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GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L18817, doi:10.1029/2007GL030472, 2007
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Anthropocene changes in desert area: Sensitivity to climate model
predictions
Natalie M. Mahowald1,2
Received 23 April 2007; revised 29 June 2007; accepted 27 August 2007; published 29 September 2007.
[1] Changes in desert area due to humans have important
implications from a local, regional to global level. Here I
focus on the latter in order to better understand estimated
changes in desert dust aerosols and the associated iron
deposition into oceans. Using 17 model simulations from
the World Climate Research Programme’s Coupled Model
Intercomparison Project phase 3 multi-model dataset and
the BIOME4 equilibrium vegetation model, I estimate
changes in desert dust source areas due to climate change
and carbon dioxide fertilization. If I assume no carbon
dioxide fertilization, the mean of the model predictions is
that desert areas expand from the 1880s to the 2080s, due to
increased aridity. If I allow for carbon dioxide fertilization,
the desert areas become smaller. Thus better understanding
carbon dioxide fertilization is important for predicting
desert response to climate. There is substantial spread in the
model simulation predictions for regional and global
averages. Citation: Mahowald, N. M. (2007), Anthropocene
changes in desert area: Sensitivity to climate model predictions,
Geophys. Res. Lett., 34, L18817, doi:10.1029/2007GL030472.
1. Introduction
[2] Changes in desert area impact the human living in
those areas substantially and can impact the surface albedo
and feedback onto climate. In addition, the changes in desert
area may lead to changes in desert dust source generation,
which has important implications for biogeochemistry and
climate [e.g., Martin, 1990; Miller and Tegen, 1998].
Previous studies have highlighted the potentially important
role that human induced climate change will have on desert
size and dust generation [e.g., Mahowald and Luo, 2003;
Tegen et al., 2004; Woodward et al., 2005]. However,
previous studies have used only one or two models in order
to assess the impact of climate onto mineral aerosols [e.g.,
Mahowald and Luo, 2003; Tegen et al., 2004; Woodward et
al., 2005; Mahowald et al., 2006a]. In this study I explore
the predictions of climate change from 17 different general
circulation models compiled in World Climate Research
Programme’s Coupled Model Intercomparison Project
phase 3 (CMIP3) multi-model dataset and use the BIOME4
model to convert the precipitation, insolation and temperature changes from those models into predictions of desert.
The goals of this study are 1) to identify desert dust source
regions which may change (or already have changed) due to
the impact of anthropogenic climate change, and 2) to look
1
National Center for Atmospheric Research, Boulder, Colorado, USA.
Now at Department of Earth and Atmospheric Sciences, Cornell
University, Ithaca, New York, USA.
for the sensitivity of the results to the general circulation
model used for the climate predictions.
[3] Carbon dioxide fertilization of arid plants is a potentially important mechanism for changing the size of desert
dust source regions [e.g., Mahowald et al., 1999]. Atmospheric carbon dioxide concentrations influence plant stomatal conductance because as CO2 diffuses into the leaf,
water diffuses out. This effect is particularly pronounced
under conditions of low atmospheric humidity, where stomatal control is an important regulator of net CO2 uptake by
plants. Under high ambient CO2 concentrations, more CO2
could be expected to diffuse into the leaf for unit water loss,
increasing plant water use efficiency and thus productivity
and survival under arid conditions. There is evidence that
this mechanism exists in the real world, but the magnitude is
controversial [e.g., Smith et al., 2000; Caspersen et al.,
2000]. Therefore I include sensitivities studies of the impact
of carbon dioxide fertilization effects in the simulations in
order to determine the importance of this mechanism.
[4] Humans can also impact desert dust sources through
direct perturbation of the soils during land use or water
diversion. The importance of human land use in perturbing
atmospheric dust is highly uncertainly and is estimated to be
between 0– 50% in the current climate [e.g., Mahowald and
Luo, 2003; Tegen et al., 2004; Mahowald et al., 2004;
Moulin and Chiapello, 2006; Mahowald et al., 2007]. The
impact of water use on atmospheric dust has also not been
assessed at a global level, but a recent study suggests no
trend in dustiness downwind of the Aral Sea, which has
substantially reduced in size during the last 50 years due to
human water use [Mahowald et al., 2007]. While human
land use is likely to be important for desert dust generation,
it is beyond the scope of this study to consider.
