15 MAY 2003
MYHRE AND MYHRE
1511
Uncertainties in Radiative Forcing due to Surface Albedo Changes Caused by
Land-Use Changes
GUNNAR MYHRE*
Department of Geophysics, University of Oslo, Oslo, Norway
ARNE MYHRE
Telemark University College, Bø, Norway
(Manuscript received 19 February 2002, in final form 10 October 2002)
ABSTRACT
A radiative transfer model has been used for estimating the radiative forcing due to land-use changes. Five
global datasets for current vegetation cover and three datasets of preagriculture vegetation have been adopted.
The vegetation datasets have been combined with three datasets for surface albedo values. A distinct feature in
all the calculations is the negative radiative forcing at the northern midlatitudes due to the conversion of forest
to cropland. Regionally the radiative forcing is likely to be among the strongest of the climate forcing mechanisms.
A wider range is estimated for the global mean radiative forcing due to land-use changes than previously reported.
The single most important factor yielding the large range in estimated forcing is the cropland surface albedo
values. This underlines the importance of characterizing surface albedo correctly.
1. Introduction
Anthropogenic activity has yielded a wide range of
climate forcing mechanisms (Hansen et al. 1998; Shine
and Forster 1999; Houghton et al. 2001). Humans have
perturbed the radiative balance of the earth in two principal ways: combustion of fossil fuels and land-use
change. These activities have both resulted in increasing
concentrations of greenhouse gases and atmospheric
aerosols. The latter involves growth of aerosols from
biomass burning and possibly higher abundance of mineral dust aerosols, and surface albedo changes.
Betts (2000) showed the importance of taking vegetation changes (leading to surface albedo changes) into
account for further climate scenarios and in greenhouse
gas reduction negotiations. His study revealed that the
planting of trees to reduce the increase in atmospheric
CO 2 in order to mitigate global warming, may actually
lead to the opposite, as the vegetation changes result in
a heating in some regions. Govindasamy et al. (2001)
showed that the global cooling from about 1000 to 1900
A.D. (prior to the twentieth century’s warming) of about
0.25 K may be due to vegetation changes. In Houghton
* Additional affiliation: Norwegian Institute for Air Research,
Kjeller, Norway.
Corresponding author address: Dr. Gunnar Myhre, Dept. of Geophysics, P.O. Box 1022, Blindern, Oslo N-0315, Norway.
E-mail: gunnar.myhre@geofysikk.uio.no
q 2003 American Meteorological Society
et al. (2001) surface albedo changes are included for
the first time in these reports as a radiative forcing mechanism with a significant, but uncertain, forcing since the
preindustrial era.
Henderson-Sellers and Wilson (1983) identified several processes where human activities have altered the
surface albedo through vegetation changes. An important land vegetation cover change is the deforestation
in temperate and tropical regions, mostly for cropland
use. In some regions arid land has been transformed to
irrigated cropland. Urbanization has taken place due to
deforestation or conversion of cropland. A globally
small vegetation change arises from dam buildings. Finally, desertification, where shrubland is converted to
desert, may partly be caused by human activity.
One of the first studies to detect the land-use change
and the impact on climate was carried out by Charney
(1975). That study investigated feedback processes in
arid regions and suggested that overgrazing could lead
to desertification. In other studies on land-use change
and climate relationships desertification or tropical deforestation have been examined (Xue and Shukla 1993;
Nicholson et al. 1998; Zeng et al. 1999; Hoffmann and
Jackson 2000; Gedney and Valdes 2000; Kleidon and
Heimann 2000). However, more recently investigations
on land-use changes have been carried out around the
world and most frequently deforestation is considered
as the primary land-use change (Sagan et al. 1979;
Chase et al. 2000; Hansen et al. 1998; Pitman and Zhao
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2000; Zhao et al. 2001; Collatz et al. 2000; Betts 2001;
Govindasamy et al. 2001). All the studies revealed climate responses as a result of the land-use changes, on
a global scale or at least regionally. Collatz et al. (2000)
found that even land-use changes may influence the diurnal temperature range and that such human impact
may have caused some of the observed decreases in the
diurnal temperature range. These estimates of climate
impact caused by land-use changes are highly uncertain
because no consensus exists on which kind and size of
vegetation changes have taken place and further on parametric values (as surface albedo, roughness length, root
depth, canopy water capacity) for different vegetation
classes.
Radiative forcing is a tool that in a simple way can
be used to compare different climate forcing mechanisms (anthropogenic and natural; see further details in
Houghton et al. 2001; Shine and Forster 1999; Hansen
et al. 1997). Houghton et al. (2001) had a best estimate
of radiative forcing for surface albedo changes owing
to vegetation changes of 20.2 W m 22 since preindustrial
times based on the work of Hansen et al. (1998) and
Betts (2001). In Hansen et al. (1998) and Betts (2001)
the main vegetation change was conversion of forest to
cropland. Snow-covered areas have much higher surface
albedo over open land (as cropland) than in forested
areas. This effect causes a temperature decrease in the
case of deforestation, particularly at high latitudes,
which Betts (2000) highlighted for forestation. Part of
the deforestation for cropland clearing took place before
the industrial era started, as opposed to other humaninfluenced climate forcing mechanisms. As for several
of the other climate forcing mechanisms, the uncertainty
in the radiative forcing due to surface albedo changes
is very large. Part of this is owing to the few available
studies on global radiative forcing as a consequence of
surface albedo changes and the uncertainties discussed
above. Betts (2001) found that the global surface temperature change owing to vegetation changes is mainly
due to the surface albedo changes and that the radiative
forcing is a concept that can be applied in such climate
impact studies.
The few previous studies on global surface albedo
changes and its radiative forcing have mainly considered
changes in vegetation due to conversion to cropland.
