TEMPORAL AND SPATIAL VARIATIONS OF TERRESTRIAL BIOMES AND
CARBON STORAGE SINCE 13000 YR BP IN EUROPE: RECONSTRUcn0N
FROM POLLEN DATA AND STATISTICAL MODELS
C. H. PENGi2, 1. GUIOT', E. VAN CAMPO'. and R. CHEDDADI'
'
'
Laboratoire de Botanique Historique et Palynologie. UA CNRS 1152. Faculte de St Jer0tiV4:
Bofte 451. 13397 Marseille cedex 20. France
Canadian Forest Service. 5320 - 122 Street. Edmonton. Alberta T6H 3S5. Canada (present address�'�n
.
'
Laboratoire de Geologie du Quaternaire. CNRS Luminy. 13288 Marseille cedex. France
.%
Statistical models calibrated from field measurement data are used to reconstruct the past.�
carbon (C) storage from pollen data for the last 13000 yr BP in Europe. The pollen-based climati�llllA:llJome
reconstructions provide the input data for these statistical models, i.e., annual mean temperature tot4ll3tlnual
precipitation, annual actual evapotranspiration, annual potential evapotranspiration and biome type with a
spatial resolution of0.50 x 0.50 longitude/latitude. Our reconstructions indicate that the last 13000 yr BP were
characterized in Europe by variations of terrestrial biome and net primary productivity (NPP) at various
temporal and spatial scales. For the considered region, our results also suggest that changes in climate have
significantly altered the distribution of terrestrial biomes and affected the uptake of CO, for NPP. However,
these changes did not translate into significant C storage change in potential terrestrial biosphere during the
Holocene. The largest decrease of terrestrial C storage (compared to modern levels) is found during the late­
Glacial period mainly due to the persistence of ice sheets and the small extension of forest.
Abstract.
•.
Keywords.
BlOME,POLLEN DATA, CARBON STORAGE, STATISTICAL MODEL, EUROPE.
1. Introduction
An understanding of past and possible future climate changes and the global carbon (C)
cycle will require a clear picture of how vegetation changed in the past and may
change in the future (Prentice et at., 1991; Overpeck, et at., 1992). The distribution of
terrestrial biomes responds to changes in summer and winter temperatures and also
moisture balance (Woodward, 1987; Prentice et at., 1992). Late Quaternary climatic
changes produced large changes in the distribution of vegetation types. Sets of '4C_
dated pollen diagrams provide records of vegetation patterns at various spatial and
temporal scales in the past (Huntley and Birks, 1983).
Patterns of primary productivity and of C storage in vegetation and soil also respond
to climatic changes. The assumption is that C storage in global terrestrial biomass was
relatively low during the full glacial time, increasing considerably to a maximum
between 9500 and 4500 years ago and then declining to an intermediate amount by the
present time was drawn by Grove (1984). On the basis of the empirical Miami
regression model (Lieth, 1975) and a climatic model (Kutzbach and Guetter, 1986),
Meyer (1988) has shown that the net primary productivity (NPP) for the past 18 000
years was sensitive to annual temperature and precipitation. Foley (1994) found that
total C storage in the terrestrial biosphere did not change significantly over the last
6000 yr BP, which corresponds to the results of Peng et at., (1994a,b). These problems
challenge our ability to identify the location and magnitude of terrestrial C sinks and
sources during periods of climate change.
Water. Air and Soil Pollution 82: 375-390, 1995.
© 1995 Kluwer Academic Publishers. Printed in the Netherlands.
C. H. PENG ET AL.
376
,preyious studies (Peng et al., 1994a, 1994b) have demonstrated the ability of
.&ta�sti(;al biospherical models to provide reconstructions of terrestrial C storage from
'Rollen data. The weak point of the Osnabrtick Biosphere Model (OBM) is the soil
submodel as was reported previously (Peng et at., 1994b). Soil C storage was formerly
expressed as a constant percentage of litter production. We now express this quantity
as a function of actual evapotranspiration (AET), annual soil moisture deficit (which
itself depends on precipitation and potential evapotranspiration (PET» and of the site
disturbance due to human land-use effect (Meentemeyer et at., 1985), the latter being
. neglible at the palaeo-scale. The calculation of NPP is also improved by the use of
�T (Montreal model, Lieth and Box, 1972). NPP then is determined by the most
limiting climatic factor amongst the annual temperature, annual precipitation and AET,
the latter introducing a climatic seasonal effect in NPP.