[5] I limit my discussion here to changes in desert dust
source area: calculating the dust source strength requires the
diurnal cycle of friction velocity of winds which is not
archived in the PCMDI archive [e.g., Luo et al., 2004]. In
addition, there may be fluctuations in the transport and
lifetime of the aerosols that are different in different climate
models, which I cannot account for in this study. I also
neglect the feedback of changes in desert or desert dust
itself onto climate in this study [Yoshioka et al., 2007].
However, this study represents a significant step forward, as
changes in the source areas can account for a large fraction
of the changes seen in different climates [e.g., Mahowald et
al., 1999, 2006a].
2. Methodology
2
Copyright 2007 by the American Geophysical Union.
0094-8276/07/2007GL030472$05.00
[6] In order to determine desert area changes, the BIOME4
model was used [Kaplan et al., 2003]. This model determines the optimal plant functional type for each grid cell on
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Figure 1. Globally averaged change in desert area predicted for each CMIP3 model simulation using the BIOME4 model
(a) without and (b) with carbon dioxide fertilization. Each line represents a different model (listed in color), and average of
all models is in black, with diamonds.
a 0.5 0.5 degree cell based on which type out-competes
the other plant types. This model will determine the equilibrium vegetation, which is attained in many ecosystems
after hundreds of years. Because the vegetation in arid
regions (like annual grasses) is likely to respond quickly
to the evolution of the climate [Tucker and Nicholson,
1999], I can use the model for 20 year average time slices
every 50 years to look at the evolution of the deserts as
predicted by this set of models.
[7] Similar to previous studies, I use an anomaly method,
where the monthly mean anomalies from the current climate
simulation are added to observational datasets for the
current climate [Harrison et al., 1998]. It is thought that
anomalies from the current climate are more robust that the
absolute value for different climates. Monthly averaged
model results for 1880 – 1900, 1940 – 1960 and 1980 –
2000 were taken from the twentieth century runs from the
CMIP3 dataset. Simulated monthly means for the 2040 –
2060 and 2080– 2100 time period were taken from the A1b
scenarios [Intergovernmental Panel on Climate Change,
2001]. Monthly anomalies were derived for each time
period and used to drive the BIOME4 model to equilibrium
for each grid box. Variables taken from the model output
datasets were precipitation, temperature and cloudiness
changes. The models included in this study are shown in
Figure 1. I use the model designation of the desert biome for
this discussion. The desert biome is generated in the
BIOME4 model when the leaf area index is below 1.0 for
woody desert plants, or when the net primary productivity is
below 100.0 for cool conifer trees, grasses or woody desert
plants.
[8] There is some data for the period 1940– 1960 that can
be used to drive the BIOME4 model for that time period, to
compare against model output. Precipitation data from a
merging of two gridded monthly time series from Chen et
al. [2002] and Dai et al. [1997], as described by Dai et al.
[2004] are used. Precipitation data are also difficult to
interpret because of the hetereogeneities in the spatial
distribution of precipitation. However, this gridded dataset
represents our best state of knowledge about precipitation at
the global scale. Temperatures from the CRU dataset [Jones
et al., 2003] are used for temperature changes, and the
cloudiness is assumed not to change in the BIOME4
simulations using observations.
[9] In order to better understand where these changes in
the dust source regions are likely to impact ocean biogeochemistry, I include a source apportionment study for the
dust module [Mahowald et al., 2006a] in the National
Center for Atmospheric Research (NCAR) Community
Climate System Model (CCSM3). This 3-dimensional model simulates the source, transport and deposition of dust
using 4 size bins, and has been compared to observations
[Mahowald et al., 2006a]. For each source region, a
separate tracer simulation is conducted for just that source
region for one year, so that I can estimate where different
sources dominate the deposition. The climate response of
Figure 2. Regional changes in desert area from the mean of the models for the case of (a) no carbon dioxide fertilization
and (b) with carbon dioxide fertilization.
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Table 1. Percent Change in Desert Area Size Using the Mean of All the Models and Available Observations for Precipitation and
Temperaturea
With No CO2 Fertilization
1880 – 1900
All
N.Afr.
Mid.East
China/Mong.
N.Am.
