INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol.
30 : 1970–1979 (2010)
Published online 20 November 2009 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.2048
The Federal University of Vi¸cosa (UFV), Vi¸cosa, MG, Brazil
ABSTRACT: Climate change predictions tied to Amazon deforestation scenarios are increasingly being used by government and non-government organisations for near-future planning applications. Despite incorporating a wide range of biophysical variables, these models neglect future scenarios of land use for adjoining regions, such as the Central Brazil
Cerrado, which has been deforested by more than 50%. In this study, we investigate the impact of different Amazon and
Central Brazil deforestation scenarios on the rainfall regime of the ‘arc-of-deforestation’ in Amazonia. We demonstrate that both Amazon and Cerrado deforestation contribute to an increase of the duration of the dry season in this region.
Combining the effects of both scenarios, the dry season may increase from 5 months to 6 months, which may change the biosphere– atmosphere equilibrium in this region. This study demonstrates that the assessment of future Cerrado land use scenarios is also necessary to understand the future climate and ecosystem health of Amazonia. Copyright  2009 Royal
Meteorological Society
KEY WORDS
Amazon deforestation; Cerrado deforestation; climate change; arc of deforestation
Received 2 October 2008; Revised 24 September 2009; Accepted 14 October 2009
1.
Introduction
A number of studies have demonstrated that the Amazon climate is tightly coupled to the characteristics of its land surface. The investigation of the mechanisms of this coupling has become a classic subject of study that, even after many decades, still demands answers for many questions (Bonan, 2002).
Most previous studies have evaluated the effects of either full-scale deforestation or of specific patterns of deforestation, the role of parameterisations, and the role of sea surface temperature (SST) patterns (see reviews by Costa and Yanagi, 2006; d’Almeida et al .,
2007). Recently, recognising that soybean croplands are increasing rapidly in the Amazon, Costa et al . (2007) and Sampaio et al . (2007) compared climate change in
Amazonia caused by soybean cropland expansion to that caused by pasture expansion. They concluded that the precipitation change after deforestation is dependent on the type of land cover that replaces the forest, and that an extensive soybean cropland would cause a greater reduction in precipitation than pastureland of equivalent size.
Since the publication of Amazon deforestation scenarios (Laurance et al ., 2001; Soares-Filho et al ., 2006), a major trend in the literature (e.g. Moore et al ., 2007;
Sampaio et al ., 2007; daSilva et al ., 2008) has been to study the effects of specific scenarios on the climate
* Correspondence to: Marcos Heil Costa, Department of Agricultural and Environmental Engineering, Federal University of Vi¸cosa, Av. P.
H. Rolfs, s/n, Vi¸cosa, MG, Brazil. E-mail: mhcosta@ufv.br
of specific regions. Because of the geographic specificity and potential realism of the scenarios, their results are increasingly being used by government and nongovernment organisations for near-future planning applications. However, these studies only provide scenarios for the Amazon deforestation, and thus neglect future scenarios of deforestation for adjoining regions which may also significantly influence the future climate of Amazonia. One such region is the nearby Central Brazilian
Cerrado, which has been deforested by more than 50%
(Sano et al ., 2008).
It should be noted that most studies have either investigated annual mean changes in precipitation, or contrasted wet versus dry seasons, or concentrated on a specific month of the year. Nevertheless, one of the possible grave climatic consequences of Amazon deforestation is not a change in precipitation intensity during the dry season, but an increase in the duration of the dry season. This is particularly important in southern and eastern Amazonia which already has a relatively long dry season and where any significant increase in its duration may contribute to a change in the vegetation that is currently in equilibrium with climate (Furley et al .,
1992). Southern and eastern Amazonia is collectively known as the ‘arc-of-deforestation’ due to its status as the contemporary epicenter of Amazon deforestation.
In this study, we investigate the role of different Amazon and Central Brazilian deforestation scenarios on the rainfall regime in Amazonia. We test the hypothesis that Cerrado deforestation significantly influences the climate of parts of Amazonia. To achieve this, we combine
Copyright

2009 Royal Meteorological Society

AMAZON DEFORESTATION SCENARIOS AND THE DURATION OF THE DRY SEASON 1971
‘business-as-usual’ (BAU) and governance (GOV) scenarios for the Amazon rainforest (Soares-Filho et al .,
2006) with a Cerrado deforestation scenario. Although we also investigate the rainfall regime in the entire Amazon region, the focus of our analysis is on the duration of the dry season in the arc-of-deforestation region.
2.
Model description
In this study, we use the National Center for Atmospheric
Research (NCAR) Community Climate Model version
3 (CCM3, Kiehl et al ., 1998) coupled with an updated version of the Integrated Biosphere Simulator (IBIS) of
Foley et al . (1996). This is essentially the same model that was used by Costa et al . (2007) and Coe et al .
