Phytologist
in order to represent the climate of LGM, conventionally
defined as 21 ka (Braconnot et al., 2007).
Synthesis activities have meanwhile yielded global observational datasets of LGM land and sea surface conditions. The
BIOME 6000 project, with subsequent regional updates, has
provided a snapshot of biome distributions at LGM based on
pollen and plant macrofossil data (Prentice et al., 2000 and
references therein; Bigelow et al., 2003; Pickett et al., 2004;
Marchant et al., 2009). Spatial coverage is far from uniform,
with undersampling in most of the tropics – yet there are sufficient data points to document biome shifts across the
tropics, and for the quantitative analysis of changes in forest
cover (Harrison & Prentice, 2003).
These data are complemented by stable isotope measurements. Interpretations of changes in the triple isotope
composition of atmospheric O2 (18O, 17O, 16O: Luz et al.,
1999; Luz & Barkan, 2005) and the Dole effect (the d18O
offset between O2 and seawater: Bender et al., 1994;
Hoffmann et al., 2004) depend on the unique capability of
biological carbon cycling to transfer oxygen isotope signatures between water and O2. These measures provide
information on global total (marine and terrestrial) gross primary production (GPP) (Bender et al., 1994; Blunier et al.,
2002; Landais et al., 2007, 2010). The d13C value of seawater, as preserved in the calcium carbonate shells of benthic
marine foraminifera, indicates changes in global terrestrial
carbon storage as a result of the fractionation in photosynthesis and the propagation of this signal into terrestrial biomass
and soils (Shackleton, 1977; Bird et al., 1994; Prentice &
Harrison, 2009). d13C measurements from other materials
and environments provide data on vegetation changes (a
combined signal of ci : ca ratios of C3 plants and C3 vs C4
plant carbon) when measured in terrestrial sediments
(Talbot & Johanessen, 1992; Giresse et al., 1994) and even
offshore sediments, where compound-specific d13C analysis
has been applied to biomarkers originating from land plants
(Rommerskirchen et al., 2006).
Two key findings already have unequivocal support from
LGM data–model comparisons. First, it is necessary to
invoke ecophysiological CO2 effects in order to account for
the reduction in the tropical forest area at LGM as shown
by pollen data. No climate model dries the continents sufficiently to produce such a reduction caused by climate
change alone (Harrison & Prentice, 2003; Prentice &
Harrison, 2009). Second, models forced by LGM climate
alone produce an insufficient reduction of total land biosphere carbon storage, compared with the evidence from
the marine d13C record (Prentice & Harrison, 2009).
These findings were obtained using equilibrium biogeography models that translate potential net primary
productivity (NPP) and bioclimatic limits of plant functional types (PFTs) into biomes using semi-empirical rules.
Analyses of the contemporary carbon cycle require dynamic
2011 The Authors
New Phytologist 2011 New Phytologist Trust
Research
global vegetation models (DGVMs), which couple timedependent changes in PFT abundances with ecophysiological and biogeochemical processes. DGVMs came to
prominence with an analysis of future land carbon uptake
projections (Cramer et al., 2001) highlighted in the IPCC
Third Assessment Report (Prentice et al., 2001). DGVMs
are now the main tool for modelling the land carbon cycle,
but current models still generate different projections (see
Friedlingstein et al., 2006; Sitch et al., 2008; and the IPCC
Fourth Assessment Report, Denman et al., 2007). They
also make assumptions about processes that are still highly
controversial in the ecological literature. The disputed phenomena include woody thickening (the observed increase in
woody plant cover in savannas across the global tropics and
subtropics) and the terrestrial uptake of anthropogenic CO2
(the net land uptake of CO2, which continues despite tropical deforestation releasing CO2). DGVMs predict both
phenomena as direct consequences of rising CO2 concentration. Woody thickening is predicted because of enhanced
competition by woody (C3) plants against grasses. Net land
uptake of CO2 is predicted because of CO2 fertilization:
when C3 photosynthesis increases, NPP and litter production outpace the compensating increase in heterotrophic
respiration from a growing soil carbon pool. Yet, both
mechanisms have been claimed to be ineffective at the space
and time scales modelled (e.g. Archer et al., 1995; Körner,
2006). If such critiques are well founded, the models must
be incorrect, and their correct predictions coincidental.
Woody thickening and net CO2 uptake, however, also
took place – over large spatial and long temporal scales –
after LGM. It is therefore of considerable interest to run
DGVMs under LGM conditions, including CO2 changes,
and to ask whether the models correctly represent the nature
and magnitude of subsequent changes in the state of the
biosphere. If they do, this finding would support the way in
which CO2 effects are treated in the models, and the implications for contemporary as well as palaeo-conditions.
We present DGVM simulations of global LGM and preindustrial Holocene (PIH) vegetation and carbon cycling.
We use the Land Processes and eXchanges (LPX) model
(Prentice et al., unpublished), the most recent development
of the Lund–Potsdam–Jena (LPJ) family of DGVMs (Sitch
et al., 2003; Gerten et al., 2004). LPJ has previously been
run for LGM using outputs from a mixed-layer ocean climate
model (Kaplan et al., 2002) and one coupled atmosphere–
ocean general circulation model (AOGCM) (Thonicke
et al., 2005). We use outputs from PMIP II simulations
made with four AOGCMs. Our objectives were to assess the
realism of modelled LGM to PIH changes in vegetation distribution and carbon cycling resulting from known CO2
changes, combined with climate changes as simulated by
state-of-the-art climate models, and to consider the implications for the simulation of carbon cycle processes in models.
New Phytologist (2011) 189: 988–998
www.newphytologist.com
989
New
Phytologist
990 Research
Description
The vegetation model
LPX (Prentice et al., unpublished) is a development from
the LPJ SPread and InTensity of FIRE (LPJ-SPITFIRE)
model (Thonicke et al., 2010), which, in turn, is based on
the LPJ DGVM (Sitch et al., 2003; Gerten et al., 2004).
The simulation of plant carbon and water exchanges in
these models is identical with that in BIOME3 (Haxeltine
& Prentice, 1996a) and BIOME4 (Kaplan et al., 2003).
PFTs and their bioclimatic limits are derived from
BIOME3 with minor modifications. LPJ adds dynamic representations of establishment, mortality, growth, carbon
allocation (to fine roots, transport tissues and leaves), plant
allometry (including relationships among height, diameter
and crown diameter at the individual plant level) and
dynamic competition among PFTs, whose abundance is
defined in terms of foliage projective cover (FPC). LPJSPITFIRE and LPX represent the influence of potential
ignition rates, vegetation properties and weather conditions
on biomass burning. LPX uses Gerten et al.’s (2004)
improved soil hydrology and a modification of Thonicke
et al.’s (2010) fire scheme.