The objective of this paper is to reconstruct biome variations and the corresponding
clitnatie changes since the end of the last glaciation and to study their influence upon C
storage. This will enable us to better understand the role of the temperate and boreal
forests of Europe in the natural global C cycle during large scale climatic changes.
We use the pollen data of Huntley and Birks (1983) (which, for Europe, are
available for the last 13 000 years) to calculate the vegetation and climatic parameters
needed by the model and to provide maps of C storage in time steps of 1000 years.
Dating of the pollen record is not perfect, but is acceptable, given the well known
errors in the calculations of the different components of the C cycle. Better data will
soon be available from the European Pollen Database.
For each time-slice, the calculations follow four steps:
•
•
•
•
attribution of a biome ("biomization") to each pollen site;
deduction of the four climatic variables (annual temperature, annual precip­
itation, PET and AET);
interpolation of the biomes and climatic parameters to a 0.50 x 0.50 latitude!
longitude grid;
calculation of C storage in vegetation and soil.
The models are validated using independent observations of NPP and C storage in
vegetation and soil.
2. Data and Methods
The use of pollen data to reconstruct climate and C storage is based on the hypotheses
that modern analogues exist for the past and that the equilibrium of C storage depends
on the vegetation structure and climate. These hypotheses are acceptable for the time­
period studied. The main limitation lies in the C storage equilibrium hypothesis which
nevertheless remains acceptable, even for soil, when time steps of 1000 years or more
are taken (Schlesinger, 1990).
2.1. POLLEN DATA
The modern pollen data set consists of 1719 surface samples collected in Europe, i.e.,
those already used in previous work (Guiot, 1990; Peng et al., 1994a).
The pollen sum used in the calculations is the sum of 26 pollen types (Abies. Alnus.
Betula. Buxus. Carpinus. Cedrus. Corylus. Fagus. Fraxinus, Juniperus. Larix. Olea,
TEMPORAL AND SPATIAL VARIAnONS OF TERRESTRIAL BIOMES AND CARBON STORAGE
377
Picea, Pinus, Pistacia, Quercus dec., Quercus ilex, Salix, TWa, Ulmus, Artemisia,
Chenopodiaceae, Ephedra, Ericaceae, Poaceae and Hedera). These taxa aretarely
recognized by the palynologist to the species level, more frequently to the genus level
and sometimes to the family level, which limits the precision.
Fossil pollen data spanning the last 13 000 yr BP in Europe were derived froin
Huntley and Birks (1983). These data consist of relativ� pollen counts of major taxa in
lake or mire sediments for ca. 360 sites. The area extends from 400N to 75°N and from
lOoW to 600E. Sixty-five per cent of the sites are 14C-dated. The rest were dated by
pollen-correlation with 14C-dated nearby sites, or by comparison with standard 14C_
dated regional pollen stratigraphies. The same 26 taxa retained in the modem pollen
data were used, and all percentages were calculated relative to the sum of these''26
taxa.
2.2. POLLEN-BASED BIOMES RECONSTRUCTION
Prentice et at. (unpubl. ms.) have developed a method to attribute a biome to each
pollen assemblage. Each pollen taxon is assigned to one of the plant functional types
such as defined in the BlOME model (Prentice et al., 1992). Because each pollen taxon
is not identified to the species level, it is sometimes impossible to do a unique
classification. For example, a given taxon such as Pinus can be a cool temperate
conifer, a boreal conifer or a warm temperate evergreen tree. A likelihood index is
calculated for each plant functional type and translated in terms of biomes according to
the combinations defined for the BlOME model. Finally for each biome, we obtain an
index defined as the sum of the percentage square root of all the taxa potentially
present in the biome. These indices are compared imd the biome for which the index is
maximum is attributed to the spectrum.
A particular interpolation scheme is applied to maintain the influence of topography
on vegetation through the pollen record, even with a sparse pollen coverage. This
scheme is based on the fact that the deviations between past and modem indices
constitute a more spatially homogenous field than the a single index. This is because
that index variations over time are relatively insensitive to topography. We calculate
the anomalies by subtracting the modem indices from palaeo-indices. The anomalies
are then smoothly interpolated to the 0.5° x 0.5° latitude/longitude grid, using a
standard method of weighted averaging according to the inverse space distance (Guiot,
1991). Simultaneously, the modem pollen database is used to interpolate the modem
biome indices to the same grid (Figure Ib). Because the modem coverage is much
better than the palaeo-coverage, this grid has higher quality than one based on direct
interpolation grid of the palaeodata. The modem grid values are then added to the
values of the grid of palaeo-anomalies to provide gridded palaeo-biome indices.