(2009). We refer to this coupled model as CCM3–IBIS
(Delire et al ., 2002). CCM3 is an atmospheric general circulation model with spectral representation of the horizontal fields. In this study, to allow for the longer runs and several required simulations, we choose to operate the model at a resolution of T42L18 (the spectral representation of the horizontal fields is truncated at the 42nd wavenumber using a triangular truncation; horizontal fields are converted to a 2 .
81 ° ×
2 .
81 ° grid;
18 levels in the vertical), with a 20-min time step. This resolution allows for a reasonable representation of the major climate features of the region, and most large and synoptic scale processes, although it is not sufficient to represent sub-synoptic or mesoscale phenomena.
The global terrestrial biosphere model IBIS (version
2.6) is a comprehensive model of terrestrial biospheric processes, representing two vegetation layers (i.e. trees and short vegetation) and simulates land surface physics, canopy physiology, and plant phenology. Although IBIS also includes a dynamic vegetation component, in this study it is not used, and vegetation cover is fixed through the duration of the experiment. Land surface physics and canopy physiology are calculated with the time step used by the atmospheric model. The plant phenology algorithm has a daily time step. In these simulations, IBIS operates on the same T42 spatial grid as the CCM3 atmospheric model. We have updated the rainforest representation in
IBIS with a new calibration against flux data from four different Amazonia flux tower sites (Imbuzeiro, 2005), using data from the Large-Scale Biosphere Experiment in Amazonia (LBA).
protected areas (PAs) will not be created. The BAU scenario assumes that as much as 40% of the forests inside of PAs are subject to deforestation, climbing to 85% outside. At the other extreme, the ‘GOV’ scenario assumes that Brazilian environmental legislation is implemented across the Amazon basin through the refinement and multiplication of current experiments in frontier governance.
These experiments include enforcement of mandatory forest reserves on private properties through a satellitebased licensing system (Fearnside, 2003), agro-ecological zoning of land use, and the expansion of the PA network
(Amazon Region Protected Areas Program). Their final product includes annual maps of simulated future deforestation under user-defined scenarios of highway paving,
PA networks, PA effectiveness, deforestation rates and legal deforestation limits. In this study, we use two BAU tropical rainforest deforestation scenarios for 2030 and
2050, and two GOV scenarios for 2030 and 2050.
These tropical rainforest deforestation scenarios are combined with a Cerrado deforestation scenario. Of the original 2 000 000 km 2 of Cerrado that existed in Brazil before ca. 1940, more than 50% has been converted and fragmented by deforestation and expansion of the agriculture frontier (Klink and Machado, 2005; Sano et al .,
2008), compared to 17% of tropical rainforest deforestation. Considering the high rates of Cerrado deforestation and the correspondingly weak conservation governance, we assume that all Cerrado will be deforested by 2030.
Table I summarises the fraction of land deforested in each scenario.
A climate experiment is designed to elucidate the role of different Amazon and Central Brazil deforestation scenarios on the regional climate. We conduct nine simulations: four simulations for the scenarios BAU2030,
GOV2030, BAU2050 and GOV2050 (Figure 1), four simulations that include the scenarios above plus the
Cerrado deforestation (BAU2030
+
Cerrado, GOV2030
+
Cerrado, BAU2050
+
Cerrado and GOV2050
+
Cerrado), in addition to a control run (Table I). Each simulation has three ensembles, where each ensemble starts from different initial conditions corresponding to the NCEP analyses from 1 to 3 January 1990.
Table I. Fraction of land deforested for each deforestation scenario used.
3.
Deforestation scenarios and experiment design
We use four scenarios of deforestation for the Amazon produced by Soares-Filho et al . (2006) from a total of eight scenarios that encompass a plausible range of future trajectories of deforestation in the Amazon. At one extreme is the ‘BAU’ scenario, which assumes that: (1) recent deforestation trends will continue; (2) highways currently scheduled for paving will be paved; (3) compliance with legislation requiring forest reserves on private land will remain low; and (4) new
Deforestation scenario
Control
BAU 2030
BAU 2050
GOV 2030
GOV 2050
BAU 2030 + Cerrado
BAU 2050 + Cerrado
GOV 2030
+
Cerrado
GOV 2050 + Cerrado
Amazon tropical rainforest (%)
28
33
47
22
28
0
33
47
22
Cerrado
(%)
0
100
100
100
100
0
0
0
0
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)

1972 M.H. COSTA AND G.F. PIRES
Figure 1. Land cover scenarios used in the simulations. (a) Control run; (b) GOV2030; (c) GOV2050; (d) BAU2030; (e) BAU2050. Averages for the tropical rainforest region in Table II, Figure 3a and Figure 4 use the rainforest area as defined in (a). Thin lines indicate country and
Brazilian states borders, while the thick line polygon indicates the arc-of-deforestation.