C3 photosynthesis is simulated in these models using a
simplified implementation of the Farquhar et al. (1980)
model. Leaf-level carboxylation capacity (Vcmax) is assigned
dynamically based on an assumption of optimality (maximizing net assimilation rate over a 24-h period), given
photosynthetically active radiation (PAR), temperature and
ambient CO2 concentration ca (Haxeltine & Prentice,
1996b). A big-leaf approximation is used to integrate assimilation rates vertically through the canopy. Leaf internal
CO2 concentration (ci) is a PFT-specific fixed fraction of ca
under well-watered conditions and is drawn down when a
water supply function falls below atmospheric demand.
The net effect of an increase in ca, if other environmental
variables are constant, is the down-regulation of Vcmax,
accompanied by an increase in daily net assimilation.
Down-regulation is not mechanistically modelled, but arises
from the optimality assumption. Given a conservative ci : ca
ratio, enhancement of ca means that the value of Vcmax that
allows the leaf to make full use of the available PAR is lower
than at current ca. (A similar principle is implicit in recent
theoretical treatments of CO2 effects: e.g. Franklin, 2007.)
Because of this acclimation of Vcmax, the simulated response
of photosynthesis under well-watered conditions varies with
ca, approximately tracking the electron transport-limited
rate. An increase in ca also produces a down-regulation of
stomatal conductance (this follows from the assumption of
a conservative ci : ca ratio combined with the ‘diminishing
return’ of increasing ci), and thus a reduction in modelled
plant water use.
New Phytologist (2011) 189: 988–998
www.newphytologist.com
The climate model simulations
The LGM climate simulations used to run LPX were provided by the FGOALS (China), HadCM3 (UK), IPSL
(France) and MIROC (Japan) AOGCMs. These were the
four models that provided all the variables required to run
LPX at the time at which the PMIP II archive was accessed
for this study. All the AOGCMs were run under the PMIP
II protocol. Outputs were extracted for mean monthly
values of daily minimum and maximum temperature,
precipitation and fractional sunshine hours (48 variables
altogether), at each climate model grid cell, in the LGM
and PIH model runs. All outputs were converted to anomalies (differences between PIH and LGM values) for each
climate model, grid cell and variable.
The vegetation modelling protocol
LPX simulations were performed on a 0.5 grid using the
Climatic Research Unit TS2.1 dataset as the baseline
climate (New et al., 2000). For the PIH control run of
LPX, CO2 concentration was fixed at 280 ppm, and baseline climate data were provided for each grid cell as a
repeated, detrended time series for 1948–2000. This
approach assumes that the PIH climate was similar to the
climate during the second half of the 20th century (in terms
of mean values and the nature of interannual variability,
which this protocol preserves), whilst avoiding the undesirable ‘sawtooth’ effect that would occur if no detrending
were used. The model was spun up from a bare ground state
until the soil carbon pool with the longest residence time
reached stability.
For LGM simulations, it was necessary first to extend the
baseline climate data out on to the continental shelf areas
that were exposed at LGM. The time-varying, gridded baseline climate data were linearly extrapolated to )130 m
elevation, applying a standard global )6 K km)1 lapse rate
for temperatures, and empirically estimated global ‘lapse
rates’: +2 mm km)1 and )0.01 km)1 for precipitation and
fractional sunshine hours, respectively. LGM ocean and
land ice grid points were then masked out, consistently with
the PMIP II protocol. LPX was spun up at all of the additional grid points to provide a starting point for the LGM
simulations. Then, for each LGM climate model and grid
cell, the end point of the control simulation was used as the
starting configuration and the model run was continued
after a step-change to low CO2 (180 ppm) and an ‘LGM
climate’. To obtain this LGM climate, the anomalies from
each climate model were interpolated spatially on to the
extended climate data grid (to deal with the lower resolution of the AOGCM grid relative to the climate data grid),
and then the anomalies for each grid cell and variable were
added to the baseline climate.
2011 The Authors
New Phytologist 2011 New Phytologist Trust
New
Phytologist
Research
LPX internally generates ‘daily’ precipitation using a simple weather generator driven by precipitation (monthly total)
and rain days (proportion of days with rain) (Gerten et al.,
2004). As rain day outputs were not available from the
climate models, rain day inputs to LPX were created using
the assumption that the same covariation of precipitation and
rain days (for each grid cell and month) holds for a change in
climate as for interannual variability in the observed modern
climate. A two-parameter function was fitted to this relationship as observed in the baseline climate data. The function is:
f ðPÞ ¼ 1 expðkPm Þ
Eqn 1
where f (P) is the proportion of days with rain (for any grid
cell and month), P is the precipitation, and k and m are
empirical constants. Eqn 1 was applied, with unchanged values of the constants, to infer LGM rain days from LGM
precipitation for each grid cell and month of the simulations.
Assigning biomes to the LPX output
We used a method similar to that of Joos et al. (2004) to
convert modelled vegetation properties to very broadly
defined vegetation types (biomes) (Fig. 1). This coarse classification of world vegetation into biomes is appropriate for
a global data–model comparison, although finer distinctions could be made on the basis of PFT abundances. The
algorithm depends on modelled total FPC to distinguish
between deserts, dry grass- and shrublands, and forests ⁄
savannas. Forests and savannas are separated by the height of
the average individuals of woody PFTs (note that this modelled average height is much less than the maximum height
that can be attained by large individuals in the real world).
The structurally defined formations were subdivided to
reflect differences between ‘cold’ biomes (with annual mean
growing degree days above 5C, GDD5 < 350 K days) and
the rest, thus separating shrub tundra and tundra from their
warmer climate counterparts. It should be noted that the
distinction between shrub tundra and tundra implies that
the latter term is used in a narrow sense, excluding shrub
tundra (which has higher productivity and carbon storage).
Within the forests and savannas, additional criteria based on
the presence or dominance of particular woody PFTs were
used to distinguish tropical, temperate and boreal biomes, as
shown in Fig. 1. The resulting simulation of the PIH biome
distribution is shown in Fig. 2.
Fig. 1 The scheme used to assign biomes to
Land Processes and eXchanges (LPX) output.
The bottom panel represents a series of
criteria that are evaluated in sequence from
the top when the category assigned in the
top panels is forest or savanna. FPC, foliage
projective cover; GDD5, annual mean
growing degree days above 5C; PFTs, plant
functional types.
2011 The Authors
New Phytologist 2011 New Phytologist Trust
New Phytologist (2011) 189: 988–998
www.newphytologist.com
991
992 Research
New
Phytologist
Fig. 2 The Land Processes and eXchanges
(LPX) simulation of pre-industrial Holocene
(PIH) biome distribution.
Representation of the outputs
As our focus here is not on the differences between
AOGCMs, we present numerical results based on the averaging of the four resulting LPX outputs. To illustrate the
uncertainty in these analyses, we also cite ranges in the text
for key vegetation and carbon cycle quantities, representing
the variation among results obtained with the four climate
models. A simulated LGM biome map is shown based on
the consensus among these four model runs (i.e. a biome is
assigned only for grid cells in which all four runs agree).