Finally the biome with the higest index is attributed to each grid point.
2.3. M AP COMPARISON
The biome map reconstructed from pollen and that simulated by the global BlOME
model of Prentice et at. (1992), are compared numerically using the Kappa statistic,
which measures the grid cell by grid cell agreement between these maps (Cohen, 1960;
Monserud, 1990; Monserud and Leemans, 1992).
Monserud (1990) and Prentice et al. (1992) used the following qualitative
descriptors to characterize the degree of agreement suggested by the Kappa statistic:
378
C. H. PENG ET AL.
very poor to poor agreement if K < 0.4, fair agreement if 0.4 < K < 0.55, good
agreement if 0.55 < K < 0.7, very good agreement if 0.7 < K < 0.85, and excellent
agreement if K > 0.85.
A visual comparison of the biomes reconstructed from pollen with the biomes
predicted by the BlOME model indicated good agreement for Europe (Figure 1) except
for some fuzzy boundaries, e.g., taiga/tundra and cool conifer/cool mixed forests. This
result is supported by the overall value of the Kappa statistic for the two maps (K = 0.57).
For individual modern biomes, the agreement was very good for steppe (warm/cool
grass and shrub) (K 0.85) and semidesert (K = 0.72); and was good for cool mi'xed
forest (K = 0.56), taiga (K = 0.62), temperate deciduous forest (K 0.56), and
xerophytic woods/shrub biome (K = 0.62). But there was poor agreement for tundra,
cool conifer forest, cold deciduous forest, cold mixed forest, and evergreen/warm
mixed forest (K < 0.4), mainly because: (1) pollen samples integrate a variable area due
to the long distance transport of some pollen types and (2) there is anthropogenic
disturbance of the vegetation.
=
=
2.4. POLLEN-BASED CLIMATIC RECONSTRUCTION
The climatic parameters needed for calculating the vegetation and the soil C storage
(i.e., annual temperature, annual precipitation, AET and PET) are deduced directly
from the biomes to ensure a good correspondence between climate and biome. The
average value of these climatic parameters is calculated for each biome and is attributed
Comparison of the Biome Patterns in Europe:
reconstructed from Biome model (a) and from pollen data (b)
_ Cool Conifer Forest
_ EvergreenMlarm Mixed Forest
_ Cold Mixed Forest
_ XerophyticWoOOslShrub
_ Taiga
lH WarmlCoolGrass andShrub
c:::J Semidesert
_ Tundra
_ Cold Deciduous Forest
_ Cool Mixed Forest
c:::J No Data
_ Temperate Deciduous Forest
(8): Biome model of Prentice et 81, (1992)
Modern
Fig.
I. Comparison of two biome maps, (a) 'reconstructed from the
BlOME model of Prentice .el al. (1992) and
(b) from modern pollen data in Europe. The Kappa statistic
between two maps.
(lC
=
0.57) indicates a good agreement
TEMPORAL AND SPATIAL VARIATlONS OF TERRESTRIAL BIOMES AND CARBON STORAGE
379
to each fossil pollen spectrum according to its biome. We have done the same for the
geographically corresponding modem pollen spectra, so that the climatic anomalies
have been calculated. These anomalies have been interpolated to the 0.5° x 0.5° grid
cells using the weighted averaging method with a large radius (5°) and these
interpolated anomalies have been added to the corresp9nd
. ing modem climate provided
by Leemans and Cramer (1991). This stepwise· procedure is again developed to
preserve the topography of Europe.
The main consequence of this climate reconstruction procedure is that a point
located in the central part of a given biome zone is attributed the mean climate of the
biome and a point located in a transition between two biomes is attributed a climate at
the midpoint of the two corresponding climates.
2.5. DESCRIPTION OF MODEL
2.5.1. Net Primary Productivity (NPP)
NPP, which is the rate at which vegetation in an ecosystem fixes C from the
atmosphere (gross primary productivity) minus the rate at which it returns to the
atmosphere (plant respiration), represents net C input from the atmosphere into the
biosphere. The NPP (g m-2 y{l) can be expressed as a function of annual temperature Ta
(0C), annual precipitation Pa (mm) and actual evapotranspiration AET (mm). We use
three empirical functions based on the original MIAMI model of Lieth (1975)
(equations (1) and (2» and on the Montreal model of Lieth and Box (1972) (equation (3».