In all cases, deforestation is defined as the replacement of the natural vegetation (either tropical rainforest or Cerrado) by pasturelands, which are parameterised according to Costa et al . (2007).
In all simulations atmospheric CO
2 concentrations are set to 380 ppmv and sea surface temperatures are set to a climatological seasonal cycle. All simulations are run for 20 years; the first 10 years are left for the model to approach an equilibrium state, specifically with respect to soil moisture, while the last 10 years of the three ensembles are averaged for the purpose of analysis. Finally, a Student’s t -test is used to analyse the significance of the results ( α
=
0 .
05). During the t -test, the ensemble members were used as additional degrees of freedom (for monthly analysis, n
=
30).
Table II. Annual mean precipitation for the Amazon tropical forest region, from different sources.
Precipitation dataset Precipitation (mm/day)
Results for the Amazon tropical rainforest region
CMAP 5.33
CRU
TRMM
Legates and Wilmott
Leemans and Cramer
Dataset average
CCM3-IBIS control run
6.07
6.43
6.33
6.10
6.05
6.51
Results for the arc-of-deforestation region
CMAP
CRU
TRMM
Legates and Wilmott
Leemans and Cramer
Dataset average
CCM3-IBIS control run
4.87
4.91
5.92
5.22
5.56
5.30
5.47
e (%)
+
12.5
+ 11.5
−
7.6
+ 4.8
−
1.6
+ 3.2
–
+ 22.2
+
7.4
+ 1.2
+
2.8
+ 6.7
+
7.6
–
4.
Results and discussion
4.1.
Validation of the control run
Precipitation estimates in the Amazon are inherently uncertain due to the low density of rain gauges in the region and the convective nature of the rainfall (Costa and Foley, 1998). This necessitates the inclusion of several precipitation estimates for effective model validation. Table II illustrates the annual mean precipitation for the Amazon tropical rainforest and for the arc-ofdeforestation regions simulated and calculated from five precipitation databases: CMAP (Xie and Arkin, 1997),
CRU (New et al ., 1999), TRMM (Tropical Rainfall Measuring Mission – Kummerow et al ., 1998), Leemans and
Cramer (1990), and Legates and Willmott (1990). For the rainforest region [as defined in Figure 1(a)], annual mean precipitation estimates vary from 5.33 to 6.43 mm/day
(dataset average 6.05 mm/day). The CCM3-IBIS annual mean climate (6.51 mm/day) overestimates the average
The mean percentual relative error [ e (%)] is calculated using each dataset as reference (Control/dataset–1).
of these datasets by 7.6% (ranging from 1% to 22%).
For the arc-of-deforestation – the main focus of this study
[also defined in Figure 1(a)] – annual mean precipitation estimates vary from 4.87 to 5.92 mm/day (dataset average 5.30 mm/day). CCM3–IBIS annual mean climate is
5.47 mm/day, an overestimation of about 3% (ranging from
−
1 .
6% to
+
12 .
5%).
Figure 2 illustrates the spatial pattern of annual mean precipitation for northern South America simulated by the control run and estimated by the same precipitation datasets used above. Figures 2(b)–(f) illustrate
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)

AMAZON DEFORESTATION SCENARIOS AND THE DURATION OF THE DRY SEASON 1973
Figure 2. Annual mean precipitation climatology for northern South America (mm/day) (a) simulated by CCM3-IBIS in the control run, and estimated by the following datasets: (b) CRU, (c) CMAP, (d) TRMM, (e) Leemans and Cramer and (f) Legates and Wilmott. Differences between the Control run and each dataset are also shown.
areas where CCM3–IBIS overestimates (positive values) or underestimates (negative values) precipitation according to CRU [Figure 2(b)], CMAP [Figure 2(c)],
TRMM [Figure 2(d)], Leemans and Cramer
[Figure 2(e)], and Legates and Willmott [Figure 2(f)].
In northwestern Amazonia CCM3-IBIS overestimates precipitation according to the rain-gauge-based datasets
CRU, CMAP, Legates and Willmott, and Leemans and
Cramer, but not according to TRMM. Due to the convective nature of precipitation in this region and the very low density of gauges it is possible that rain-gauge-based products do not effectively capture spatial patterns of precipitation, while the TRMM product should overcome this limitation. CCM3–IBIS underestimates precipitation over the Guyanas, Amap´a and the Maraj´o Island in Brazil, and over the Peru–Ecuador border, according to these datasets. In the arc-of-deforestation, spatial patterns of annual mean precipitation are accurately represented.