This approach to the representation of the uncertainty of
the different AOGCMs (summarizing results from several
vegetation model runs) is preferred to the alternative of
taking ‘average climate model’ results as a single driver for the
DGVM, because of the potential for highly nonlinear
(threshold) behaviour in large-scale vegetation patterns. For
example, in the DGVM, several PFTs have winter temperature
limits that represent each PFT’s characteristic mechanism of
cold tolerance (Harrison et al., 2010). Tropical trees are
assumed to be completely intolerant of frost, and so the
expected effects of a large cooling include the elimination of
tropical trees from regions in which they are found today
because of the presence of frost in currently frost-free environments. This type of response could create discontinuities
in aggregate values of variables, such as carbon storage, which
the averaging of inputs to DGVM would conceal.
Results
We compared simulated biome distributions at LGM,
based on a consensus of LPX outputs among the four climate models, with the LGM palaeovegetation data from
BIOME 6000 (Fig. 3). The data have been aggregated to
the same set of biomes as the model output, using the definitions provided in Table 1. Simulated total biome areas,
GPP and NPP, and carbon storages in biomass, detritus
New Phytologist (2011) 189: 988–998
www.newphytologist.com
and soil organic matter were calculated for the PIH simulation (Table 2) and as mean values across the four LGM
simulations (Table 3). (Note that the simulated values for
soil carbon storage do not include additional carbon stored
in peatlands or permafrost soils.)
Visual comparison of the LGM biome data and the simulated LGM vegetation maps (Fig. 3) shows agreement in
major features. Both the model results and the data show
the largest changes to have been in northern mid- to high
latitudes, with dramatic reductions in the area occupied by
boreal, temperate and warm-temperate forests at LGM. The
lowland tundra region was greatly expanded at LGM,
including vast areas in Eurasia at much lower latitudes than
those at which lowland tundra is found today. There were
also substantial, albeit less far-reaching, vegetation changes
across the tropical regions. Savannas and grasslands
encroached on the dry margins of today’s tropical forests in
Amazonia, consistent with pollen evidence. Tropical forests
were also partially replaced by savannas, sclerophyll woodlands or grasslands in Africa, southern China and, to a lesser
extent, in South-East Asia, again consistent with pollen
evidence (Fig. 3; see also Wurster et al., 2010). These
retreats of tropical forest were partially compensated for in
terms of the area occupied by the modelled occurrence of
tropical forests on the considerable areas of exposed continental shelf. Although not documented in this dataset,
there is additional pollen evidence from marine cores supporting the modelled expansion of tropical forests on to the
continental shelf in South-East Asia (see, for example, Sun
et al., 2000; Kershaw et al., 2001).
Total ice-free land area at LGM was reduced by 3.5%
(Tables 2, 3). The reduction was relatively small because
losses of available land because of ice-sheet extension were
nearly compensated for by gains (mainly in the tropics)
caused by lowered sea level. Overall, modelled global GPP
and NPP were reduced by 30% (27–36% for GPP; 28–
37% for NPP), and carbon storage in soils and vegetation
2011 The Authors
New Phytologist 2011 New Phytologist Trust
New
Phytologist
Research
Fig. 3 Comparison of Land Processes and
eXchanges (LPX)-simulated biome
distribution at the last glacial maximum
(LGM: top panel), based on LGM simulations
from four climate models, with LGM biomes
inferred from pollen and plant macrofossil
records compiled by the BIOME 6000 project
(bottom panel). The simulated biomes are
shown as ‘uncertain’ at grid cells at which the
four simulations do not yield the same
biome.
Table 1 Assignment of biomes classified by BIOME 6000 to the biomes mapped in Fig. 2
Land Processes and eXchanges
(LPX) biome name
Tundra
Shrub-tundra
Desert
Dry grassland ⁄ shrubland
Boreal parkland
Temperate parkland
Sclerophyll woodland
Savanna
Boreal forest
Temperate forest
Warm-temperate forest
Tropical forest
BIOME 6000 names
Tundra, erect dwarf shrub tundra, prostrate dwarf shrub tundra, cushion forb tundra, graminoid forb
tundra, alpine grassland, moor
Low and high shrub-tundra
Desert
Temperate xerophytic shrubland, xerophytic woods ⁄ scrub, temperate grassland, steppe, temperate
grassland and xerophytic shrubland, cool grassland ⁄ shrubland, xerophytic shrubland, heathland
Cold deciduous forest
Temperate evergreen needleleaf open woodland, open conifer woodland
Temperate sclerophyll woodland and shrubland, dry sclerophyll forest ⁄ woodland, semi-arid woodland
scrub
Savanna, tropical savanna, tropical deciduous broadleaf forest and woodland, tropical dry forest
Cold evergreen needleleaf forest, taiga
Cool evergreen needleleaf forest, cool conifer forest, cool mixed forest, cool-temperate rainforest,
cool-temperate evergreen needleleaf and mixed forest, cold mixed forest, temperate evergreen
needleleaf forest, temperate conifer forest, temperate deciduous broadleaf forest, temperate deciduous
forest
Warm-temperate broadleaf and mixed forest, warm-temperate broadleaf forest, warm-temperate
rainforest, wet sclerophyll forest, warm mixed forest, broadleaved evergreen ⁄ warm mixed forest, warm
evergreen forest
Tropical evergreen broadleaf forest, tropical semi-evergreen broadleaf forest, tropical rainforest, tropical
seasonal forest
by 37% (33–41%) or 620 Pg C (550–694 Pg C). The
greater part of this modelled reduction comes from reduced
biomass and detritus. Only 162 Pg C (112–196 Pg C) is
2011 The Authors
New Phytologist 2011 New Phytologist Trust
from reduced soil carbon storage. The modelled total carbon storage reduction is similar to that obtained in an
earlier study with LPJ (610 Pg C: Thonicke et al., 2005).
