GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L15403, doi:10.1029/2006GL026919, 2006
Small net carbon dioxide uptake by Russian forests during 1981–1999
C. Beer,1,2 W. Lucht,3 C. Schmullius,1 and A. Shvidenko4
Received 23 May 2006; revised 21 June 2006; accepted 29 June 2006; published 4 August 2006.
[1] A permafrost-enhanced biogeochemical process model
using observed climate and CO2 data, and satellite-observed
maps of forest composition and density predicts a moderate
biomass increment and carbon sink of 74 and 131 Tg
carbon per year (TgC/a) in the Russian forests during
1981 – 1999. The enhanced process model realistically
represents ecosystem state in terms of river runoff, area
burned by fires, vegetation productivity and biomass, in
comparison to monitoring and inventory data. Rising
atmospheric CO2 content is found to have been the main
cause of the carbon sink. Amounting to 7% of carbon
emissions from fossil fuel emissions in Eurasia, our results
demonstrate a limited capability of the Russian boreal forest
in its current state to compensate anthropogenic carbon
emissions. Citation: Beer, C., W. Lucht, C. Schmullius, and
A. Shvidenko (2006), Small net carbon dioxide uptake by
Russian forests during 1981 – 1999, Geophys. Res. Lett., 33,
L15403, doi:10.1029/2006GL026919.
1. Introduction
[2] The terrestrial biosphere north of 25° latitude is
believed to have sequestered 1 –2 PgC/a from the atmosphere during the 1990s, offsetting anthropogenic carbon
emissions by that amount [e.g., Prentice et al., 2000].
Magnitude, spatial patterns and underlying mechanisms of
this sink are still under debate. Analyses of forest inventory
data identify a moderate biomass increase in the forests of
Russia of 76 TgC/a during the 1980s and 1990s [Shvidenko
and Nilsson, 2003] while a much stronger increment in
woody biomass of 284 TgC/a has been derived by remote
sensing [Myneni et al., 2001]. However, changes in biomass
are only one component of the carbon balance of forests.
Net carbon exchange between the land surface and the
atmosphere is determined by the balance of net primary
production (NPP) of the vegetation, heterotrophic respiration of the soil (Rh) and disturbances (mostly fire). On a
continental scale, these quantities can be quantified with a
biogeochemical process model. Here we use the LPJ Dynamic Global Vegetation Model (LPJ-DGVM) [Sitch et al.,
2003] to estimate carbon fluxes and pools in the forests of
Russia as a function of monthly climate observations, soil
texture and atmospheric CO2 concentration. The model was
linked to satellite-observed data sets of vegetation distribution and fraction [Hansen et al., 2003; Bartholome and
Belward, 2005] for a representation of land cover hetero1
Institute for Geography, Friedrich-Schiller-University, Jena, Germany.
Now at Max Planck Institute for Biogeochemistry, Jena, Germany.
3
Potsdam Institute for Climate Impact Research, Potsdam, Germany.
4
International Institute for Applied Systems Analysis, Laxenburg,
Austria.
2
Copyright 2006 by the American Geophysical Union.
0094-8276/06/2006GL026919
geneity. In addition, freeze-thaw impacts on carbon and
water fluxes, which are crucial in the boreal zone [Bonan
and Shugart, 1989], are explicitly simulated. In doing so,
we (i) estimate the biomass change and carbon balance of
the Russian forests during 1981 – 1999, and (ii) determine
the climatic factors that are responsible for a carbon uptake
or release.
2. Methodology
2.1. Model-Based Estimation of Carbon Pools
[3] The LPJ-DGVM combines large-scale representations of terrestrial vegetation dynamics and land-atmosphere
carbon and water exchanges in a modular framework. For a
detailed description and evaluation of the model see Sitch et
al. [2003] and Gerten et al. [2004]. To simulate the size of
the carbon pools leaves, heartwood, sapwood, fine roots,
litter and soil, the model explicitly considers key ecosystem
processes such as photosynthesis, plant growth, mortality,
resource competition, disturbances and respiration. Combined carbon and water fluxes are modeled on a pseudodaily basis from monthly inputs but vegetation dynamics
annually. To account for the variety of structure and
functioning among plants, 4 Plant Functional Types (PFTs)
are distinguished for the boreal zone: Evergreen needleleaf, deciduous broadleaf, deciduous needleleaf and grass
vegetation.