NPPT = 3000 {I + exp(1.315 - 0.119 Ty }
[1]
NPPp = 3000 {I - exp(-0.000664 Pa)}
[2]
NPPAET = 3000 {I - exp[-0.0009695(AET - 20m
[3]
According to the limiting factors principle, the NPP is the minimum of these three
values:
NPP
=
Min (NPPp NPPp, NPPAET)
[4]
2.5.2. Vegetation carbon storage
The relationship between vegetation biomass and NPP has been discussed by
Whittaker and Woodwell (1971), by Esser (1984, 1991) and by Peng et aI., (1994a).
Vegetation C storage (VCS, in g m-2 y{l) is expressed as a non-linear function of NPP
(Peng et at., 1994a) and mean stand age, calibrated on 106 measurement sites in
European forest ecosystems (Cannell, 1982; Rodin et ai. 1975; Lossaint and Rapp, 1978).
O.S 9
VCS = 0.45 (0.0147 A077S NPp 7 )
J5]
Here NPP is expressed in g m-2 y{1 dry matter. Mean stand age (A, expressed in years)
of each vegetation type is derived from Esser (1991). The equation assumes a biomass
C content of 0.45 (Olson et ai., 1985).
380
C. H. PENG ET AL.
2.5.3. Soil carbon storage
The geographic and climatic model of global soil C has been proposed by
Meentemeyer et ai., (1985). This model predicts soil C storage (SCS) from the annual
actual evapotranspiration (AET), the site disturbance (DIS) due to land-use, and annual
soil moisture deficit (D). SCS is expressed as the following function:
SCS = exp{-1.05989DIS +O.OOI56(400[ln(AET + 1)] - AET) - 0.OO102D - 0.2269} [6]
The disturbance factor (DIS) splits the land coverage into undisturbed (DIS = 0) and
disturbed (DIS = 1). Soil moisture deficit (D) is defined as the difference between
annual potential evapotranspiration (PET) and AET. An AET which falls below PET
implies a soil moisture deficit. The AET and PET are calculated by using a simple
bucket model and assuming a soil water capacity of 150 mm, which is a reasonable
estimate for deeply rooted natural vegetation in Europe (Harrison et ai., 1993). In any
case, we have tested the value of 300 mm used by Meentemeyer (1985) for the globe,
without significant changes in AET for Europe.
2.6. MODEL VALIDATION
Simulations of NPP are compared to 20 independent observations reported by Raich et
ai. (1991), McGuire et ai. (1992) and Gauquelin et at. (1994), which represent 11
major ecosystems. The simulations and observations of NPP are highly correlated
(r = 0.93) (Table I). It should be noted that19 of these observations have already been
used to calibrate the TEM model (Raich et al. 1991; McGuire et ai., 1992; Melillo et
ai., 1993), the CASA model (Potter et at., 1993), and the DEMETER model (FoJey,
1994).
The same dataset is used to validate the vegetation C and soil C estimates. In both
cases high correlations are obtained: r = 0.91 for vegetation C and r = 0.81 for soil C
(Table I).
These independent tests provide a means of estimating errors associated with these
calculations. For this the standard deviation of the differences between the 20
observations and the 20 estimates have been calculated for NPP, vegetation and soil C
'
density. Table I shows that the errors range between 19% and 29%, which is relatively
small when considering that they are calculated from independent observations.
TABLE I
Verification statistics of the carbon parameters: correlation between estimates and observations. mean value.
standard error and percentage of the mean represented by the standard error. These parameters are calculated
from 20 observations.
Parameter
NPP (g C m-' y{')
Correlation
Mean
Standard error
% Mean
0.93
477
140
29
19
29
Soil carbon (kg C m-')
0.81
12
2.3
Vegetation carbon (kg C m-')
0.91
9.3
2.7
TEMPORAL AND SPATIAL VARIATIONS OF TERRESTRIAL BIOMES AND CARBON STORAGE
381
3. Results and Discussion
We will successively present the reconstructions and discuss the results for the biomes,
the NPP, vegetation and soil C storage. We will present maps only for the 12, 9, 6, 3
and 0 ka BP time periods, but the synthetic results will
. pe discussed for each 1000 year
time-slice.