New Phytologist (2011) 189: 988–998
www.newphytologist.com
993
New Phytologist (2011) 189: 988–998
www.newphytologist.com
137.0
4.8
5.8
17.4
18.3
8.4
7.4
7.0
14.5
14.1
11.8
4.6
23.0
Total
Tundra
Shrub-tundra
Desert
Dry grassland
Boreal parkland
Temperate parkland
Sclerophyll
Savanna
Boreal forest
Temperate forest
Warm-temperate forest
Tropical forest
59.2 (432)
0.1 (11)
1.6 (279)
0.5 (29)
3.7 (202)
3.2 (377)
2.7 (364)
3.2 (462)
7.4 (515)
6.6 (469)
6.4 (543)
3.3 (711)
20.5 (894)
NPP, Pg C a)1
(g C m)2 a)1)
124 (903)
0.1 (15)
2.4 (408)
1.5 (87)
8.8 (480)
5.7 (682)
5.9 (791)
7.5 (1060)
17.9 (1240)
14.6 (1030)
15.7 (1340)
8.3 (1800)
35.4 (1540)
GPP, Pg C a)1
(g C m)2 a)1)
659 (4.8)
0 (0)
3 (0.5)
0 (0)
1 (0)
26 (3.0)
6 (0.8)
3 (0.5)
7 (0.5)
159 (11.3)
103 (8.7)
36 (0.8)
316 (13.7)
Above
ground
132.0
17.8
7.1
23.3
21.4
7.6
6.0
9.6
11.6
3.0
3.7
2.1
18.9
Total
Tundra
Shrub-tundra
Desert
Dry grassland
Boreal parkland
Temperate parkland
Sclerophyll
Savanna
Boreal forest
Temperate forest
Warm-temperate forest
Tropical forest
41.0 (311)
0.1 (6)
1.7 (241)
1.0 (43)
4.1 (193)
2.2 (296)
2.2 (363)
4.1 (431)
6.8 (591)
1.3 (423)
1.8 (467)
1.4 (672)
14.3 (755)
NPP, Pg C a)1
(g C m)2 a)1)
86.3 (655)
0.3 (19)
2.9 (411)
2.4 (105)
9.7 (453)
4.6 (611)
5.8 (959)
9.5 (989)
14.8 (1280)
3.0 (1020)
5.7 (1500)
3.4 (1600)
24.3 (1290)
GPP, Pg C a)1
(g C m)2 a)1)
340 (2.6)
0 (0)
1 (0.1)
0 (0)
1 (0.1)
8 (1.0)
6 (1.0)
6 (0.6)
10 (0.9)
31 (10.5)
31 (8.1)
18 (8.7)
228 (12.1)
Above
ground
65 (0.5)
0 (0)
1 (0.1)
1 (0)
2 (0.1)
5 (0.7)
4 (0.7)
5 (0.5)
5 (0.3)
4 (1.4)
6 (1.6)
3 (1.6)
29 (1.6)
Below
ground
405 (3.1)
0 (0)
2 (0.3)
1 (0)
3 (0.1)
13 (1.7)
10 (1.7)
10 (1.1)
15 (1.3)
36 (11.9)
36 (9.6)
22 (10.2)
257 (13.6)
Total
771 (5.6)
0 (0)
6 (1.0)
0 (0)
2 (0.1)
43 (5.0)
10 (1.3)
6 (0.9)
12 (0.8)
180 (12.8)
118 (10.1)
43 (9.3)
352 (15.3)
Total
95 (0.7)
0 (0.1)
11 (1.9)
1 (0)
6 (0.3)
17 (2.0)
8 (1.1)
7 (1.0)
7 (0.5)
19 (1.4)
10 (0.9)
3 (0.6)
5 (0.2)
Below
ground
236 (1.7)
1 (0.2)
26 (4.5)
1 (0.1)
9 (0.5)
40 (4.8)
13 (1.8)
12 (1.7)
12 (0.8)
68 (4.8)
29 (2.5)
7 (1.5)
18 (0.8)
Total
79 (0.6)
1 (0.1)
16 (1.7)
1 (0)
4 (0.2)
14 (1.9)
4 (0.7)
7 (0.7)
5 (0.4)
9 (3.1)
5 (1.2)
2 (1.0)
10 (0.6)
Above
ground
68 (0.5)
1 (0.1)
11 (1.6)
2 (0.1)
8 (0.4)
13 (1.7)
5 (0.9)
9 (0.9)
7 (0.6)
4 (1.3)
2 (0.6)
1 (0.6)
5 (0.3)
Below
ground
147 (1.1)
2 (0.1)
27 (3.9)
2 (0.1)
13 (0.6)
27 (3.6)
10 (1.6)
16 (1.6)
12 (1.0)
13 (4.4)
7 (1.8)
3 (1.6)
15 (0.8)
Total
Detritus, Pg C (kg C m)2)
142 (1.0)
1 (0.1)
15 (2.6)
0 (0)
3 (0.2)
23 (2.8)
5 (0.7)
5 (0.7)
5 (0.3)
49 (3.5)
19 (1.6)
4 (0.9)
12 (0.5)
Above
ground
Detritus, Pg C (kg C m)2)
520 (3.9)
90 (8.3)
82 (12.9)
8 (0.4)
39 (1.9)
80 (10.3)
28 (4.5)
45 (5.2)
34 (2.8)
38 (10.0)
20 (4.6)
10 (0.4)
46 (2.3)
Soil, Pg C
(kg C m)2)
682 (5.0)
3 (0.6)
77 (13.2)
3 (0.2)
25 (1.4)
116 (13.8)
39 (5.2)
33 (4.8)
34 (2.3)
194 (13.8)
85 (7.2)
21 (4.5)
52 (2.3)
Soil, Pg C
(kg C m)2)
1070 (8.1)
92 (8.5)
111 (17.4)
11 (0.5)
55 (2.6)
120 (15.6)
48 (7.6)
71 (8.1)
61 (5.0)
87 (22.8)
107 (14.2)
36 (15.0)
318 (16.0)
Total, Pg C
(kg C m)2)
1690 (12.3)
4 (0.8)
109 (18.7)
5 (0.3)
37 (2.0)
199 (23.6)
62 (8.3)
52 (7.3)
57 (4.0)
441 (31.3)
233 (19.8)
71 (15.4)
422 (18.4)
Total, Pg C
(kg C m)2)
NPP, net primary productivity; GPP, gross primary production.
a
Carbon cycle quantities are given as global values, and per unit area (in parentheses). All values that are greater for LGM than for the pre-industrial Holocene (PIH) are shown in bold.
Land area
(Mm2)
Biome
112 (0.8)
0 (0)
3 (0.5)
0 (0)
1 (0.1)
17 (2.0)
4 (0.5)
3 (0.4)
5 (0.3)
21 (1.5)
16 (1.3)
7 (1.4)
36 (1.6)
Below
ground
Biomass, Pg C (kg C m)2)
Table 3 Modelled land biosphere properties for the Last Glacial Maximum (LGM)a
GPP, gross primary production; NPP, net primary productivity.
a
Carbon cycle quantities are given as global values, and per unit area (in parentheses).