[4] They contribute to the calculated carbon and water
fluxes of the whole grid cell subject to the simulated and
observed vegetation cover since the maximum cover of each
PFT is constrained by observations (section 2.2). The
extend of Russian forests is defined as all grid cells with
a tree foliage cover greater than 15% in this study (see
auxiliary material1). Wetlands are not represented.
[5] The model was driven by 0.5 gridded monthly fields
of air temperature, precipitation, number of rainy days of a
month and cloud cover [Mitchell and Jones, 2005; Oesterle
et al., 2003], and by nine soil texture classes [Food and
Agriculture Organisation, 1991]. Annual CO2 concentrations were used that were derived from ice-core measurements and atmospheric observations, provided by the
Carbon Cycle Model Linkage Project [McGuire et al.,
2001], as a non-gridded global input. LPJ was run from
1901 until 2003 at a daily time step, preceded by a 1000year spinup period using 1901 –1930 climate data to bring
the carbon pools and vegetation cover into an equilibrium
with climate.
2.2. Satellite-Derived Land Cover for Use in a DGVM
[6] We combine two different types of remote sensing
products, dominant land cover type and fractional vegetation coverage, to derive six maps with fractional coverage of
1
Auxiliary materials are available in the HTML. doi:10.1029/
2006GL026919.
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Figure 1. Comparison of modeled watershed runoff of the
Lena river and observations of its discharge [Vörösmarty et
al., 1996] between 1936 and 1984, (a) monthly long-term
means and (b) annual runoff dynamics (see auxiliary
material). The modeled monthly long-term mean values
are shifted by 1 month to allow for time lags in lateral water
flow. LPJ+perm and LPJ+perm+sat denote to model results
with the consideration of freeze-thaw processes and the
coupling to satellite-derived land cover maps, respectively,
in addition to the model version as published before (LPJ
original) [Sitch et al., 2003; Gerten et al., 2004].
the four boreal PFTs considered in the model, agricultural
areas and water bodies. The information about the dominant
land cover (1-km pixel size) originated from the Global
Land Cover 2000 (GLC2000) project of the Joint Research
Centre of the European Commission which is based on
SPOT Vegetation data from 2000 [Bartholome and Belward,
2005]. The fractional tree and grass cover (500-m pixel size)
was provided by the Vegetation Continuous Fields (VCF)
map of the University of Maryland generated from MODIS
data from 2001 [Hansen et al., 2003].
2.3. Permafrost Modeling Within a DGVM
[7] In the LPJ-DGVM soil temperature is assumed to
follow a sinusoidal cycle of surface air temperature with a
damped oscillation about a common mean, and a temporal
lag [Campbell and Norman, 2000; Sitch et al., 2003].
Within permafrost regions, the null of this equation (0°C
isotherm, daily thaw depth) is calculated numerically by
applying Newton’s algorithm. In doing so, the temperature
below near-surface biomass (aboveground litter and grass
biomass) is used, i.e., damping effects of snow and nearsurface biomass are taken into account. Phase change is
considered to directly impact soil moisture. In addition,
water holding capacity is adjusted to the daily thaw depth.
The extent of the permafrost zone is derived by the frostindex, with a threshold value of 0.6 representing the
changeover from continuous to discontinuous permafrost
[Nelson and Outcalt, 1987]. Outside of permafrost regions,
the impact of exact frost depth in winter on vegetation is
neglected, rather the whole soil is assumed to be frozen
when temperature in 10 cm depth is below 0°C. Thus, the
time lag between the start of snow melt and the ability of the
soil to receive water in spring is also considered in nonpermafrost regions of the boreal zone.
3. Results and Discussion
3.1. Model Evaluation
[8] Evaluation (see auxiliary material) of the twofold
enhanced model was performed with respect to the inter-
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linked boreal ecosystem components hydrology, fire disturbances, NPP, Rh and biomass. In this study, computed
ecosystem parameters were chiefly evaluated against results
produced during the project SIBERIA-II [Schmullius and
Hese, 2002]. The study region spans a 3 Mio km2 transect
swath from the Arctic Ocean to the southern steppe, and
between the Yenisey river and Lake Baikal in Central
Siberia. As part of this study, the International Institute
for Applied Systems Analysis (IIASA) has provided new
inventory-based information on NPP, biomass, Rh, and
carbon emission by fires of this area for the year 2003.