Desertification is an issue that is not considered in detail
in these studies, but it is an important issue because
desertification gives a significant increase in the surface
albedo. However, the causes of desertification are complex, and vary from one part of the world to another.
In general, it is difficult to distinguish between humaninduced and climatically induced changes of the land
surface. The United Nations (2001) estimated that desertification had affected some 36 million km 2 of land
globally. The phenomenon was considered to be mainly
man-made. But the concept of desertification, extent of
affected land, and rate of change have been challenged
by a number of researchers (Helldén 1991; Tucker et
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al. 1991; Nicholson et al. 1998). In particular, this is
true for the Sahelian zone of Africa. Nicholson et al.
(1998) found that the vegetation cover within the Sahelian zone fluctuated from year to year during the period 1980–95 in accordance with the interannual variability of rainfall. Altogether, desertification can both
be a direct climate forcing mechanism and a climate
feedback.
Kuang and Yung (2000) found a 1% decrease in the
visible albedo over the last one to two decades due to
reduced snow cover, which corresponds to about a
2 W m 22 increase in the shortwave heating averaged
over the entire Northern Hemispheric land. Only a very
small part of this is a direct radiative forcing mechanism
as it is mainly a climate feedback from the global warming. Sturm et al. (2001a,b) showed that snow-cover
changes are not only due to global warming, but also
that the vegetation cover changes with an increase in
the shrub abundance in the Arctic. Both the snow-cover
and the vegetation change will strongly influence the
surface albedo.
Hansen et al. (1998) and Betts (2001) used the vegetation cover from Matthews (1983) and Wilson and
Henderson-Sellers (1985), respectively. In this study we
also use these two datasets along with three other vegetation datasets. Further, we apply surface albedo values
of vegetation classes from three different sources. The
main focus of this study is not to determine the most
likely human-influenced vegetation changes and their
radiative forcing but to investigate the range and uncertainty in their quantity.
2. Methods
a. Vegetation data and surface albedo values
We use five datasets for present global distribution of
vegetation. Three of the datasets have information on
human-influenced vegetation. Table 1 lists the five vegetation datasets and their key characteristics.
Ramankutty and Foley (1999) estimated historical
changes in global cropland. Their current vegetation is
based on a combination of remote sensing data and cropland inventory data in the year 1992. The dataset has
17 vegetation classes of which about half encompasses
forest. The dataset includes potential natural vegetation
(PNV) data based on Haxeltine and Prentice (1996) in
regions now dominated by human-influenced vegetation
cover. Note that the PNV dataset does not necessarily
represent the natural preagriculture vegetation, but rather the vegetation cover that would have most likely
existed there today with the current climate in the absence of human activities.
The Surface and Atmospheric Radiation Budget
(SARB) dataset has 18 vegetation classes according to
the International Geosphere Biosphere Programme
(IGBP) scheme. The data are determined by using a 1km map of satellite-retrieved vegetation cover. The
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TABLE 1. Description of the five vegetation datasets used in this study including the number of vegetation classes, whether PNV is
available in the dataset, the horizontal resolution, and the main sources of the dataset.
Classes
PNV
Resolution
Ramankutty and Foley (1999)
Reference
17
Yes
0.58 3 0.58
SARB*
Wilson and Henderson-Sellers (1985)
Mathews (1983)
Goldewijk (2001)
18
53
33
17
No
No
Yes
Yes
1/68
18
18
0.58
3
3
3
3
1/68
18
18
0.58
Main sources
A combination of remote sensing data,
cropland inventory data, and a terrestrial biosphere model.
Remotely sensed.
A variety of data sources, mainly atlases.
A variety of data sources, mainly atlases.
A combination of remote sensing data,
cropland inventory data, and a terrestrial biosphere model.
* More information is available online at http://www-surf.larc.nasa.gov/surf/.
SARB dataset has a high resolution with 36 grid points
within a 18 3 18 (10 min) horizontal resolution. For
each vegetation class a surface albedo value is also given, mainly based on Briegleb et al. (1986).
In Wilson and Henderson-Sellers (1985) a 18 3 18 horizontal resolution has been determined primarily from atlases. This dataset has 53 land cover classes, and both
primary and secondary land classes are given. The primary
land cover class occupies 75% of the grid cell and the
secondary 25%. In addition the dataset consists of three
main soil types. Surface albedo values are given for 24
general classes. Wilson and Henderson-Sellers (1985) indicate how to convert the 53 classes of land cover to the
24 albedo values. Both summer and winter surface albedo
values are presented. The three soil types have surface
albedo values and they depend on the soil moisture, which
is also given for a global distribution on 18 3 18.
Matthews (1983) has 33 surface vegetation classes in
a 18 3 18 resolution dataset. The data are mainly based
on atlases and over half of the classes are forest classes.
In addition to the PNV a cultivation intensity is given for
the human-influenced vegetation classes. All the 33 classes
have surface albedo values, which depend on season.
Remotely sensed satellite data of land cover and national land-use statistics are applied in Goldewijk
(2001). A resolution of 0.58 3 0.58 and 17 vegetation
classes are adopted in the dataset. The investigation exTABLE 2. Surface albedo values.
Vegetation type
Forest
Shrubland
Grass/savanna
Desert
Cropland
SARB [1]
0.12–0.17
0.22–0.23
0.18
0.36
0.15c
Wilson and
HendersonSellers (1985)
[2]
0.13–0.14
0.16–0.17
0.19–0.20
a
0.20b
Matthews
(1983) [3]
0.11–0.18
0.16–0.29
0.18–0.20
0.30
0.18
Depend on soil types described in Wilson and Henderson-Sellers
(1985) with a max value of 0.35.