Land area
(Mm2)
Biome
Biomass, Pg C (kg C m)2)
Table 2 Modelled land biosphere properties for the pre-industrial Holocene (PIH)a
994 Research
New
Phytologist
2011 The Authors
New Phytologist 2011 New Phytologist Trust
New
Phytologist
These changes can be compared with estimates based on
global isotopic signals. The triple oxygen isotope method
relies on the different modes of isotopic fractionation of the
three isotopes in biological processes and in photochemical
reactions involving exchanges with ozone in the stratosphere. Fractionation associated with primary production
and respiration is mass dependent (so that fractionation
against 18O is about twice as strong as fractionation against
17
O), whereas fractionation in the stratosphere is mass independent. The resulting depletion of atmospheric 17O
depends on the balance of biospheric vs stratospheric reactions and, with some assumptions, can be used to estimate
total global GPP (Luz et al., 1999; Blunier et al., 2002; Luz
& Barkan, 2005). The data for LGM, based on ice-core
palaeoatmospheric measurements, indicate a reduction in
global total GPP by 25–40% (Landais et al., 2007). This
includes marine GPP, which is of a similar magnitude to
terrestrial GPP and was probably not reduced at LGM (e.g.
Bopp et al., 2003). The reduction in terrestrial GPP was
therefore probably > 25–40%, and so the modelling procedure may even underestimate the magnitude of the
reduction in GPP. Estimates of changes in terrestrial carbon
storage were reviewed by Prentice & Harrison (2009), who
noted that credible published estimates obtained with a
variety of methods have continued to fall within the range
300–700 Pg C, as given by Bird et al. (1996). Thus, the
isotopic analyses show changes of the same sign and, within
their admittedly large uncertainties, generally similar magnitude to the LPX simulation of changes in total terrestrial
carbon storage and GPP.
According to the model results, the tundra, desert, dry
grassland ⁄ shrubland and sclerophyll biomes occupied larger
areas at LGM than at PIH. By contrast, the area occupied
by tropical forests was reduced by 18% (13–21%). Greater
reductions were suffered by warm-temperate (54%, 42–
61%), temperate (68%, 62–77%) and boreal (78%, 73–
82%) forests. NPP per unit area, averaged over the (changing) distribution areas of each biome, was reduced, on
average, by 16% (13–18%) for tropical forests, 5% (3–7%)
for warm-temperate forests, 14% (11–19%) for temperate
forests and 10% (7–13%) for boreal forests. By contrast,
the modelled NPP of deserts and savannas (with a substantial C4 component) was increased at LGM because of
reduced competition from C3 woody plants. The modelled
increase in NPP of deserts was 46% (37–57%) and, of savannas, 15% (7–19%).
Vegetation carbon densities in forest biomes were generally reduced, but savanna, sclerophyll woodland, dry
grassland and desert biomes showed increases. Soil carbon
densities followed a qualitatively similar pattern to vegetation carbon densities with one notable exception: vegetation
carbon density in the tundra biome was reduced by 46%
(37–74%), whereas soil carbon density increased by a large
and variable factor because of the strong inhibitory effect of
2011 The Authors
New Phytologist 2011 New Phytologist Trust
Research
low LGM temperatures on soil organic matter decomposition in mid- to high latitudes.
The modelled vegetation carbon density in tropical forests was reduced by 11% (10–13%), whereas soil carbon
density was unchanged ()6 to +9%). This might be surprising at first sight (because NPP and litter production were
reduced, which should lead to reduced soil carbon storage),
but probably reflects a compensating effect of lower average
temperatures, slowing soil decomposition. Although the
tropical forest area was reduced, the modelled relative
importance of tropical forests as a land carbon store was
greater at LGM (30%; 28–32%) than at PIH (25%),
primarily because of the major reductions in area of the
other forest biomes.
Discussion
Harrison & Prentice (2003) first used a process-based global biogeography model to show that the ecophysiological
effects of CO2, acting through modification of the competitive equilibrium between woody plants and grasses –
especially in the tropics, where the modelled negative effects
of low CO2 on woody plant growth are greatest, whereas
the effects on (C4) grasses are least – were required to
account for the documented reduction of forest area at
LGM. Effects of climate change alone were found to be
inadequate, regardless of which model was used to provide
the LGM climate. We have performed a comparable set of
simulations here with more recent climate modelling results
and using a DGVM, including both the CO2 and climate
effects. We have compared the results with a more comprehensive set of pollen and plant macrofossil-based biome
reconstructions (Fig. 3). Our results confirm that simulations including the effects of CO2 changes on tree–grass
competition, as represented much more explicitly through
dynamic competition and vegetation–fire interactions in
DGVM, capture the broad features of the observed biome
shifts (within the limitations imposed by the relatively
sparse palaeoecological record from the tropics), and that
they do so robustly despite differences in the LGM climate
as represented by four AOGCMs (Fig. 3).
Thus, our results, when taken together with those of
Harrison & Prentice (2003) using a model which simulates
CO2 and climate effects on plant carbon and water
exchanges identically to LPX, support the hypothesis that
the contraction of tropical forest cover under LGM
conditions was, to a large extent, a predictable outcome of
the low CO2 concentration. This analysis is consistent with
a CO2-based explanation for woody thickening today.
There is also recent experimental evidence in favour of
such an explanation (Kgope et al., 2010; Wigley et al.,
2010), which can thus be considered both parsimonious
and consistent with contemporary and palaeoecological
evidence.
New Phytologist (2011) 189: 988–998
www.newphytologist.com
995
996 Research
There remains a caveat that there are additional pressures
that may favour woody thickening today, and so it may not
be possible to attribute contemporary woody thickening
uniquely to CO2 in any one case. However, this does not
invalidate the general inference that rising CO2 is likely to
be contributing to woody thickening in contemporary ecosystems, and that this process is likely to intensify as the
CO2 concentration continues to rise.
By using a DGVM in this study, we have also been able
to provide a complete global accounting of changes in GPP
and carbon storage that is mutually consistent with the simulation of biome shifts. The modelled increases in GPP and
carbon storage from LGM to PIH derive straightforwardly
from the effect of low CO2 on C3 plant photosynthesis
(Gerber et al., 2004). The magnitudes of the effects broadly
agree with independent isotopic evidence. Extensive sensitivity experiments with the ‘parent’ LPJ DGVM (again,
with an identical set-up to LPX for the simulation of CO2
and climate effects on productivity and water balance) show
that GPP and carbon storage in the low (LGM to PIH)
range of CO2 concentration respond much more strongly
to CO2 than to commensurate changes in temperature
(Gerber et al., 2004; see also simulations by Kaplan et al.,
2002 and Joos et al., 2004). A parsimonious explanation
thus attributes the LGM reductions in forest cover, production and carbon storage, especially in the tropics, primarily
to downstream effects of photosynthetic physiology. The
success of these simulations supports the implication that
these same mechanisms are likely to be at work today.