S.Am.
S.Af
Aus.
4.3
2.3
8.0
4.2
15.4
21.1
5.3
13.1
1940 – 1960
Obs
1980 – 2000
2040 – 2060
2080 – 2100
0
0
0
0
0
0
0
0
3.2
4.4
3.0
8.1
16.6
9.5
28.1
1.2
7.9
5.1
1.1
10.2
42.3
17.2
47.0
25.1
1.6
4.0
3.5
3.9
13.7
17.2
1.8
12.3
With CO2 Fertilization
1940 – 1960
0.8
0.2
0.7
1.0
1.0
2.2
0.4
3.2
Obs
1880 – 1900
1940 – 1960
1980 – 2000
2040 – 2060
2080 – 2100
1940 – 1960
2.4
5.5
3.3
12.7
2.6
3.4
3.0
7.1
1.8
5.7
0.9
7.6
6.7
8.5
5.1
1.9
0
0
0
0
0
0
0
0
4.0
1.8
8.4
4.5
8.0
13.2
21.1
19.9
1.6
1.9
8.6
2.5
5.2
10.9
35.9
2.0
1.8
1.1
1.9
1.6
8.4
6.6
5.8
4.2
All
N.Afr.
Mid.East
China/Mong.
N.Am.
S.Am.
S.Af
Aus.
a
Last Column for 1940.
this model to various dust forcings is discussed by Yoshioka
et al. [2007] and Mahowald et al. [2006b].
3. Results and Conclusions
[10] In the global mean, the models predict drying of the
desert regions due to warmer temperatures with an increase
in greenhouse gases, as seen in the mean observations
during the past 25 years [e.g., Dai et al., 2004]. With no
carbon dioxide fertilization in the BIOME4 model, this
means an increase in desert regions globally (Figure 1a),
with a wide variation in the individual model predictions. If
we take the median across the models instead of the mean,
we obtain similar results (not shown). If carbon dioxide
fertilization is included in the simulations, this produces a
decrease in desert area, as the higher carbon dioxide levels
allow the plants to respond to the increased aridity more
effectively (Figure 1b). There is however, a wide spread in
the predictions of different models, with the NCAR CCSM
showing the greatest decrease in desert area in the future,
Figure 3. Simulation of the fractional change in desert in each grid box (2.8 2.8 degree) for the mean of the models for
the case of (a) no CO2 fertilization and (b) CO2 fertilization. The number of models predicting an increase in desert area at
each grid point (c) with no fertilization and (b) with fertilization.
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Figure 4. Current climate source apportionment. (a) Annual deposition fluxes from model, with boxes showing the source
areas. (b) Different colors show regions dominated by the different source areas. In all cases, the source areas dominate the
deposition in their own source areas. Asia is in purple, North America is in white, North Africa is in blue, Australia is in
yellow, and South America is in green. Red is for both South Africa and Middle Eastern sources.
and the GFDL and CSIRO models showing the biggest
increase in desert in the future. The response of the CCSM3
in this study is similar to that shown by Mahowald et al.,
[2006] using the BIOME3 model, suggesting that differences from the IPCC models is more important than
whether BIOME3 or BIOME4 is used.
[11] The averaged response of the CMIP3 models (average across all models used here) in different regions is
shown in Figures 2a and 2b and Table 1 for the cases of no
CO2 fertilization or with CO2 fertilization, respectively.
With no CO2 fertilization, most desert regions increase in
size in the future, with the exception of the desert extent in
the Middle East, which decreases slightly. In the case of
CO2 fertilization, some of the desert regions are predicted to
increase in size (S. Africa, China/Mongolia, and N. Africa),
while others decrease. Smaller desert areas tend to have the
greatest percent change in area. The relative change in
desert area predicted for the different models shows substantial spread (shown in Figure S1).1
[12] The mean projected change in future desert area
(2080 – 2100) is shown in Figure 3 for the case of no CO2
fertilization or including CO2 fertilization, respectively. In
addition, it is useful to know whether the models agree or
disagree on the change in deserts in any particular region—
perhaps the response in that region is more robust. There is
not strong consensus in the models about whether different
desert regions will increase or decrease in size (Figure 3).