Simulated amplitude of the Amazon tropical rainforest precipitation is within the amplitude of the datasets
[Figure 3(a)] and seasonality is also accurately simulated, although it is advanced in time by 1 month. Most of the overestimation of the simulated rainfall occurs at the beginning of rainy season, from September to December.
Copyright

2009 Royal Meteorological Society
(a) 10
8
6
4
2
0
(b)
6
4
2
0
12
10
8
Rainforest area
J F M A M J J A S O N D
Arc-of-deforestation
J F M A M J J A S O N D
Control simulation
CRU
Legates and Wilmott
CMAP
TRMM
Leemans and Cramer
Figure 3. Model validation against five datasets for (a) the rainforest region and (b) the arc-of-deforestation. The horizontal line indicates the average evapotranspiration of the rainforest (3.5 mm/day) and is intended to mark the months with water deficit.
Int. J. Climatol.
30 : 1970–1979 (2010)

1974 M.H. COSTA AND G.F. PIRES
For the arc-of-deforestation region [Figure 3(b)], precipitation simulated by CCM3–IBIS overestimates the rainfall during 2 months in the rainy season, but is accurately simulated during the dry season and in the transition months. The duration of the dry season, defined as the period with rainfall smaller than 3.5 mm/day, is also well simulated for the arc-of-deforestation.
4.2.
Effects of deforestation
An analysis of the climate effects of the scenarios
GOV2030, GOV2050, BAU2030 and BAU2050 indicates that these scenarios of deforestation significantly reduces Amazon rainforest region precipitation in 42 of the 48 months analysed
−
9 out of 12 months in
GOV2030, 11 out of 12 months in GOV2050, 11 out of 12 months in BAU2030, and 11 out of 12 months in BAU2050 (open boxes on thin line, Figure 4). As anticipated, precipitation decreases more in the BAU scenarios than in the GOV scenarios, and more in 2050 than in 2030. However, the Cerrado deforestation does not cause additional significant decreases in rainfall for the whole tropical rainforest area, except in 7 of the
48 months analysed
−
1 / 12 in GOV2030
+
Cerrado, 2/12 in GOV2050
+
Cerrado, 3/12 in BAU2030
+
Cerrado, and 2/12 in BAU2050
+
Cerrado (open boxes on dashed line, Figure 4). The differences between statistically significant results (at the 95% significance level) seem to be small, but this is probably due to the relatively small standard deviation of the runs and the high number of degrees of freedom ( n
= 30), which substantially narrow the confidence interval.
The climate change predictions are remarkably different when we analyse the results for the arc-ofdeforestation region (Figure 5). In this region, deforestation in the GOV2030, GOV2050, BAU2030 and
BAU2050 scenarios significantly reduces precipitation in 33 of the 48 months analysed
−
8 out of 12 months in GOV2030, 9 out of 12 months in GOV2050, 8 out of 12 months in BAU2030, and 8 out of 12 months in
BAU2050 (open boxes on thin line, Figure 5). The main difference when we analyse the results over the arc-ofdeforestation as compared to the entire Amazon forest region is that precipitation decreases are not relevant during the dry season, when there is little precipitation.
This finding explains the significant reduction in precipitation in only 33 months in Figure 5, compared to
42 months in Figure 4. However, there are significant reductions during the transition months and the rainy season months, indicating that both GOV and BAU scenarios tend to increase the duration of the dry season in the arcof-deforestation region. Moreover, Cerrado deforestation further decreased precipitation in 15 of the 48 months, twice as much as in the rainforest region. More important than the number of months with significant reduction, is that most of the periods with significant reduction in rainfall happen during the transition months between the dry and the rainy season (April, September, October and
November). This causes an extension of the dry season by about 1 month.
The reduction of rainfall after deforestation is a consequence of changes in the moisture and energy budgets of the region, which affects the sources of
(c)
12
10
8
6
4
2
0
(a)
12
10
8
6
4
2
0
GOV2030 and GOV2030+Cerrado
J F M A M J J A S O N D
BAU2030 and BAU2030+Cerrado
J F M A M J J A S O N D
Control GOV or BAU
(d)
12
10
8
6
4
2
0
(b)
12
10
8
6
4
2
0
GOV2050 and GOV2050+Cerrado
J F M A M J J A S O N D
BAU2050 and BAU2050+Cerrado
J F M A M J J A S O N D
GOV or BAU+Cerrado
Figure 4. Results for the control (thin solid line), BAU/GOV (thick solid line), and BAU/GOV
+
Cerrado (thin dashed line) experiments, averaged over the tropical rainforest region. White squares on the control or cerrado curve indicate that value is significantly different from the
GOV/BAU scenario, at the 95% level of significance.