There are important caveats regarding the time scale of
the CO2 effects. The deglacial CO2 rise, and the associated
increase in terrestrial biosphere carbon storage, took place
over several thousand years. LPX does not model the effects
of nitrogen limitation on plant productivity. The hypothesis of progressive nitrogen limitation (Luo et al., 2004)
suggests that the near-term stimulatory effect of rising CO2
on NPP in temperate forest ecosystems [as observed over
periods of several years in Free Air Carbon dioxide
Enrichment (FACE) experiments, e.g. Norby et al., 2005]
may decline on a multi-decadal time scale, as nitrogen availability falls behind the increasing plant demand for
nitrogen set in train by the initial ecophysiological response
to CO2. A decline in the CO2 fertilization effect and leaf
nitrogen content during forest stand maturation has also
been reported from a forest FACE study (Norby et al.,
2010). It is plausible that nitrogen availability constraints to
the CO2 response of plant and vegetation growth could
more easily be overcome given thousands of years for a
rebalancing of the total ecosystem nitrogen budget (e.g.
through increased nitrogen fixation, natural nitrogen deposition and reduced nitrogen losses in the gaseous and
aqueous phases). Our results do not rule out the possibility
that nitrogen limitations constrain the realized CO2
response over time scales intermediate between the time
New Phytologist (2011) 189: 988–998
www.newphytologist.com
New
Phytologist
scale of current FACE experiments and the much longer
time scale of glacial–interglacial climate changes. Nor do
they rule out a possible role of phosphorus limitation in
constraining CO2 responses in tropical savannas and forests
(e.g. Vitousek, 1984; Herbert & Fownes, 1995; Domingues
et al., 2010). However, the evidence from LGM, as presented here, is inconsistent with the view espoused, for
example, by Körner (2006) that the ecosystem-level
response of carbon uptake and storage to CO2 concentration on long time scales must be negligible because of the
constraint provided by the stoichiometry of plant biomass.
Rather, the evidence suggests that a full biogeochemical
analysis of ecosystem-level CO2 responses will depend on
an improved understanding of the mechanisms that allow
ecosystems to acquire and retain sufficient nutrients so that
the photosynthetic response to CO2 can be translated into
biomass growth.
At first glance, other models might be expected to behave
in a generally similar way to LPX because of their common
core of photosynthetic physiology. However, these models
differ in many respects, such as whether (and, if so, how)
they implement explicit nitrogen cycling constraints on carbon allocation and growth, whether carbon allocation to
leaves, transport tissues and fine roots is influenced by CO2
concentration and ⁄ or nitrogen availability, their representation (or not) of fire–vegetation interactions and the
particular assumptions they make about photosynthetic,
respiratory and stomatal acclimation in the face of changes
in CO2 concentration, temperature and water availability
(Sitch et al., 2008). All of these differences could influence
the modelled responses of ecosystem composition and carbon storage to CO2 changes. There is growing interest in
the application of standard benchmarks for land models,
exploiting a range of observations of the land surface and
the carbon cycle in an attempt to narrow down some of the
large uncertainties in carbon cycle modelling (Randerson
et al., 2009). So far, these efforts have focused on contemporary observations, such as the seasonal cycles and
interannual variability of atmospheric CO2, ‘greenness’
measures and runoff (see, for example, Abramowitz, 2005;
Blyth et al., 2010). The use of multiple climate model
results as a source of palaeoclimatic information for key
times in the past, such as LGM, opens up new possibilities
for the evaluation of terrestrial models under a more
broadly inclusive range of environmental conditions (AbeOuchi & Harrison, 2009). Mid-Holocene and LGM climate model experiments, as developed by PMIP, are now
designated as a priority for the IPCC Fifth Assessment
Report, and will be included in the public archive of climate
model results generated for IPCC. We suggest that the
LGM modelling experiment described here could provide
an additional valuable benchmark for terrestrial models,
building on both PMIP and the solid body of evidence that
now exists for the state of the biosphere at LGM.
2011 The Authors
New Phytologist 2011 New Phytologist Trust
New
Phytologist
Acknowledgements
This article is the outcome of an invitation to I.C.P. to give a
keynote presentation at the 23rd New Phytologist Symposium
in Guangzhou, China, in November 2009, which is gratefully acknowledged. It has benefited from the additional
opportunity provided by Rich Norby and Ram Oren for
I.C.P. to present these results at a symposium on FACE
data–model comparisons at the Ecological Society of
America Meeting, Pittsburgh, in August 2010. We thank
Doug Kelley for performing the LPX model runs, Wang
Han for assistance with GIS manipulations and mapping the
model outputs and biome data, and the PMIP modelling
groups for their work in developing and implementing simulation protocols and making their outputs freely available
for research. The work has benefited from discussions with
many colleagues, especially Philippe Ciais and Pierre
Friedlingstein, and from the Palaeo Carbon Modelling
Intercomparison Project (PCMIP: Abe-Ouchi & Harrison,
2009) Workshop sponsored by the Quantifying and Understanding the Earth System (QUEST) programme of the UK
Natural Environment Research Council (NERC). PCMIP is
an activity of the International Geosphere–Biosphere
Programme’s Analysis, Integration and Modelling of the
Earth System (AIMES) core project, and is co-ordinated by
Ayako Abe-Ouchi, Pierre Friedlingstein, S.P.H. and I.C.P.
References
Abe-Ouchi A, Harrison SP. 2009. Constraining the carbon-cycle feedback
using palaeodata: the PalaeoCarbon Modelling Intercomparison Project.
EOS. Transactions of the American Geophysical Union 90: 140.
Abramowitz G. 2005. Towards a benchmark for land surface models.
Geophysical Research Letters 32: L22702.
Archer S, Schimel DS, Holland EA. 1995. Mechanisms of shrubland
expansion: land use, climate or CO2? Climatic Change 29: 91–99.
Bender M, Sowers T, Labeyrie L. 1994. The Dole effect and its variations
during the last 130,000 years as measured in the Vostok ice core. Global
Biogeochemical Cycles 8: 363–376.
Bigelow NH, Brubaker LB, Edwards ME, Harrison SP, Prentice IC,
Anderson PM, Andreev AA, Bartlein PJ, Christensen TR, Cramer W
et al. 2003. Climate change and Arctic ecosystems 1. Vegetation
changes north of 55N between the last glacial maximum, midHolocene, and present. Journal of Geophysical Research 108: 8170.
Bird MI, Lloyd J, Farquhar GD. 1994. Terrestrial carbon storage at the
LGM. Nature 371: 566.
Bird MI, Lloyd J, Farquhar GD. 1996. Terrestrial carbon storage from the
last glacial maximum to the present. Chemosphere 33: 1675–1685.
Blunier T, Barnett B, Bender ML, Hendricks MB. 2002. Biological
oxygen productivity of the last 60,000 years from triple oxygen isotopic
measurements. Global Biogeochemical Cycles 16: GB1029.
Blyth EM, Gash JHC, Lloyd A, Pryor M, Weedon GP, Shuttleworth JW.
2010. Evaluating the JULES model with Fluxnet data. Journal of
Hydrometeorology 11: 509–519.
Bopp LE, Kohfeld KE, Le Quéré C, Aumont O. 2003. Dust impact on
marine biota and atmospheric CO2 during glacial periods.