[9] A comparison of discharges from large Arctic Siberian rivers with modeled runoffs in their watersheds
demonstrates that both freeze-thaw processes in the soil
and evapotranspiration have a significant impact on the
hydrological regime in Northern Eurasia (Figure 1). Large
runoff peaks in spring are fully explained by the frozen
state of the ground during snow melt. Runoff values in
summer and autumn, however, are mainly determined by
vegetation density. This is evident from the effect of
assimilating satellite-observed lower vegetation densities
into the model.
[10] The water loss during snow melt due to surface
runoff together with beginning transpiration by vegetation
leads to dry conditions of the thawed upper active layer in
spring. This drought period is responsible for a peak in fire
frequency in Siberia in spring [Korovin, 1996]. If freezethaw effects are removed from the computations, fire return
intervals of up to 1000 years are simulated in Northern
Eurasia. With the improved representation of the hydrological regime, fire return intervals of 100 – 200 years are
simulated, in accordance with observations [Bonan and
Shugart, 1989]. The fire model embedded in the LPJDGVM [Thonicke et al., 2001] uses a statistical approach
that does not resolve the interannual variability of fire
occurrence, but the simulation of soil moisture taking into
account freeze-thaw effects leads to a correct prediction of
the mean fire probability (see auxiliary material). In the
SIBERIA-II evaluation region, on average 1.2 Mha burned
annually from 1992 to 2003 as derived from satellite data
[Balzter et al., 2005]. The enhanced LPJ-DGVM calculates
1.5 Mha. The fire return interval has a substantial direct
effect on biomass. In 2003, 43.3 TgC were consumed by
fire in the SIBERIA-II study region based on the GIS
analysis by the IIASA using burned area estimates from
[Balzter et al., 2005]. The improved LPJ-DGVM independently agrees with 42.8 TgC. Except by fire, the amount of
carbon stored in the terrestrial biosphere in Siberia is
strongly determined by NPP. A function of the rather short
summer season, NPP decreases with latitude in Central
Siberia. By constraining the simulated vegetation fraction
with satellite-derived data we are able to reproduce well the
latitudinal variation of both NPP and biomass in Central
Siberia and hence the underlying biogeochemical processes
(Figure 2). NPP of the 3 Mio km2 SIBERIA-II study region
is estimated to have been 255 gC/m2/a in 2003, which
deviates only by 2.4% from the result of the IIASA GIS
based inventory approach, 249 gC/m2/a.
[11] Our NPP estimates are at the high end of independent estimates of 123 –250 gC/m2/a in the Russian forests
[Schulze et al., 1999; Lloyd et al., 2002; Röser et al., 2002;
Shvidenko and Nilsson, 2003] which are based on eddy
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BEER ET AL.: CARBON UPTAKE BY RUSSIAN FORESTS
Figure 2. (a) Net Primary Production and (b) vegetation
carbon density as a function of latitude in the SIBERIA-II
study transect (80 to 110°E) in 2003 as modeled by LPJDGVM versions (with and without permafrost simulation,
satellite-observed vegetation density patterns) versus inventory data of the International Institute for Applied
Systems Analysis, Austria.
covariance measurements or forest inventory data (see
auxiliary material). The modeled north-south gradient of
biomass (Figure 2b) illustrates the crucial effects of both
permafrost (through fire) and vegetation fraction distribution on biomass.
3.2. Climatic Causes for Changes in Carbon Pools
[12] The agreement of the DGVM output with observed
river runoff, remotely sensed burned area and with inventory-based NPP and biomass gradients demonstrates that the
LPJ-DGVM is able to simulate the correct magnitude of
carbon exchange of the boreal forest biome in Central
Siberia. It increases confidence also in the terrestrial carbon
balance derived (see auxiliary material). For the whole of
Russia, we calculate a moderate increase in the biomass of
the Russian forests of 74 TgC/a between 1983 and 1998.
This result agrees with the inventory-based analysis of
changes, which arrives at 76 TgC/a [Shvidenko and Nilsson,
2003]. In contrast, the change analysis of remotely sensed
cumulative growing season Normalised Difference Vegetation Index (NDVI) led to a much larger biomass increase of
284 TgC/a [Myneni et al., 2001]. The discrepancy with our
results originates from differences in the estimates mainly
for dense forests in the southern taiga. Here, cumulative
NDVI changes were translated into biomass changes of up
to 77 gC/m2/a [Myneni et al., 2001] while the LPJ-DGVM
calculates values lower than 23 gC/m2/a.