.
.
3.1. SPATIAL AND TEMPORAL VARIATIONS IN BlOME PATTERNS
The reconstructed 12 000 yr BP (12 ka) biomes are dramatically different from the
modem ones (Figure 2). In southern Europe, the typical Mediterranean biomes, e.g.,
xerophytic woods/shrub and evergreen/warm mixed forests were replaced by temperate
deciduous forests and by warm grass/shrub (steppes). In central Europe, modern cool
mixed and temperate forests were replaced by cold deciduous forests and tundra. Taiga
was only present in eastern Europe northwest of the Black Sea. Most of northern
Europe was covered by ice sheet (Denton and Hughes; 1981). No data were available
east of 30°E.
At 9 ka, the most dramatic changes are in northern Europe (between 600N and
700N) where ice cover was strongly reduced and replaced by tundra, cold deciduous
and mixed forests. Temperate deciduous forests extended over most of western Europe.
Dry vegetation (xerophytic wood/shrubs and steppes) was reduced to a few spots in
southern Europe, due to a more humid climate in summer.
For 6 ka, the most pronounced changes in biome cover' are seen in Fennoscandia,
where taiga and tundra were considerably decreased. This indicates a warmer climate
especially in summer. Between 9 ka and 6 ka, the northern limit of the temperate
deciduous and cold mixed forests shifted North. In southern Europe, we do not note
any significant changes.
The reconstructed 3 ka biomes distribution are broadly similar to those at 6 ka. The
only large differences are found in northern Scandinavia, where the cold mixed forest
was replaced by cool conifer forest and taiga. The limit of the cool temperate
deciduous mixed forest was already occupying its present position and the
Mediterranean vegetation started to extend into southern Europe.
Climatically speaking, these biome area variations show a cold and dry climate
predominant over much of Europe during the Late-Glacial period (between 13 and 10
ka), and warm and wet climatic conditions in most of Europe (especially in northern
Europe) during the mid-Holocene (approximately between 4000 and 8000 yr BP). This
is in agreement with the climatic reconstructions of Huntley and Prentice (1988, 1994),
and Guiot et at. (1993); and is also consistent with the reconstruction of past moisture
conditions based on lake-level records (Harrison et ai., 1991, 1993).
3.2. SPATIAL AND TEMPORAL VARIATIONS IN NET PRIMARY PRODUCTIVITY (NPP)
For 12 ka, Figure 3 shows the largest decrease in NPP compared to the modem
situation because of the cold climate especially due to the proximity of the ice sheet.
The only small regions with large positive anomalies of NPP are found in southern
Europe, especially in Spain; because of increased precipitation over this area at 12-ka
(as already reconstructed in COHMAP, 1988). Total NPP for 12 ka is approximately
39% lower than today.
382
C. H. PENG ET AL.
Biomes reconstructed from
pollen in Europe
_
_ V\\:)
Xerophytic
odsishrub
EvergreenNVarm
Mixed Forest
D
_
•
Fig.
'Narm/cool
Grass and Shrub
Semldesert
Cool Mixed
Forest
Cold Deciduous
Forest
_
Temperate
Deciduous Forest
Cool Conifer
Forest
•
_
• Tundra
D
Cold Mixed
Forest
Taiga
Ice Sheet I
No Data
2. Spatial patterns of terrestrial biomes at 3000 yr intervals as reconstructed from pollen data since 12000
yr BP in Europe. The ice sheet extent follows Denton and Hughes (1981).
ill<
No Data
Ice I No Data
Fit!.
3.
-400
-200
Spatial varlalino of
siner.: 12000
Primaf::' PrOdUClI\'il:
BP in Eur,}pc:
(\,pp,
in
In
}
{Pa:-.,t minus \·1odcrn}
frum pulkn data u\ing
�H
Y)(iO
statl:-.lll',d model.
384
C. H. PENG ET AL.
At 9 ka (Figure 3), the regions of positive NPP anomalies are found in
Fennoscandia, France and Spain, where temperature was more than 2 °C warmer than
now. Total NPP is about 3% lower than at the pesent time.
At 6 ka, warmer summers over northern Europe and warmer winters over central
and northern Europe (Huntley and Prentice, 1988, 1994), where the temperature is the
main limiting factor, explain the positive anomaIles of NPP. In southern Europe, the
positive anomalies must be explained by high precipitation. Total NPP over the
common area covered by available pollen data in Europe for 6 ka is about 7% larger
than today, which is qualitatively consistent with the results of the global simulations
of Meyer (1988) and of Foley (1994).