Paleoceanography 18: 24, 16 pp.
Braconnot P, Otto-Bliesner B, Harrison SP, Joussaume S, Peterchmitt JY, Abe-Ouchi A, Crucifix M, Driesschaert E, Fichefet TH, Hewitt CD
2011 The Authors
New Phytologist 2011 New Phytologist Trust
Research
et al. 2007. Results of PMIP2 coupled simulations of the MidHolocene and Last Glacial Maximum – Part 1: experiments and largescale features. Climate of the Past 3: 261–277.
Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B,
Mitrovica JX, Hostetler SW, McCabe AM. 2009. The last glacial
maximum. Science 325: 710–714.
Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA, Brovkin
V, Cox PM, Fisher V, Foley J, Friend AD et al. 2001. Global responses
of terrestrial ecosystems to changes in CO2 and climate. Global Change
Biology 7: 357–373.
Denman KL, Chidthaisong A, Ciais P, Cox PM, Dickinson RE,
Hauglustaine D, Heinze C, Holland E, Jacob D, Lohmann U et al.
2007. Couplings between changes in the climate system and
biogeochemistry. In: Solomon S, Qin D, Manning M, Chen Z, Marquis
M, Averyt KB, Tignor M, Miller HL, eds. Climate change 2007: the
physical science basis. Cambridge, UK: Cambridge University Press,
499–587.
Domingues TF, Meir P, Feldpausch TR, Saiz G, Veenendaal EM, Schrod
F, Bird M, Djagbletey G, Hien F, Compaore H et al. 2010. Colimitation of photosynthetic capacity by nitrogen and phosphorus in
West Africa woodlands. Plant, Cell & Environment 33: 959–980.
Edwards RL. 2010. Ice Age rhythms. Science 327: 790.
Farquhar GD, Caemmerer S, Berry JA. 1980. A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:
78–90.
Franklin O. 2007. Optimal nitrogen allocation controls tree responses to
elevated CO2. New Phytologist 174: 811–822.
Friedlingstein P, Cox P, Betts R, Bopp L, von Bloh W, Brovkin V,
Cadule P, Doney S, Eby M, Fung I. 2006. Climate–carbon cycle
feedback analysis: results from the C4MIP model intercomparison.
Journal of Climate 19: 3337–3353.
Gerber S, Joos F, Prentice IC. 2004. Sensitivity of a dynamic global
vegetation model to climate and atmospheric CO2. Global Change
Biology 10: 1223–1239.
Gerten D, Schaphoff S, Haberlandt U, Lucht W, Sitch S. 2004.
Terrestrial vegetation and water balance – hydrological evaluation of a
dynamic global vegetation model. Journal of Hydrology 286: 249–270.
Giresse P, Maley J, Brenac P. 1994. Late Quaternary palaeoenvironments
in the Lake Barombi Mbo (West Cameroon) deduced from pollen and
carbon isotopes of organic matter. Palaeogeography, Palaeoclimatology,
Palaeoecology 107: 65–78.
Harrison SP, Prentice IC. 2003. Climate and CO2 controls on global
vegetation distribution at the last glacial maximum: analysis based on
palaeovegetation data, biome modelling and palaeoclimate simulations.
Global Change Biology 9: 983–1004.
Harrison SP, Prentice IC, Barboni D, Kohfeld KE, Ni J, Sutra J-P. 2010.
Ecophysiological and bioclimatic foundations for a global plant
functional classification. Journal of Vegetation Science 21: 300–317.
Haxeltine A, Prentice IC. 1996a. BIOME3: an equilibrium terrestrial
biosphere model based on ecophysiological constraints, resource
availability and competition among plant functional types. Global
Biogeochemical Cycles 10: 693–709.
Haxeltine A, Prentice IC. 1996b. A general model for the light use
efficiency of primary production. Functional Ecology 10: 551–561.
Hays JD, Imbrie J, Shackleton NJ. 1976. Variations in the Earth’s orbit:
pacemaker of the Ice Ages. Science 194: 1121.
Herbert DA, Fownes JH. 1995. Phosphorus limitation of forest leaf area
and net primary production on a highly weathered soil. Biogeochemistry
29: 223–235.
Hoffmann G, Cuntz M, Weber C, Ciais P, Friedlingstein P, Heimann
M, Jouzel J, Kaduk J, Maier-Reimer E, Seibt U et al. 2004. A model of
the Earth’s Dole effect. Global Biogeochemical Cycles 18: GB1008.
Joos F, Gerber S, Prentice IC, Otto-Bliesner BL, Valdes PJ. 2004.
Transient simulations of Holocene atmospheric carbon dioxide and
New Phytologist (2011) 189: 988–998
www.newphytologist.com
997
998 Research
terrestrial carbon since the Last Glacial Maximum. Global
Biogeochemical Cycles 18: GB2002.
Kaplan J, Prentice IC, Knorr W, Valdes PJ. 2002. Modeling the dynamics
of terrestrial carbon storage since the Last Glacial Maximum. Geophysical
Research Letters 29: 2074.
Kaplan JO, Bigelow NH, Prentice IC, Harrison SP, Bartlein PJ,
Christensen TR, Cramer W, Matveyeva NV, McGuire A, Murray DF
et al. 2003. Climate change and Arctic ecosystems II: modeling,
palaeodata-model comparisons, and future projections. Journal of
Geophysical Research 108: 8171.
Kershaw AP, Penny D, van der Kaars S, Anshari G, Thamotherampili A.
2001. Vegetation and climate in lowland southeast Asia at the Last
Glacial Maximum. In: Metcalfe I, Smith JMB, Morwood M, Davidson
I, eds. Faunal and floral migrations and evolution in SE Asia–Australasia.
Rotterdam, the Netherlands: Taylor & Francis, 227–236.
Kgope BS, Bond WJ, Midgley GF. 2010. Growth responses of African
savanna trees implicate [CO2] as a driver of past and current changes in
savanna tree cover. Austral Ecology 35: 451–463.
Kohfeld KE, Le Quéré C, Harrison SP, Anderson RF. 2005. Role of
marine biology in glacial–interglacial cycles. Science 308: 74–78.
Körner C. 2006. Plant CO2 responses: an issue of definition, time and
resource supply. New Phytologist 172: 393–411.
Landais A, Dreyfus G, Caprona E, Masson-Delmotte V, Sanchez-Goñi
MF, Desprat S, Hoffmann G, Jouzel G, Leuenberger M, Johnsen S.
2010. What drives the millennial and orbital variations of d18Oatm?
Quaternary Science Reviews 29: 235–246.
Landais A, Lathière J, Barkan E, Luz B. 2007. Reconsidering the change
in global biosphere productivity between the Last Glacial Maximum and
present day from the triple oxygen isotopic composition of air trapped
in ice cores. Global Biogeochemical Cycles 21: GB1025.