[13] In order to determine the climatic factors that were
responsible for this biomass increase, runs of the LPJDGVM were made for the time period 1901 –2003 with
selected climatic inputs allowed to vary as observed while
keeping the remaining monthly inputs constant at average
values (including the spinup time period). Previously it was
shown [Lucht et al., 2002] that variations in temperature
account for nearly all of the variability of LAI and NPP in
the global boreal zone. Our results, however, show that
increasing atmospheric CO2 concentrations were an additional cause for rising NPP in the Russian forests during the
last two decades (Figure 3a). As a result of increasing NPP
and on average nearly constant disturbances, the resulting
biomass of Russian forests is estimated to have increased
during the 1980s and 1990s. This increase is explained by
both changes in atmospheric CO2 content and temperature
(Figure 3b). The simulated increase in soil and litter carbon
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densities is due to increased litter fall (Figure 3c). Simulations with constant meteorological input under rising CO2
show the same behavior while trends in temperature lead to
decreasing model estimates of soil and litter carbon densities, due to the temperature dependence of Rh [Lloyd and
Taylor, 1994]. This effect acts to counter-balance the temperature-dependent carbon gain in vegetation. The resulting
net carbon balance of Russian forests in vegetation, litter and
soil is seen to have been mostly a result of increasing carbon
dioxide concentration in the atmosphere (Figure 3d).
[14] The carbon sink in the Russian forests between 1981
and 1999 is quantified from these computations to have
been 131 TgC/a. The largest uncertainty in this figure is
caused by the fire model used, which is based on climatically dependent ignition probabilities rather than actual
ignitions, producing fire emissions with reduced interannual
variability. If the actual occurrence of fires should have been
skewed toward later years in the period (for example, a
doubling of burned area between 1990 – 1994 and 1995–
1999 has been reported for Siberia [Conard et al., 2002]),
carbon emissions from fires may have increased by 110 TgC/a
rather than 40 TgC/a (mean modeled emission of 220 TgC/a,
and assuming a factor of two increase for the whole of Russia
from 1981–1999). The residual terrestrial sink would then
have been only 61 TgC/a. This figure does not take into
account, however, that more fire would have simultaneously
reduced Rh, and it would be reconcilable with the observed
[Shvidenko and Nilsson, 2003] biomass increase (see above)
if the model underestimated NPP increase or overestimated
mortality. In addition, forest expansion and management
could compensate higher disturbance-induced losses but are
not represented by this model analysis.
[15] Considering the area of these forests (21% of the
northern-hemisphere extra-tropical land), this carbon sink of
Figure 3. (a) Anomalies of simulated NPP and carbon
density of different pools: (b) biomass, (c) litter and soil
(d) and vegetation, litter and soil in Russian forests. Model
results are shown for full inputs (LPJ) and runs with
constant climatic inputs at 1901– 2003 averages but variable
temperature (LPJtmp), precipitation (LPJpre), cloudiness
(LPJcld) or CO2 content (LPJco2). LPJall represents model
results with only constant climatic inputs.
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between 7 and 13% of the total terrestrial carbon sink of 1 –
2 PgC/a in the Northern Hemisphere in the 1990s [e.g.,
Prentice et al., 2000] signals that other ecosystems or
processes in the Northern Hemisphere have been intensively
contributing to the global carbon balance, and that the
Russian forests have a limited ability (7%) to offset
CO2 emissions from fossil fuel use in Eurasia (European
and FSU states).
[16] Acknowledgments. We are indebted to H. Balzter and C. George
from the Centre for Ecology and Hydrology Monks Wood, UK for
providing satellite-derived burned area over the SIBERIA-II region from
1992 until 2003. Furthermore, we thank S. Sitch, B. Smith, D. Gerten,
S. Schaphoff, K. Thonicke, I. McCallum and S. Quegan for valuable
comments, data and contributions to the LPJ code. Financial support came
from the European Commission for the SIBERIA-II project (EVG2-200100008) and the German Ministry for Education and Researcher DEKLIM/
CVECA project (W.L., partly).
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