The NPP variations at 3 ka are similar to those at 6 ka because of a similar climatic
regime (Huntley and Prentice, 1994). Some differences are observed in Spain, Portugal
and Italy, where the precipitation was higher at 6 ka than at 3 ka. The total NPP for this
period is only 1% larger than the modern value.
The temporal variations of NPP show that maximum NPP is found at 6 ka,
minimum NPP is found at 13 ka, and only small changes in NPP are observed during
the Holocene. These results are very similar to those simulated by Meyer (1988) on the
basis of the Miami Model and the global climate model simulations of Kutzbach and
Guetter (1986).
3.3. TEMPORAL AND SPATIAL VARIATIONS IN VEGETATION CARBON STORAGE
The spatial variability of modern vegetation C density reflects spatial heterogeneity of
the biome and climate distributions (Figure 4).
At 12 ka, the spatial variations of vegetation C densities (Figure 4) are correlated to
variations in NPP (Figure 3). The largest decrease is found in the area of western
Europe covered in tundra. Small regions of C storage increase are found in southern
Spain and Greece. Total vegetation C storage was 59% lower than the modern value
(Figure 5).
For 6 ka, changes of vegetation C densities (compared to modern levels) range from
-5 to +5 kg C/m2 over northern Europe and central Europe. The largest positive
changes are found in Spain, southern Italy, and on the northeastern shore of the Black
Sea, mainly due to the extension of the temperate deciduous forests relative to the less
productive steppes and xerophytic woods and shrub. Total vegetation C storage was
only 5% larger than the modern value (Figure 5).
The overall spatial patterns of vegetation C densities for 3 ka and for 9 ka are
similar to those of 6 ka. Nevertheless, total vegetation C storage is lower than at the
present time by 4.2% and 12.3% respectively (Figure 5).
Based on the common area (5.64 x 106 km2) covered by available pollen data, Figure
5 shows the temporal variation of vegetation C storage. There are no significant
changes 'in terms of vegetation C storage after 8 ka. If we apply the errors calculated in
section 2.6, we find that vegetation C storage varies from 18 (±5) to 70 (±20) Pg C.
3.4. TEMPORAL AND SPATIAL VARIATIONS IN SOIL CARBON STORAGE
For 12 ka (Figure 6), apart from glaciated areas of northern Europe, much of central
Europe and southern Europe shows no dramatic change in soil C densities (ranging
from -3 to +3 kg C/m\ Some southern regions have increased by more than 3 kg C/m2•
In summary, the total soil C storage was reduced by 23% (Figure 5).
\r
C. H. PENG ET AL.
386
At 6 ka, the changes in soil C densities (compared to modern levels) range from -3
to +3 kg C/m2 over much of Europe, except in the far eastern part. This is because of
the reduction in steppe (Figure 2). Total soil C storage is not different from the modem
value for the considered area (Figure 5).
Although the spatial patterns of soil C densities of 3 ka· and 9 ka were similar to
those of 6 ka, the total soil C storage anomalies were respectively -1% and -6% (Figure 5).
The temporal variation of soil C storage is roughly parallel to that of vegetation C
storage (Figure 5) but with a much smaller amplitude. If we apply the errors calculated
in section 2.6, we find that soil C storage varies from 66 (±13) to 92 (±17) Pg C.
3.5. SPATIAL VARIATIONS IN TERRESTRIAL (VEGETATION+SOIL) CARBON STORAGE
Figure 5 shows that after 9 ka, changes in total terrestrial C storage range from -5% to
+3% compared with modern values, and between -31% and -47% during the Late­
Glacial periods, mainly due to the persistence of the ice sheet (accounting for a value
of -28%), and cold and dry climatic conditions (accounting for a value between -4%
and -19%). Gliemeroth (pers. comm.) found a similar result when examining changes
in belowground and aboveground biomass in Europe since Late-Glacial times.
In conclusion, if we add the errors calculated in sections 3.3 and 3.4, we find that
the total C storage varies from 84 (±18) to 162 (±37) Pg C.
180 �------,
160
140
a
e:,120
8,
I.! 100
�
C
80
�--�---e���--e---�---&--����
o
€
60
til
(.)