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)

AMAZON DEFORESTATION SCENARIOS AND THE DURATION OF THE DRY SEASON 1975
(c)
12
10
8
6
4
2
0
(a)
12
10
8
6
4
2
0
GOV2030 and GOV2030+Cerrado
J F M A M J J A S O N D
BAU2030 and BAU2030+Cerrado
J F M A M J J A S O N D
Control GOV or BAU
8
6
4
(d)
12
10
2
0
(b)
12
10
8
6
4
2
0
GOV2050 and GOV2050+Cerrado
J F M A M J J A S O N D
BAU2050 and BAU2050+Cerrado
6 months
5 months
J F M A M J J A S O N D
GOV or BAU+Cerrado
Figure 5. Results for the control (thin solid line), BAU/GOV (thick solid line), and BAU/GOV
+
Cerrado (thin dashed line) experiments, averaged over the arc-of-deforestation region. White squares on the control or cerrado curve indicate that value is significantly different from the
GOV/BAU scenario, at the 95% level of significance. The horizontal lines indicate the average evapotranspiration of the rainforest (3.5 mm/day) and are intended to mark the months with water deficit.
moisture and the convective systems. Figure 6 illustrates the regional patterns of moisture components
(precipitation, moisture convergence, and evapotranspiration) and energy budget components (evapotranspiration/latent heat flux, net radiation and Bowen ratio) for the control simulation [Figure 6(a), (e), (i), (m) and
(q)] and the effects of the BAU2050 [Figures 6(b), (f),
(j), (n) and (r)] and BAU2050 + Cerrado deforestation scenarios [Figures 6(c), (g), (k), (o) and (s)]. The effects of the Cerrado deforestation alone are inferred from the difference between the results of BAU2050
+
Cerrado and BAU2050 [Figures 6(d), (h), (l), (p) and
(t)]. We focus this analysis on the quarter September–October–November, the beginning of the rainy season, where the effects are most significant for the extension of the dry season.
The major rainfall reduction occurs over and slightly westward (downstream) of the deforested area [compare
Figure 6(b) and 6(c) with Figure 1(e)]. This pattern is a consequence of the downstream transport by the westward winds of atmospheric properties influenced by the changes in land cover.
During the transition from the dry season to the wet season moisture convergence is small in the arc-ofdeforestation [Figure 6(e)], and most of the moisture provided to the atmosphere is from the local evapotranspiration [Figure 6(i)]. Deforestation does not significantly decrease moisture convergence over the arc-ofdeforestation [Figures 6(f), (g), (h)], but causes important reductions in evapotranspiration [Figures 6(j), (k) and
(l)]. The reduction in evapotranspiration after the deforestation is related to the increased albedo, reduced leaf area, reduced rooting depth and reduced turbulence of the pasture over the rainforest/Cerrado (Costa, 2005). During this time of the year the reduced rooting depth is probably the most significant ecological factor.
Analysis of the atmospheric moisture budget indicates that a drop in evapotranspiration due to changes in land cover is the main cause of reduction of the source of moisture to the atmosphere at the beginning of rainy season. This is a very robust result, as it reflects basic biophysical characteristics of the land cover, in particular decreased rooting depth, and does not depend on the simulated atmospheric circulation.
The amount of net radiation available at the surface, in addition to providing energy for evapotranspiration, also provides energy for convection. According to Eltahir
(1996), the reduction in the net surface radiation after deforestation cools the upper atmosphere over the deforested area, inducing a thermally-driven circulation that results in subsidence. This process is apparently more efficient as the deforested area increases. Figures 6(n),
(o) and (p) illustrate the change in the surface net radiation after the deforestation. This is mainly a local effect due to changes in land surface albedo, although remote effects driven by cloud feedbacks may also be occurring.
Finally, Figures 6(q)–(t) demonstrate that the Bowen ratio increases after the deforestation, increasing sensible heat flux and decreasing latent heat flux.
Figure 7 illustrates the seasonal variability of the components of the moisture budget (precipitation, moisture convergence and evapotranspiration) for the arc-ofdeforestation for the control simulation and the anomaly of the BAU2050 and BAU2050
+
Cerrado. In the control simulation [Figure 7(a)], during the dry season months and period of low convection, the main source
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)

1976
(a)
Control
M.H. COSTA AND G.F. PIRES
(b)
BAU2050 - Control
(c)
BAU2050+Cerrado
- Control
(d)
BAU2050+Cerrado
- BAU2050
(e) (f) (g) (h)
(i) (j) (k) (l)
(m) (n) (o) (p)
(q) (r) (s) (t)
Figure 6. Regional patterns of moisture components (precipitation, moisture convergence, and evapotranspiration, all in mm/day) and energy budget components (evapotranspiration/latent heat flux, net radiation in W/m 2 and Bowen ratio – adimensional) for the quarter
September–October–November. Control simulation results are displayed in figures (a), (e), (i), (m) and (q). The effects of the BAU2050 scenario are shown in figures (b), (f), (j), (n) and (r). The effect of the BAU2050
+
Cerrado deforestation scenario with respect to the control is presented in figures (c), (g), (k), (o) and (s), while the effects of the BAU2050
+
Cerrado deforestation scenario with respect to the BAU2050 scenario are shown in figures (d), (h), (l), (p) and (t).
of atmospheric water vapour is the surface evapotranspiration. At the beginning of the rainy season (September–November) the pattern of moisture divergence gradually shifts towards strong convergence, which remains throughout the rest of the rainy season.