Values are also given for rice, sugar, maize, cotton, and irrigated
crop and the values range from 0.12 to 0.25.
c
A value for pasture of 0.16 is given.
a
b
plicitly deals with pasture land, and the allocation of
cropland areas is steered by the presence of historical
population densities. The dataset has been combined
with two PNV datasets. The first is a modified version
of the BIOME model by Prentice et al. (1992; Ga) [see
further details in Goldewijk (2001)] and the second is
the same as PNV described in Ramankutty and Foley
(1999; Gb).
For the two vegetation datasets, which do not have information on the PNV [SARB and Wilson and HendersonSellers (1985)], information from Ramankutty and Foley
(1999) or Matthews (1983) has been adopted. This has
been done by replacing grid cells in SARB and Wilson
and Henderson-Sellers (1985) where human activity has
changed the land cover types (such as cropland and urbanization) with vegetation classes from the PNV dataset
from Ramankutty and Foley (1999) or Matthews (1983).
Table 2 shows the three datasets that include surface
albedo values for land cover types. While there is general agreement on the albedo associated with grassland,
the datasets reveal important differences between the
albedo values associated with forest, shrubland, and
cropland.
b. Comparison of human land-use change in various
vegetation datasets
This section estimates the human influence on the
vegetation cover in the five vegetation datasets by comparing the current vegetation with the PNV. The changes
are mainly due to expansion of cropland and to a very
small extent owing to urbanization.
1) CURRENT CROPLAND DISTRIBUTION
Figure 1 shows the geographical distribution of cropland for the five datasets as a fraction within a 18 3 18
resolution. The extent of cropland is substantially different in the five studies, with the SARB and Goldewijk
(2001) data having the highest fraction of cropland. In
particular the differences are large over South America,
South Africa, and Europe. Despite the large differences
in the fraction of cropland the general pattern is much
more similar. Matthews (1983) and Wilson and Hen-
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FIG. 1. Current cropland fraction for different vegetation datasets: (a) Ramankutty and Foley (1999), (b) SARB, (c) Wilson and
Henderson-Sellers (1985), (d) Matthews (1983), and (e) Goldewijk (2001).
derson-Sellers (1985) refer to a slightly different time
frame for current land cover compared to the other three
datasets and this describes a small part of the differences
in cropland. Only SARB and Goldewijk (2001) have
included pasture (which can be considered as part of
cropland) and the true extent of pasture is somewhat
uncertain. In Ramankutty and Foley (1999) the fraction
of cropland in each grid is given with the consequence
that large regions have croplands but only in small
amounts.
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FIG. 2. PNV in regions with human-influenced vegetation cover: (a) Ramankutty and Foley (1999), (b) SARB with Ramankutty and Foley
(1999) PNV, (c) Wilson and Henderson-Sellers (1985) with Ramankutty and Foley (1999) PNV, (d) Matthews (1983), (e) Goldewijk (2001)
with Gb PNV, and (f ) Goldewijk (2001) with Ga PNV.
2) HUMAN-INFLUENCED
VEGETATION
At mid- and high latitudes in the Northern Hemisphere the various vegetation datasets have in general
the same pattern. In these regions cropland has been
converted primarily from forest and to some extent from
grassland in the middle part of North America and parts
of Russia. However, in arid regions the differences are
much larger. Note that in Figs. 2a,b,c,e the Ramankutty
and Foley (1999) PNV data are used. The effect of
different PNV can be assessed by looking at Figs. 2e,f
where the same cropland distribution is adopted in both
but associated with different PNV datasets. The differences between these two figures are particularly evident
in Australia, India, and large parts of Africa.
c. Radiative transfer model
To assess the radiative forcing of the various current
vegetation and PNV we relied on radiative transfer calculations. We apply a multistream model using the discrete-ordinate (DISORT) method (Stamnes et al. 1988)
in the solar region. The radiative transfer calculations
are performed with eight streams. Rayleigh scattering
and clouds are included in the radiative transfer model,
and the exponential sum fitting method (Wiscombe and
Evans 1977) is used to account for absorption by gases.
The solar spectra are divided into four spectral regions
[see Myhre et al. (2002) for further details].
Our calculations are performed in T63 resolution
(about 1.98 3 1.98) with 19 vertical layers, adopting
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monthly mean global distribution of temperature, water
vapor, clouds, snow depth, and snow cover from the
European Centre for Medium-Range Weather Forecasts
(ECMWF) for the year 1996. The random cloud overlap
assumption is adopted, based on radar observations (Hogan and Illingworth 2000). The optical properties of
clouds are calculated using the procedure described in
Slingo (1989) with effective radius of 10 mm for low
clouds and 18 mm for high clouds (Stephens 1978; Stephens and Platt 1987).
The calculated global and annual mean shortwave
radiative flux at the top of the atmosphere (TOA) is
within 3 W m 22 of the measured radiative flux at the
TOA from the Earth Radiation Budget Experiment
(ERBE; Ramanathan et al. 1989). Global and annual
mean cloud radiative forcing at the TOA is 2–3 W m 22
weaker than the ERBE values indicating that the radiation budget is reasonably well reproduced with the radiative transfer model applied in this study.
The impact of snow on surface albedo is dependent
on the underlying land cover types. We have adopted
the procedure applied in the Hadley Centre climate model (Cox et al. 1999; Betts 2000) to calculate surface
albedo in snow-covered regions. This albedo depends
on the surface albedo for snow-free vegetation (A 0 ),
snow-covered vegetation (A S ), and snow depth (S). The
surface albedo is calculated as
A 5 A 0 1 (A S 2 A 0 )(1 2 e 20.2S ).
(1)
The snow depth is given in kg m 22 . The values of A 0
and A S are given for each vegetation class, and the difference between A S and A 0 obtains the largest magnitude
in the case of open land.