[13] For the period 1940– 1960 relative to the present,
there is enough temperature and precipitation data in arid
regions to use gridded data to assess changes in desert areas
using the BIOME4. The results are shown in Table 1, the
overall patterns are similar to the mean of the models, in that
1940 – 1960s have slightly larger deserts than today, but the
relative change in each region is not consistent between the
CMIP3 model-derived and observational-derived changes
in desert areas. This is especially true for North Africa,
much of which change is driven by the Sahel drought, which
may or may not be captured by the models accurately.
[14] One of the important implications of changes in
desert size is its impact on the downwind dust deposition.
A source apportionment study from a model simulation
shows generally which source areas will predominately
impact which ocean deposition regions. Plotted are the
annual averaged deposition fluxes (Figure 4a) and the
source region with the largest deposition flux (Figure 4b)
from the model simulations given by Mahowald et al.
[2006a]. This highlights the importance of understanding
what climate change and humans will do to the small
Southern Hemisphere sources in order to better predict
deposition fluxes, and thus iron deposition changes to these
important ocean regions. Looking at the mean changes in
the desert size, and the downwind regions these impact,
shows that the mean model prediction is that deposition to
the Arabian Sea will decrease whether or not carbon dioxide
fertilization is included. The average of the models predicts
an increase in deposition in 2100 relative to today downwind of South Africa, Australia, Asia and North Africa
whether or not carbon dioxide fertilization occurs. The
prediction is for a small change for North Africa, but that
due to the strength of the North African plume, this change
is important for broad regions of the globe. For other
regions, the mean of the model changes between 2100
and today changes sign if carbon dioxide fertilization is
included. This includes the South American source, which
dominates dust deposition to the equatorial south Pacific
and broad regions of the Southern Oceans, regions which
may be especially sensitive to changes in dust [Martin,
1990]. However, realize that there is substantial variation
between models in the predictions, and the mean of the
models does not match the observationally-based predictions for 1940– 1960.
[15] For the impact of changes in desert dust area on
aerosol optical depth and thus on climate, there is no
information for each source area. But Mahowald et al.
[2006b] show that there is a linear response in radiative
forcing or temperature to optical depth. Making a very
simple assumption that changes in source area will lead to
proportional changes in aerosol optical depth (which is
largely supported by Mahowald and Luo [2003] and
Mahowald et al., [2006]), this suggests that the globally
averaged aerosol optical depth could be 1.6% lower or 7.9%
higher in 2100. These are much more moderate changes
then predicted by Mahowald and Luo [2003] or Mahowald
et al. [2006a], more similar to Tegen et al. [2004] because
1
Auxiliary materials are available in the HTML. doi:10.1029/
2007GL030472.
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the CCSM tends to predict larger changes in deserts than the
rest of the CMIP3 models.
[16] This paper highlights the importance of evaluating
multiple models in assessing future predictions of changes
in desert dust. Predicting precipitation in a general circulation is very difficult, and the models do not agree what will
happen in the future. This leads to a wide disagreement
from the models on which source areas will increase or
decrease in the future. Unfortunately it is not known which
model (if any) is correct—comparisons with desert changes
between 1940 – 1960s and present, where there is data,
suggests very different results than the mean (or median)
of the models. Overall, changes predicted using the mean of
all the models is more moderate than using one model
prediction [cf. Mahowald et al., 2006a]. In addition, the
uncertainty in the magnitude of the carbon dioxide fertilization mechanism can change the sign in the predictions.
Potential interactions between humans land use and desert
dust source strength are neglected in this study, although
they are likely to be important. In addition, desert area
changes and especially desert dust itself may significantly
change climate [Yoshioka et al., 2007], and these impacts
should be considered in future studies.
[17] Acknowledgments. This work was done at the National Center
for Atmospheric Research, sponsored by NSF. I acknowledge the modeling
groups for making their simulations available for analysis, the Program for
Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and
archiving the CMIP3 model output, and the WCRP’s Working Group on
Coupled Modelling (WGCM) for organizing the model data analysis
activity. The WCRP CMIP3 multi-model dataset is supported by the Office
of Science, U.S. Department of Energy. The manuscript was improved by
the comments of two anonymous reviewers.
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N. M. Mahowald, Department of Earth and Atmospheric Sciences,
Cornell University, Ithaca, NY 14853, USA. ([email protected])
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