The BAU2050 deforestation scenario predicts reduced precipitation through the year, with weaker changes in the dry season and stronger changes in the beginning and end of the rainy season [Figure 7(b)]. Evapotranspiration also decreases throughout the year, with smaller changes in
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)

AMAZON DEFORESTATION SCENARIOS AND THE DURATION OF THE DRY SEASON
Copyright

2009 Royal Meteorological Society
1977 the rainy season. Moisture convergence decreases during the end of the rainy season (March), shifting towards a slight increase in the dry season (May–September) and then decreasing again to a negative peak in November.
This decrease in moisture convergence in the transition months (March–May and September–November) is an important driver of the precipitation decrease in the transition months, and, hence, the extension of the dry season.
Precipitation is predicted to decrease even more in the
BAU2050
+
Cerrado deforestation, in particular during the beginning of the rainy season [September–November,
Figure 7(c)]. Although there is little change in evapotranspiration in the arc-of-deforestation in this scenario when compared to the BAU2050 scenario [Figure 7(b)], the change in moisture convergence in this period nearly doubles (
−
0 .
54 mm/day in BAU2050
+
Cerrado, compared to
−
0 .
29 mm/day in BAU2050, for October;
−
1 .
68 mm/day in BAU2050
+
Cerrado, compared to
−
1 .
12 mm/day in BAU2050, for November). This is because the deforested Cerrado has lower evapotranspiration than the Cerrado itself during this period of the year. In the BAU2050 scenario, the additional evapotranspiration from the Cerrado is advected to the arcof-deforestation, where part of it converges and precipitates. In the BAU2050
+
Cerrado scenario, there is less advection of moisture to the arc-of-deforestation region, causing lower precipitation during this time of the year.
Overall, in addition to its geographic position upstream of the arc-of-deforestation, we believe the main mechanism linking Cerrado deforestation to decreases in precipitation over the arc-of-deforestation region is reduced evapotranspiration during the beginning of the rainy season [Figure 6(l)]. In this season, when the moisture advection from the ocean is still low, reduced evapotranspiration dries out the atmosphere causing less advection of water vapour to the arc-of-deforestation region, delaying the onset of the rainy season, and extending the length of the dry season.
(a) 12
10
8
6
0
–2
4
2
Control
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
(b) 2
BAU2050 - Control
1
0
–1
–2
–3
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
(c) 2
BAU2050+Cerrado - Control
1
0
–1
–2
–3
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Precipitation
Evapotranspiration
Moisture Convergence
5.
Conclusions
While most previous studies of the effects of Amazon deforestation on local climate have analysed the effects on the Amazon region as a whole or in specific regions, none of them have focussed on the arc-of-deforestation region. This region may be particularly important for at least three reasons: first, it shows the longest dry season in Amazonia; second, it exhibits the largest deforestation rates of the Amazon; and, third, its vegetation is particularly sensitive to changes in dry season duration. Our analysis demonstrates that the climate effects of deforestation vary considerably depending on whether we consider the entire rainforest region or just the arc-ofdeforestation. Specifically, in the entire rainforest region, changes in rainfall are most dependent on tropical forest deforestation, while in the arc-of-deforestation region,
4000
3500
3000
BAU/GOV deforestation scenarios
2500
2000
1500
Rainforest climate region
BAU/GOV+Cerrado deforestation scenarios
Cerrado climate border
Control
1000
500
0
–800 –700 –600 –500 –400 –300 –200 –100 0
Maximum Climatological Water Deficit (mm)
Figure 7. Seasonal variability of the components of the atmospheric moisture budget (precipitation, moisture convergence and evapotranspiration) for the arc-of-deforestation for the control simulation (a) and the anomaly of the four BAU2050 (b) and four BAU2050
+
Cerrado
(c) scenarios.
Figure 8. The relationship between vegetation type and rainfall regime, according to Malhi et al . (2009). The rainforest climate region is drawn from the rainforest cloud of points in Malhi’s Figure 1. The suggested cerrado climate border is maximum climatological water deficit <
−
300 mm and annual precipitation < 1500 mm. The simulated climate for the arc-of-deforestation for the control simulation, and the
BAU/GOV and BAU/GOV
+
Cerrado deforestation scenarios are also shown.