3. Results
a. Surface albedo changes
Figure 3 displays surface albedo changes for the five
datasets. Various combinations of the current vegetation
datasets, PNV, and surface albedo datasets have been
performed, but the main results presented in this study
are based on the following assumptions:
1) The Ramankutty and Foley (1999) current vegetation
data and PNV are combined with Wilson and Henderson-Sellers (1985) surface albedo values.
2) Current vegetation and surface albedo values from
SARB are combined with PNV from Ramankutty
and Foley (1999).
3) For Wilson and Henderson-Sellers (1985) current
vegetation data and surface albedo values are combined with PNV from Ramankutty and Foley (1999)
(same as for SARB).
4) Matthews (1983) includes both current vegetation
data, PNV, and surface albedo values.
5) Current vegetation data from Goldewijk (2001) are
combined with surface albedo values from Wilson
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and Henderson-Sellers (1985) and two PNV datasets
(Ga and Gb).
A general pattern in all the datasets is the increase in
the surface albedo in the Northern Hemisphere midlatitudes, particularly evident in eastern Europe and the eastern United States. This is due to the conversion of forest
to cropland and the influence of snow cover over such
vegetated areas. The magnitude of the surface albedo increase in this area highly depends on the fraction of cropland and the surface albedo of the vegetation classes. In
experiments with snow cover excluded in the simulations
the surface albedo changes substantially compared to results displayed in Fig. 3, and even the sign of the global
mean albedo change. Albedo changes in regions other than
the Northern Hemisphere midlatitudes show a much larger
variation both in extent and magnitude between the datasets. This can be partly seen from Figs. 1 and 2, which
demonstrate the large variations in extent of cropland in
the datasets and the variations in PNV. Further the surface
albedo for various vegetation classes differs between the
albedo datasets (Table 2).
Figures 3e,f have the same current vegetation from
Goldewijk (2001) but with two different PNV datasets.
At the Northern Hemisphere midlatitudes the albedo
changes in these two simulations are rather consistent
with the largest difference over North America. On the
other hand there are large differences between the figures, particularly over Australia and India. The differences in surface albedo changes in the two datasets were
much smaller when pasture was excluded. The Goldewijk (2001) dataset includes a large fraction of pasture in
arid regions. In arid regions it is very uncertain how a
conversion to pasture influences the surface albedo, but
likely the changes are modest. In our simulations the
difference in surface albedo values between pasture and
barren is substantial. This results in a large decrease in
the surface albedo in the arid regions with conversion
to pasture, as a result of an unrealistically low surface
albedo of pasture in these areas. Satellite observations
indicate that pasture in such regions have not decreased
the surface albedo as shown in Figs. 3e,f. Therefore
calculated albedo changes based on Goldewijk (2001)
data and the two different PNV are in much better agreement when surface albedo changes have not been taken
into account in regions with the PNV of the desert. This
is illustrated in Figs. 3g,h with no conversion from barren to pasture, and in this case with the albedo values
from Wilson and Henderson-Sellers (1985) resulted in
no reduction in surface albedo. The corresponding results on radiative forcing with conversion from barren
to pasture included and excluded will be shown.
Among the datasets used in this study the SARB dataset has the best representation of urbanization. However, surface albedo changes based on the SARB data
for urbanization are a factor of 1000 smaller than for
cropland, as a result of a much smaller fraction of urban
areas than cropland. Therefore for global studies we can,
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FIG. 3. Surface albedo changes since preagriculture time: (a) Ramankutty and Foley (1999), (b) SARB, (c) Wilson and Henderson-Sellers
(1985), (d) Matthews (1983), (e) Goldewijk (2001) with Gb PNV, (f ) Goldewijk (2001) with Ga PNV, (g) Goldewijk (2001) with Gb PNV
and no change in barren regions, and (h) Goldewijk (2001) with Ga PNV and no change in barren regions.
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at present, ignore changes in surface albedo caused by
urbanization.
b. Radiative forcing
1) SENSITIVITY
STUDIES
To illustrate some of the factors influencing the radiative forcing due to surface albedo changes, we have
performed some sensitivity experiments increasing the
surface albedos associated with the Ramankutty and
Foley (1999) vegetation classes by 0.01. Figure 4a demonstrates the clear-sky (cloud excluded in the calculations) radiative forcing due to an increase in the surface
albedo of 0.01 for snow-free land cover classes [A 0 in
Eq. (1)]. The snow albedo A S in the same equation is
not increased. The strongest radiative forcing is in tropical regions owing to stronger insolation than at higher
latitudes. The forcing is rather homogeneous in tropical
regions, and it decreases with latitude. At higher latitudes both the weaker insolation and more snow are
factors decreasing the radiative forcing.
Figure 4b is similar to Fig. 4a, but clouds are included
in the calculations. The radiative forcing due to surface
albedo increases is weaker when clouds are included as
less solar radiation is available to be reflected at the
surface. The strongest impact of clouds on the radiative
forcing is in regions with large cloud amounts at midlatitudes and around the equator. However, over arid
regions the cloud amount is low and smaller differences
compared to Fig. 4a are found.
In Fig. 4c the surface albedo values are increased by
0.01 over all land areas, even in regions with large snow
depths. This strengthens the radiative forcing at high
latitudes compared to Fig. 4b and demonstrates that
snow cover along with the solar insolation and clouds
are factors making large spatial differences in the radiative forcing for even the same surface vegetation.
Therefore, with similar vegetation change the radiative
forcing can be a factor of 5 higher in certain tropical
regions compared to mid- and high latitudes. Further, it
was found that even in regions experiencing a similar
surface albedo change, the radiative forcing is up to a
factor of 3 higher in the Tropics compared to mid- and
high latitudes.