Luo Y, Su B, Currie WS, Dukes JS, Finzi A, Hertwig U, Hungate B,
McMurtrie RE, Oren R, Parton WJ et al. 2004. Progressive nitrogen
limitation of ecosystem responses to rising atmospheric carbon dioxide.
BioScience 54: 731–739.
Lüthi D, Le Floch M, Bereiter B, Blunier T, Barnola J-M, Siegenthaler
U, Raynaud D, Jouzel J, Fischer H, Kawamura K et al. 2008. Highresolution carbon dioxide concentration record 650,000-800,000 years
before present. Nature 453: 379–382.
Luz B, Barkan E. 2005. The isotopic ratios 17O ⁄ 16O and 18O ⁄ 16O in
molecular oxygen and their significance in biogeochemistry. Geochimica
et Cosmochimica Acta 69: 1099–1110.
Luz B, Barkan E, Bender ML, Thiemens MH, Boering KA. 1999. Tripleisotope composition of atmospheric oxygen as a tracer of biological
productivity. Nature 400: 547–550.
Marchant R, Cleef A, Harrison SP, Hooghiemstra H, Markgraf V, van
Boxel J, Ager T, Almeida L, Anderson R, Baied C et al. 2009. Pollenbased biome reconstructions for Latin America at 0, 6000 and 18 000
radiocarbon years. Climates of the Past 5: 725–767.
New M, Hulme M, Jones P. 2000. Representing twentieth-century space–
time climate variability. Part II: Development of 1901–96 monthly
grids of terrestrial surface climate. Journal of Climate 13: 2217–2238.
Norby RJ, DeLucia EH, Gielen B, Calfapietra C, Giardina CP, King JS,
Ledford J, McCarthy HR, Moore DJP, Ceulemans R et al. 2005. Forest
response to elevated CO2 is conserved across a broad range of productivity.
Proceedings of the National Academy of Sciences, USA 102: 18052–18056.
Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE. 2010.
CO2 enhancement of forest productivity constrained by limited
nitrogen availability. Proceedings of the National Academy of Sciences,
USA 107: 19368–19373.
Pickett EJ, Harrison SP, Flenley J, Grindrod J, Haberle S, Hassell C,
Kenyon C, MacPhail M, Martin H, Martin AH et al. 2004. Pollenbased reconstructions of biome distributions for Australia, South-East
New Phytologist (2011) 189: 988–998
www.newphytologist.com
New
Phytologist
Asia and the Pacific (SEAPAC region) at 0, 6000 and 18,000 14C years
B.P. Journal of Biogeography 31: 1381–1444.
Prentice IC, Farquhar GD, Fasham MJR, Goulden ML, Heimann M,
Jaramillo VJ, Kheshgi HS, Le Quéré C, Scholes RJ, Wallace DWR.
2001. The carbon cycle and atmospheric carbon dioxide. In: Houghton
JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell
K, Johnson CA, eds. Climate change 2001: the scientific basis.
Cambridge, UK: Cambridge University Press, 183–237.
Prentice IC, Harrison SP. 2009. Ecosystem effects of CO2 concentration:
evidence from past climates. Climates of the Past 5: 297–307.
Prentice IC, Jolly D, BIOME 6000 participants. 2000. Mid-Holocene
and glacial-maximum vegetation geography of the northern continents
and Africa. Journal of Biogeography 27: 507–519.
Randerson JT, Hoffman FM, Thornton PE, Mahowald NM, Lindsay K,
Lee Y-H, Nevison CD, Doney SC, Bonan G, Stöckli R et al. 2009.
Systematic assessment of terrestrial biogeochemistry in coupled climate–
carbon models. Global Change Biology 15: 2462–2484.
Rommerskirchen F, Dupont L, Eglinton G, Rullkötter J. 2006.
Glacial ⁄ interglacial changes in southern Africa: compound-specific d13C
land plant biomarker and pollen records from southeast Atlantic
continental margin sediments. Geochemistry Geophysics Geosystems 7:
Q08010, 21pp.
Shackleton NJ. 1977. Carbon-13 in Uvigerina: tropical rainforest history
and the equatorial Pacific carbonate dissolution cycles. In: Andersen
NR, Malahoff A, eds. The fate of fossil fuel CO2 in the oceans. New York,
NY, USA: Plenum, 401–427.
Siegenthaler U, Stocker TF, Monnin E, Lüthi D, Schwander J, Stauffer
B, Raynaud D, Barnola J-M, Fischer H, Masson-Delmotte V et al.
2005. Stable carbon cycle–climate relationship during the Late
Pleistocene. Science 310: 1313–1317.
Sitch S, Huntingford C, Gedney N, Levy PE, Lomas M, Piao S, Betts R,
Ciais P, Cox P, Friedlingstein P et al. 2008. Evaluation of the
terrestrial carbon cycle, future plant geography and climate–carbon cycle
feedbacks using 5 Dynamic Global Vegetation Models (DGVMs).
Global Change Biology 14: 1–25.
Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan
JO, Levis S, Lucht W, Sykes MT et al. 2003. Evaluation of ecosystem
dynamics, plant geography and terrestrial carbon cycling in the LPJ
dynamic global vegetation model. Global Change Biology 9: 161–185.
Sun X, Li X, Luo Y, Chen X. 2000. The vegetation and climate on the
emerged continental shelf of the South China Sea. Palaeogeography
Palaeoclimatology Palaeoecology 160: 301–316.
Talbot MR, Johanessen T. 1992. A high resolution palaeoclimate
reconstruction for the last 27,500 years in tropical West Africa from the
carbon and nitrogen isotope composition of lacustrine organic matter.
Earth and Planetary Science Letters 110: 23–37.
Thonicke K, Prentice IC, Hewitt C. 2005. Modelling glacial–interglacial
changes in global fire regimes and trace gas emissions. Global
Biogeochemical Cycles 19: GB3008, 10 pp.
Thonicke K, Spessa A, Prentice IC, Harrison SP, Dong L, CarmonaMoreno C. 2010. The influence of vegetation, fire spread and fire
behaviour on global biomass burning and trace gas emissions: results
from a process-based model. Biogeosciences 7: 1999–2011.
Vitousek PM. 1984. Litterfall, nutrient cycling, and nutrient limitation in
tropical forests. Ecology 65: 285–298.
Wigley BJ, Bond WJ, Timm Hoffmann M. 2010. Thicket expansion in a
South African savanna under divergent land use: local versus global
drivers? Global Change Biology 16: 964–976.
Wurster CM, Bird MI, Bull ID, Creed F, Bryant C, Dungait JAJ, Paz V.
2010. Forest contraction in north equatorial Southeast Asia during the
Last Glacial Period. Proceedings of the National Academy of Sciences, USA
107: 15508–15511.
2011 The Authors
New Phytologist 2011 New Phytologist Trust