Int. J. Climatol.
30 : 1970–1979 (2010)

1978 M.H. COSTA AND G.F. PIRES reductions in precipitation are dependent on both deforestation of the tropical forest and the adjoining Cerrado region.
While many studies have focussed on the analysis of rainy season rainfall or in contrasting rainy and dry seasons, this study highlights an important and generally overlooked effect of Amazon deforestation – the duration of the dry season. The arc-of-deforestation region, being the transition zone between the rainforest and the Cerrado region in Central Brazil, currently has a 5-month-long dry season. Although comparatively long, the climate of this region still supports a rainforest, in contrast to the 6-month-long dry season of the nearby Cerrado region, reported by CMAP and CRU datasets. Our results here indicate that GOV and BAU tropical rainforest deforestation scenarios tend to decrease precipitation in nearly all months outside of the dry season. While this would not make the dry season drier, it would contribute to make the dry season longer with potentially severe ecological consequences.
Deforestation of the Amazon rainforest is not the only contributing factor leading to an increase in the duration of the dry season in the arc-of-deforestation. Cerrado deforestation also contributes to decrease rainfall in the transition months from the dry to the rainy season
(April, September, October and November). The combined climatic effects of the Amazon tropical rainforest deforestation scenario (regardless of the scenario chosen) and a Central Brazil Cerrado deforestation scenario cause an increase in the duration of the dry season in the arc-ofdeforestation by about 1 month. Although the dry season months are as dry in the BAU
+
Cerrado scenario as they are in the control simulation, the dry season starts earlier and ends later in the year.
Given the likely increase in dry season length, the key question is how will this influence the remaining rainforest in the arc-of-deforestation region? According to Malhi et al . (2009), the precipitation threshold of rainforest to Cerrado is represented by an annual mean rainfall of 1500 mm/year (4.10 mm/day) and a maximum climatological water deficit (MCWD) of at least 300 mm.
Figure 8 illustrates that, although the control run climate is at a significant distance from the rainforest/Cerrado border, some climate scenarios, in particular the ones that include Cerrado deforestation, are more representative of a seasonal forest climate and much closer to the Cerrado climate transition zone. This indicates the potential for major shifts in natural vegetation induced by the ongoing rainforest and Cerrado deforestation.
Other factors may also contribute to extend the duration of the dry season in the arc-of-deforestation region.
For example, when aerosol particles from biomass fires become very dense in the atmosphere they produce an excess of condensation nuclei, and individual water droplets do not grow big enough to precipitate (Andreae et al ., 2004). Although these effects are not included in the present simulations, they may act in conjunction with the deforestation to further extend the dry season.
In conclusion, our analysis demonstrates that the study of rainforest deforestation scenarios, although relevant, does not provide sufficient information for the accurate prediction of future climate in Amazonia. Deforestation patterns in the nearby regions such as the Cerrado may be equally important for understanding and predicting the future climate in different areas of the Amazon rainforest and should be factored in to subsequent studies.
Acknowledgements
This research has been supported by the Gordon and
Betty Moore Foundation (USA) and by CNPq (Brazil).
We thank Drs Michael T. Coe, Britaldo S. Soares-Filho,
Richard Ladle and three anonymous reviewers for their comments on an earlier version of this manuscript.
References
Andreae MO, Rosenfeld D, Artaxo P, Costa AA, Frank GP, Longo
KM, Silva-Dias MAF. 2004. Smoking rain clouds over the Amazon.
Science 303 : 1337–1342, DOI: 10.1126/science.1092779.
Bonan GB. 2002.
Ecological Climatology: Concepts and Applications .
Cambridge University Press: Cambridge.
Coe MT, Costa MH, Soares-Filho BS. 2009. The influence of historical and potential future deforestation on the stream flow of the Amazon
River – land surface processes and atmospheric feedbacks.
Journal of Hydrology 369 : 165–174.
Costa MH. 2005. Large-scale hydrological impacts of tropical forest conversion. In Forests, Water and People in the Humid Tropics ,
Bonell M, Bruijnzeel LA (eds). Cambridge University Press: New
York, pp 590–597.
Costa MH, Foley JA. 1998. A comparison of precipitation datasets for the Amazon basin.
Geophysical Research Letters 25 : 155–158.
Costa MH, Yanagi SNM. 2006. Effects of Amazon deforestation on the regional climate: historical perspective, current and future research.
Brazilian Journal of Meteorology 21 : 200–211.
Costa MH, Yanagi SNM, Oliveira PJ, Ribeiro A, Rocha EJP. 2007.
Climate change in Amazonia caused by soybean cropland expansion, as compared to caused by pastureland expansion.