2) REALISTIC
VEGETATION CHANGES
In this part our goal is to illustrate the similarities
and differences in the radiative forcing based on our
←
FIG. 4. Radiative forcing due to an increase in land surface albedo
of 0.01: (a) clear-sky forcing due to an increase in albedo for snowfree land cover types, (b) forcing when clouds are included with an
increase in albedo for snow-free land cover types, and (c) forcing
when clouds are included and there is an increase in albedo over all
land areas.
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FIG. 5. Radiative forcing due to human-influenced changes in vegetation cover (a), (c), (e) based on Ramankutty and Foley (1999) for
various surface albedo values and (b), (d), (f ) for Goldewijk (2001) with Gb PNV for various surface albedo values. The dataset of surface
albedo values are used from SARB in (a), (b); Wilson and Henderson-Sellers (1985) in (c), (d); and Matthews (1983) in (e), (f).
five different vegetation datasets. Moreover, we will focus on the uncertainties introduced by the surface albedo
values for the vegetation classes.
Figures 5a,c,e show radiative forcings due to vegetation changes based on current Ramankutty and Foley
(1999), and Figs. 5b,d,f are based on current Goldewijk
(2001) data. The PNV from Ramankutty and Foley
(1999) have been adopted for both datasets in the figure.
Simulations have been performed for various sets of
albedo values corresponding to the vegetation classes
in order to investigate the importance of these values
(Table 2). In Figs. 5a,b; 5c,d; 5e,f the following datasets
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TABLE 3. Global and annual mean radiative forcing (W m 22 ) due
to vegetation changes from preagriculture time based on Ramankutty
and Foley (1999) and Goldewijk (2001) for different sets of surface
albedo values. Numbers in parentheses are for the Goldewijk (2001)
vegetation data when pasture is excluded in barren regions.
Surface albedo set
1) SARB
2) Wilson and HendersonSellers (1985)
3) Matthews (1983)
Ramankutty
and Foley
(1999)
Goldewijk (2001)
20.02
0.28 (0.10)
20.29
20.19
20.51 (20.66)
20.19 (20.36)
for surface albedo values are applied: SARB, Wilson
and Henderson-Sellers (1985), and Matthews (1983),
respectively.
The main common feature of Figs. 5a–f is the strong
negative radiative forcing at the Northern Hemisphere
midlatitudes, which is somewhat dependent on the values selected for surface albedos. Additionally, conversion of tropical rainforest to cropland gives a small negative forcing in all the simulations owing to only a small
albedo change in this area. This forcing is small and
the extent of this conversion differs in the two vegetation
datasets. Another distinct and evident pattern in all the
simulations with the Goldewijk (2001) vegetation data,
and to a much lesser extent with the Ramankutty and
Foley (1999) vegetation data is the strong positive radiative forcing over parts of the Sahara and the Arabian
Deserts due to conversion of arid to agriculture land.
These simulations give a strong reduction in the surface
reflection, although it is not realistic with a large change
in surface albedo (and radiative forcing) caused by conversion to cropland in such regions. In other regions
than those mentioned the radiative forcing differs substantially with surface albedo values and even the sign
of the forcing. In summary, the uncertainty in the radiative forcing owing to the different surface albedo
datasets overwhelms the differences between the vegetation datasets.
Table 3 summarizes the global annual mean radiative
forcing due to vegetation changes since the preagriculture time shown in Fig. 5. Similar to Fig. 5 the table
reveals that the radiative forcing differs substantially
between the two vegetation datasets, but the surface
albedo datasets are responsible for even larger differences in the radiative forcing. Excluding pasture in barren regions in the calculation with vegetation data from
Goldewijk (2001) impacts the global mean forcing significantly. The agreement in global mean forcing applying surface albedo values from Matthews (1983) is
somewhat coincidental, as can be seen from Figs. 5e,f).
In Houghton et al. (2001) the estimates for the radiative forcing mechanisms are given for changes since
1750 to the present. So far in this article the forcings
since preagriculture time have been presented, but part
of the vegetation changes have taken place before 1750.
Radiative forcing calculations based on the vegetation
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data in Ramankutty and Foley (1999) indicate that about
one-third of the present forcing took place before 1750
independent of surface albedo values (not shown). This
relation of forcings was not the case based on Goldewijk
(2001) vegetation data. In particular when the present
forcing is positive the forcing in 1750 was slightly negative. The forcings in 1750 were slightly larger than
one-third of the magnitude of the present radiative forcing for the two cases with negative radiative forcings.
Figure 6 shows radiative forcing due to vegetation
changes from SARB, Wilson and Henderson-Sellers
(1985), Matthews (1983), and Goldewijk (2001). Their
surface albedo changes are displayed in Figs. 3b,c,d,f,
respectively. In addition the radiative forcing is calculated from Goldewijk (2001) vegetation data with the
two PNV datasets when pasture is excluded in barren
regions. The surface albedo changes are displayed in
Figs. 3g,h. The main pattern in the two datasets in Fig.
5 with various surface albedo values can also be seen
in Fig. 6. Parts of the differences are due to unequal
cropland cover as demonstrated in Fig. 1.
In Fig. 6a with SARB vegetation data the main reason
for the large areas with positive radiative foring is related to surface albedo values adopted in this dataset,
which can also be seen in Figs. 5a,b.
The radiative forcing in Fig. 6b is calculated from the
Wilson and Henderson-Sellers (1985) dataset with PNV
from Ramankutty and Foley (1999). The distribution in
radiative forcing is rather similar to Betts (2001) who
adopted the same dataset with another PNV dataset. The
magnitude of the global and annual mean radiative forcing is stronger in our results. The difference is within
what would be expected from different PNV datasets
and models.
Figure 6c shows results based on Matthews (1983)
data. The forcing is in reasonable agreement, except
over Pakistan and India, with Hansen et al. (1998) who
applied the same data. In these countries we model a
positive forcing whereas the opposite was the case in
Hansen et al. (1998). The PNV in Matthews (1983) in
this region has a large fraction of shrub as well as desert
with a higher surface albedo than agricultural land,
which gives a positive forcing in our study. The modeled
transition to lower surface albedo in this region is the
main reason for the weaker global mean radiative forcing in our study compared to Hansen et al. (1998).