40
20
O +---�----�--�
o
2
4
3
1
___
5
6
7
8
Time (x1000 yr BP)
Veg-C
-e-- Soil-C
10
9
-.- Total-C
11
12
13
I
Fig. 5. Temporal variations of total carbon storage (in Pg C) at 1000 yr intervals since 13 000 yr BP based on
the common area (5.64 x 106 km') covered by pollen data.
Spatial \ :lriatiliH of
BP
[2 non
\uil i..:arhon dcnsitjl'� (ill
fccunstruclcd
C
III
)r
pullen
i
(P,b! min!!s
\!()d�rn i at
30(JO
lh� statislical model.
388
C. H. PENG ET AL.
4. Summary .and Conclusions
The distribution of biomes reconstructed from pollen is in good agreement with results
obtained from the modern climate using the BlOME model of Prentice et at. (1992),
excluding some boundaries that are still fuzzy (e.g., the taiga/tundra transition and the
cool coniferlcool mixed forests transition). The .poHen-based biome patterns have changed
significan�ly during the last 13 ka. The most striking changes of reconstructed biome
(compared to the present) are found during the Late-Glacial period (13-10 ka) with a
large part of northern Europe covered by ice and with reduction of the temperate forests
and their replacement by colder forests and tundra. At 6 ka, we observe a northward shift of
forest zones and an eastward shift of the temperate deciduous forest. These biome
changes suggest a cold and dry climate over much of Europe during the Late-Glacial period,
and warmer and wetter climatic conditions in most of Europe during the mid-Holocene.
NPP, vegetation and soil C storage calculations have been validated using 20
independent observations reported by Raich et al. (1991), McGuire et al. (1992) and
Gauquelin et al. (1994). These tests show that the errors associated with the models
range from 19% to 29% of the estimates.
For Europe, the regional scale patterns of NPP are strongly affected by climatic
changes. Maximum NPP is found at 6 ka mainly due to warmer summer over northern
Europe and warmer winters over central and northern Europe, while minimum NPP is
found at 13 ka because of the cold climate resulting from the proximity of the ice
sheet. Small changes in NPP are observed before the mid-Holocene.
On the basis of the common area covered by pollen data, we do not find any
significant changes in total C storage of the terrestrial biosphere during the Holocene.
This result appears to be in agreement with the ice core evidence that shows that
atmospheric CO concentration did not change dramatically over the last 10 000 yr BP
2
(Barnola et al., 1987). The major changes in total terrestrial C storage are found during
the Late-Glacial period. Nevertheless, changes in litter and in peatlands are not taken
into account. Our estimates are qualitatively comparable to other studies based on
paleoclimatic simulations (Foley, 1994).
In conclusion, the results presented here suggest that changes in climate can
significantly alter the distribution of the terrestrial biomes and affect net primary
production. In Europe, however, these changes did not translate into significant
changes of C storage during the Holocene. The largest decreases of terrestrial C storage
(between -31% and -47%) are found during the Late-Glacial period due to the
persistence of the ice sheet (accounting for -28% reduction), and cold and dry climatic
conditions (accounting for a value ranging between -4% and -19%). These changes are
significant as the standard error is around 23% of the estimate's value. Of course, our
understanding of past vegetation changes and modelling ability for terrestrial C
budgets of the past are rudimentary and are limited not only by available pollen data,
but also by the accuracy of pollen-based climatic reconstructions and statistical models
(Peng et aI., 1994b). Our results only give a snapshot of the selected periods and the
considered region assumed to be in equilibrium. However, these records of the past
could help to improve our understanding of future climatic change and its potential
impact on vegetation and on the global C cycle. Moreover, the most noticeable
improvements in this field of research will be realized using the European Pollen
Database which contains pollen data from all over Europe.
TEMPORAL AND SPATIAL VARIATIONS OF TERRESTRIAL BIOMES AND CARBON STORAGE
389
Acknowledgments
We are grateful to F. Saadi, J. Belmonte, V. Ruis-Vasquez and S. Bottema who have
kindly provided pollen surface samples for Spain, Morocco and the Near East and B. Huntly
who has kindly provided fossil pollen data. Thanks also to T. Gauquelin, and G. Jalut who
provided some field data for Spain to validate the statiStical model. Valuable comments
have been given by 1. C. Duplessy and R. Leemans. The Programme Environnement of the
French Centre National de Recherche Scientifique, and the EPOCH and ENVIRONMENT
programs of the European Community have funded this study.
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