Geophysical
Research Letters 34 : L07706, DOI: 10.1029/2007GL029271.
da Silva RR, Werth D, Avissar R. 2008. Regional impacts of future land-cover changes on the Amazon Basin wet-season climate.
Journal of Climate 21 : 1153–1170.
D’Almeida C, V¨or¨osmarty CJ, Hurtt G, Marengo JA, Dingman SL,
Keim B. 2007. The effects of deforestation on the hydrological cycle in Amazonia: a review on scale and resolution.
International Journal of Climatology 27 : 633–647.
Delire C, Foley JA, Thompson S. 2002. Evaluating the carbon cycle of a coupled atmosphere-biosphere model.
Global Biogeochemical
Cycles 17 : 1012, DOI:10. 1029/2002 GB001870.
Eltahir EAB. 1996. Role of vegetation in sustaining large-scale atmospheric circulations in the tropics.
Journal of Geophysical
Research 101 : 4255–4268.
Fearnside P. 2003. Deforestation control in Mato Grosso: a new model for slowing the loss of Brazil’s Amazon forest.
Ambio 32 : 343–345.
Foley JA, Prentice IC, Ramankutty N, Levis S, Pollard D, Sitch D,
Haxeltine A. 1996. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics.
Global Biogeochemical Cycles 10 : 603–628.
Furley PA, Ratter JA, Proctor J. 1992.
Nature and Dynamics of Forest-
Savanna Boundaries . Chapman and Hall: New York.
Imbuzeiro HMA. 2005.
Calibration of the IBIS model in the Amazonian forest using multiple sites . Unpublished MS Thesis (in Portuguese with abstract in English), Federal University of Vi¸cosa, Vi¸cosa, 67pp.
Kiehl JT, Hack JJ, Bonan GB, Boville BA, Williamson DL, Rasch PJ.
1998. The National Center for Atmospheric Research Community
Climate Model: CCM3.
Journal of Climate 11 : 1131–1149.
Klink CA, Machado R. 2005. Conservation of the Brazilian Cerrado.
Conservation Biology 19 : 707–713, DOI: 10.1111/j.1523–1739.
2005.00702.x.
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)

AMAZON DEFORESTATION SCENARIOS AND THE DURATION OF THE DRY SEASON 1979
Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J. 1998. The tropical rainfall measuring mission (TRMM) sensor package.
Journal of Atmospheric and Oceanic Technology 15 : 809–817.
Laurance WF, Cochrane MA, Bergen S, Fearnside PM, Delamˆonica P,
Barber C, D’Angelo S, Fernandes T. 2001. The future of the
Brazilian Amazon.
Science 291 : 438–439.
Leemans R, Cramer WP. 1990. The IIASA database for mean monthly values of temperature, precipitation and cloudiness on a global terrestrial grid, IIASA Working Papers, WP-90-41, Laxenburg,
Austria.
Legates DR, Willmott CJ. 1990. Mean seasonal and spatial variability in gauge-corrected, global precipitation.
International Journal of
Climatology 10 : 111–127.
Malhi Y, Arag˜ao LEOC, Galbraith D, Huntingford C, Fisher R,
Zelazowski P, Sitch S, McSweeney C, Meir P. 2009. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest.
Proceedings of the National Academy of
Sciences , DOI:10.1073/pnas.0804619106.
Moore N, Arima E, Walker R, da Silva RR. 2007. Uncertainty and the changing hydroclimatology of the Amazon.
Geophysical Research
Letters 34 : L14707, DOI: 10.1029/2007GL030157.
New M, Hulme M, Jones P. 1999. Representing twentieth-century space-time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology.
Journal of Climate 12 :
829–856.
Sampaio G, Nobre C, Costa MH, Satyamurty P, Soares-Filho BS,
Cardoso M. 2007. Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion.
Geophysical
Research Letters 34 : L17709, DOI: 10.1029/2007GL030612.
Sano EE, Rosa R, Brito JL, Ferreira Jr LG. 2008. Mapeamento semidetalhado do uso da terra do Bioma Cerrado.
Pesquisa
Agropecuaria Brasileira 43 : 153–156, DOI: 10.1590/S0100-
204X2008000100020.
Soares-Filho B, Nepstad DC, Curran L, Cerqueira G, Garcia R,
Ramos C, Voll E, McDonald A, Lefebvre P, Schlesinger P. 2006.
Modeling Amazon conservation.
Nature 440 : 520–523, DOI:10.1038
/nature04389.
Xie P, Arkin PA. 1997. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs.
Bulletin of the American Meteorological
Society 78 : 2539–2558.
Copyright

2009 Royal Meteorological Society Int. J. Climatol.
30 : 1970–1979 (2010)