The results in Fig. 6d are based upon Goldewijk’s
(2001) vegetation dataset with the Ga PNV combined
with surface albedo values from Wilson and HendersonSellers (1985). This can therefore be compared to Fig.
5d as the only difference is the PNV. The largest differences are in Australia and India, and the global mean
radiative forcing is less negative in Fig. 6d compared
to Fig. 5d. When pasture is not taken into account the
difference between the two results is much smaller. This
was also the case when omitting to change the PNV
albedo value of barren as can be seen in Figs. 6e and
15 MAY 2003
MYHRE AND MYHRE
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FIG. 6. Radiative forcing due to human-influenced vegetation changes: (a) SARB, (b) Wilson and Henderson-Sellers (1985), (c) Matthews
(1983), (d) Goldewijk (2001) with Ga PNV, (e) Goldewijk (2001) with Gb PNV and no change in barren regions, and (f ) Goldewijk (2001)
with Ga PNV and no change in barren regions.
6f. Notice also that in the latter case positive radiative
forcing values are almost nonexisting.
Table 4 summarizes the results of the global and annual mean albedo and radiative forcing due to vegetation
changes presented in Figs. 3 and 6. The table reveals
that there is no linear relationship between albedo
changes and radiative forcing in accordance with Fig.
4. Radiative forcing from these datasets is within the
range given in Table 3.
To identify the range of radiative forcing based on
the five vegetation datasets and the three surface albedo
datasets used in this study we performed some additional calculations. A minimum value of 20.55 W m 22
was found when the SARB data was combined with
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JOURNAL OF CLIMATE
TABLE 4. Surface albedo changes and radiative forcing (W m 22 )
due to vegetation changes from preagriculture time to present from
various vegetation datasets.
Vegetation data
SARB
Wilson and Henderson-Sellers (1985)
Matthews (1983)
Goldewijk (2001) with Ga PNV
Goldewijk (2001) with Gb PNV*
Goldewijk (2001) with Ga PNV*
Surface
Radiaalbedo Albedo
tive
set
change forcing
1
2
3
2
2
2
0.0021
0.0025
0.0015
0.0034
0.0053
0.0056
20.11
20.29
20.12
20.36
20.66
20.65
* No change in the surface albedo of pasture in barren regions.
Wilson and Henderson-Sellers (1985) surface albedo
values (Table 5). An additonal estimate, which we feel
is more uncertain, can be found by combination of the
same surface albedo values and the Goldewijk (2001)
data with no change in surface albedo in desert areas
of 20.66 W m 22 (see Table 4). Goldewijk (2001) with
Ga PNV vegetation data together with SARB surface
albedo values resulted in a maximum forcing of 0.47
W m 22 . In the case of no change in surface albedo in
areas of conversion of barren to pasture and use of Ga
PNV and Gb PNV gave the same global mean radiative
forcing of 0.10 W m 22 . To uncover the most important
factor in the calculation of the radiative forcing due to
vegetation changes we carried out two simulations
where only the surface albedo values of cropland have
been altered in the Wilson and Henderson-Sellers (1985)
surface albedo dataset (Table 5). These calculations reveal that most of the differences in global and annual
mean radiative forcing arise from the deviations in cropland surface albedo values (see Table 2). In fact, the
radiative forcing range is from 20.06 to 20.29 W m 22
for the current Ramankutty and Foley (1999) vegetation
data. Interestingly, this almost covers the same range of
values for this vegetation dataset as shown in Table 3.
4. Summary
We have used a radiative transfer scheme and various
vegetation datasets combined with surface albedo datasets
to estimate radiative forcing due to human-induced land
vegetation changes. The uncertainty in the global and annual mean radiative forcing due to vegetation changes
since preagriculture time is large, and the results are in
VOLUME 16
the large range of 20.6 to 0.5 W m 22. However, the
positive forcing is calculated in only a few cases and involves a large reduction in surface albedo in arid regions
with conversion to pasture. In actuality, surface albedo
changes in these regions are likely to have been small.
We therefore assess that the radiative forcing due to vegetation changes of human impact is probably negative in
accordance with previous estimates.
We find a consistent pattern between the various datasets
with a strong negative forcing at the northern midlatitudes.
These regions originally had mainly forests that are converted to cropland. In general this leads to an increase in
surface albedo, but the main contribution to the increase
in surface albedo is the different albedo values regarding
snow cover of forest and cropland. Tropical deforestation
to cropland has also given a negative radiative forcing,
but the extent of the forcing varies more between the
vegetation datasets and is much smaller than for the potentially snowy northern midlatitudes. The radiative forcing due to vegetation changes in other regions differs substantially between the datasets.
Altogether we used three datasets with surface albedo
values associated with different land vegetation classes.
The datasets have significant differences in the surface
albedo values with regard to cropland, forest, and shrubland. These differences are the dominating factor causing the large range in the radiative forcing. In particular,
coupling the wide range of albedo values for crops with
the large differences in cropland extent among the vegetation datasets results in a large variation in radiative
forcing results. Recent satellite remote sensing surface
albedo values for various vegetation classes in different
regions and seasons also reveal large variations (Strugnell and Lucht 2001; Strugnell et al. 2001; Jacob et al.
2002). However, these new albedo values give no clear
indications concerning which of our datasets is the most
realistic.
We have demonstrated that the radiative forcing is
not linear with vegetation changes and surface albedo
changes. In general, tropical regions have a stronger
forcing than at higher latitudes for the same vegetation
change or surface albedo change. Further, we estimate
that about one-third of the current radiative forcing due
to vegetation changes brought about by human activities
had developed before 1750, except for large and probably unrealistic vegetation changes in arid regions.
To reduce the uncertainties in the radiative forcing
TABLE 5. Results of sensitivity tests of the radiative forcing (W m 22 ) due to vegetation changes since preagricultural time. The lower part
of the table gives radiative forcing owing to variation in surface albedo values of cropland.
Vegetation data
Surface albedo set
Radiative forcing
Description
SARB
Goldewijk (2001) with Ga PNV
Goldewijk (2001) with Ga PNV
Ramankutty and Foley (1999)
Ramankutty and Foley (1999)
Ramankutty and Foley (1999)
2
1
1
2
2
2
20.55
0.47
0.10
20.06
20.29
20.20
Minimum radiative forcing
Maximum radiative forcing
No change in barren areas
Cropland value 1 (0.15)
Cropland value 2 (0.20; as in Table 3)
Cropland value 3 (0.18)
15 MAY 2003
MYHRE AND MYHRE
due to human-induced land vegetation changes the following issues are important.
1) An improved dataset of the current vegetation dataset
(including various cultivation classes) should be established with a reasonable resolution for global calculations based on existing datasets and especially
emphasizing the 1-km resolution dataset by Loveland and Belward (1997) or from the Moderate Resolution Imaging Spectroradiometer (MODIS; Friedl
et al. 2002).
2) More information is needed regarding preagricultural vegetation in current human-influenced regions.
3) We underscore the immediate need for better data
on surface albedo values for different vegetation
classes in further studies regarding radiative impact
or climate effects caused by land-use changes. Some
of this information can certainly be retrieved and
amended from careful use of satellite data in regions
with known vegetation. The surface albedo data in
Wilson and Henderson-Sellers (1985) include values
of different cultivation classes, which are very useful
taking into account the large variability in the surface
albedo values of cultivated land. The MODIS data
will give much useful information in this respect
(Schaaf et al. 2002).
4) In this study we have not considered desertification,
but this has the potential for generating a significant
radiative forcing as the difference in surface albedo
between desert and other vegetation is substantial.
A major question is whether desertification is mainly
a direct human influence or a climate feedback.
Furthermore the radiative forcing of land-use change
is linked to climate feedback. In the Northern Hemisphere midlatitude the radiative forcing due to vegetation changes is dependent on the snow distribution, and
snow has an important role as a climate feedback mechanism. The forcing of land-use change may also be
linked to another radiative forcing mechanism as soot
from human activity reduces the snow albedo (Hansen
and Sato 2001).
Acknowledgments. We thank Norvald Fimreite, Alf
Bårtvedt, and Bjørn Steen for suggestions during the preparation of our manuscript. The authors thank several researchers for sending or making vegetation data available.
REFERENCES
Betts, R. A., 2000: Offset of the potential carbon sink from boreal
forestation by decreases in surface albedo. Nature, 408, 187–
190.
——, 2001: Biogeophysical impacts of land use on present-day climate: Near-surface temperature change and radiative forcing.
Atmos. Sci. Lett., 2, doi:10.1006/asle.2000.0023.
Briegleb, B. P., P. Minnis, V. Ramanathan, and E. Harrison, 1986:
Comparison of regional clear-sky albedos inferred from satellite
observations and models comparisons. J. Climate Appl. Meteor.,
25, 214–226.
1523
Charney, J. G., 1975: Dynamics of deserts and drought in the Sahel.
Quart. J. Roy. Meteor. Soc., 101, 193–202.
Chase, T. N., R. A. Pielke Sr., T. G. F. Kittel, R. R. Nemani, and S.
W. Running, 2000: Simulated impacts of historical land cover
changes on global climate in northern winter. Climate Dyn., 16,
93–105.
Collatz, G. J., L. Bounoua, S. O. Los, D. A. Randall, I. Y. Fung, and
P. J. Sellers, 2000: A mechanism for the influence of vegetation
on the response of the diurnal temperature range to changing
climate. Geophys. Res. Lett., 27, 3381–3384.
Cox, P. M., R. A. Betts, C. B. Bunton, R. L. H. Essery, P. R. Rowntree,
and J. Smith, 1999: The impact of new land surface physics on
the GCM simulation of climate and climate sensitivity. Climate
Dyn., 15, 183–203.
Friedl, M. A., and Coauthors, 2002: Global land cover mapping from
MODIS: Algorithms and early results. Remote Sens. Environ.,
83, 287–302.
Gedney, N., and P. J. Valdes, 2000: The effect of Amazonian deforestation on the Northern Hemisphere circulation and climate.
Geophys. Res. Lett., 27, 3053–3056.
Goldewijk, K. K., 2001: Estimating global land use change over the
past 300 years: The HYDE database. Global Biogeochem. Cycles, 15, 417–434.
Govindasamy, B., P. B. Duffy, and K. Caldeira, 2001: Land use changes and Northern Hemisphere cooling. Geophys. Res. Lett., 28,
291–294.
Hansen, J. E., and M. Sato, 2001: Trends of measured climate forcing
agents. Proc. Natl. Acad. Sci. USA, 98, 14 778–14 783.
——, ——, and R. Ruedy, 1997: Radiative forcing and climate response. J. Geophys. Res., 102, 6831–6864.
——, ——, A. Lacis, R. Ruedy, I. Tegen, and E. Matthews, 1998:
Climate forcings in the Industrial Era. Proc. Natl. Acad. Sci.
USA, 95, 12 753–12 758.
Haxeltine, A., and C. Prentice, 1996: BIOME 3: An equilibrium terrestrial biosphere model based on ecophysiological constraints,
resource availability, and competition among plant functional
types. Global Biogeochem. Cycles, 10, 693–709.
Helldén, U., 1991: Desertification—Time for an assessment? Ambio,
20, 372–383.
Henderson-Sellers, A., and M. F. Wilson, 1983: Surface albedo data
for climatic modeling. Rev. Geophys. Space Phys., 21, 1743–
1778.
Hoffmann, W. A., and R. B. Jackson, 2000: Vegetation–climate feedbacks in the conversion of tropical savanna to grassland. J. Climate, 13, 1593–1602.
Hogan, R. J., and A. J. Illingworth, 2000: Deriving cloud overlap
statistics from radar. Quart. J. Roy. Meteor. Soc., 126, 2903–
2909.
Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der
Linden, X. Dai, K. Maskell, and C. A. Johnson, Eds., 2001:
Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.
Jacob, F., A. Olioso, M. Weiss, F. Baret, and O. Hautecoeur, 2002:
Mapping short-wave albedo of agricultural surfaces, using airborne POLDER data. Remote Sens. Environ., 80, 36–46.
Kleidon, A., and M. Heimann, 2000: Assessing the role of deep rooted
vegetation in the climate system with model simulations: Mechanism, comparison to observations and implications for Amazonian deforestation. Climate Dyn., 16, 183–199.
Kuang, Z., and Y. L. Yung, 2000: Observed albedo decrease related
to the spring snow retreat. Geophys. Res. Lett., 27, 1299–1302.
Loveland, T. R., and A. S. Belward, 1997: The IGBP-DIS global 1
km land cover data set, DISCover: First results. Int. J. Remote
Sen., 18, 3291–3295.
Matthews, E., 1983: Global vegetation and land use: New high-resolution data bases for climate studies. J. Climate Appl. Meteor.,
22, 474–487.
Myhre, G., J. E. Jonson, J. Bartnicki, F. Stordal, and K. P. Shine,
2002: Role of spatial and temporal variations in the computation
1524
JOURNAL OF CLIMATE
of radiative forcing due to sulphate aerosols: A regional study.
Quart. J. Roy. Meteor. Soc., 128, 973–989.
Nicholson, S. E., C. J. Tucker, and M. B. Ba, 1998: Desertification,
drought, and surface vegetation: An example from the West African Sahel. Bull. Amer. Meteor. Soc., 79, 815–829.
Pitman, A. J., and M. Zhao, 2000: The relative impact of observed
change in land cover and carbon dioxide as simulated by a climate model. Geophys. Res. Lett., 27, 1267–1270.
Prentice, I. C., W. Cramer, S. P. Harrison, R. Leemans, R. A. Monserud, and A. M. Solomon, 1992: A global biome model based
on plant physiology and dominance, soil properties and climate.
J. Biogeogr., 19, 117–134.
Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243, 57–63.
Ramankutty, N., and J. Foley, 1999: Estimating historical changes in
global land cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles, 13, 997–1027.
Sagan, C., O. B. Toon, and J. B. Pollack, 1979: Anthropogenic climate
changes and the Earth’s climate. Science, 206, 1363–1368.
Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo,
and nadir reflectance products from MODIS. Remote Sens. Environ., 83, 135–148.
Shine, K. P., and P. M. de F. Forster, 1999: The effects of human
activity on radiative forcing of climate change: A review of
recent developments. Global Planet. Change, 20, 205–225.
Slingo, A., 1989: A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos. Sci., 46, 1419–1427.
Stamnes, K., S.-C. Tsay, W. Wiscombe, and K. Jayaweera, 1988: A
numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media.
Appl. Opt., 27, 2502–2509.
Stephens, G. L., 1978: Radiation profiles in extended water clouds.
I: Theory. J. Atmos. Sci., 35, 2111–2122.
VOLUME 16
——, and C. M. R. Platt, 1987: Aircraft observations of the radiative
and microphysical properties of stratocumulus and cumulus
cloud fields. J. Climate Appl. Meteor., 26, 1243–1269.
Strugnell, N. C., and W. Lucht, 2001: An algorithm to infer continental-scale albedo from AVHRR data, land cover class, and
field observations of typical BRDFs. J. Climate, 14, 1360–1376.
——, ——, and C. Schaaf, 2001: A global albedo data set derived
from AVHRR data for use in climate simulations. Geophys. Res.
Lett., 28, 191–194.
Sturm, M., J. P. McFadden, G. E. Liston, F. Stuart Chapin III, C. H.
Racine, and J. Holmgren, 2001a: Snow–shrub interactions in
Arctic tundra: A hypothesis with climate implications. J. Climate, 14, 336–344.
——, C. Racine, and K. Tape, 2001b: Increasing shrub abundance
in the Arctic. Nature, 411, 546–547.
Tucker, C. J., H. E. Dregne, and W. W. Newcomb, 1991: Expansion
and contraction of the Sahara Desert from 1980 to 1990. Science,
253, 299–301.
United Nations, cited 2001: 3600 million hectares seriously affected
by desertification. FAO Press Release PR 01/38. [Available online
at http://www.fao.org/WAICENT/OIS/PRESSpNE/PRESSENG/
2001/pren0138.htm.]
Wilson, M. F., and A. Henderson-Sellers, 1985: A global archive of
land cover and soils data for use in general circulation climate
models. J. Climatol., 5, 119–143.
Wiscombe, W., and J. Evans, 1977: Exponential-sum fitting of radiative transmission functions. J. Comput. Phys., 24, 416–444.
Xue, Y., and J. Shukla, 1993: The influence of land surface properties
on Sahel climate. Part I: Desertification. J. Climate, 6, 2232–2245.
Zeng, N., J. D. Neelin, K.-M. Lau, and C. J. Tucker, 1999: Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science, 286, 1537–1540.
Zhao, M., A. J. Pitman, and T. N. Chase, 2001: The impact of land
cover change on the atmospheric circulation. Climate Dyn., 17,
467–477.