GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L19802, doi:10.1029/2011GL049099, 2011
How does local tropical deforestation affect rainfall?
L. Garcia‐Carreras1 and D. J. Parker1
Received 29 July 2011; revised 8 September 2011; accepted 8 September 2011; published 7 October 2011.
[1] The aim of this study is to investigate the potential
impacts of vegetation‐breezes on locally‐generated rainfall
and its distribution on the mesoscale. Ensembles of
simulations with a 2D large‐eddy model were performed
using various heterogeneous land surfaces. Rainfall was
found to be 4–6 times higher over warmer surface
anomalies, associated with cropland, compared to a
homogeneous surface, but rainfall was reduced to half or
less over the forest. While the suppression of rainfall tended
to occur throughout the forest with an intensity comparable
to the surface anomaly, the exact location of the maximum
in rainfall was less predictable. The location of peak rainfall
depended on an interplay between the size of the heat flux
gradient (governing the vegetation‐breeze strength), the
size of the anomaly (as vegetation‐breezes organize in
certain preferential length‐scales), and the distance to other
anomalies (since convection in one location could suppress
it elsewhere). The presence of surface heterogeneity also
increased the total rainfall in the domain by 13% on
average, with higher increases in the presence of more
intense surface variabilities. Citation: Garcia‐Carreras, L.,
and D. J. Parker (2011), How does local tropical deforestation affect
rainfall?, Geophys. Res. Lett., 38, L19802, doi:10.1029/
2011GL049099.
1. Introduction
[2] Land‐use change, such as conversion of part of a
forest to pasture, can lead to sharp gradients in surface
fluxes, which in turn can generate thermally‐induced circulations analogous to sea‐breezes. Convergence of vegetation‐breezes can lead to enhanced shallow convection over
the warm temperature anomalies [Wang et al., 2000; Roy
and Avissar, 2002; Kawase et al., 2008; Roy, 2009]. This
enhancement of convection is due both to an enhancement
of vertical motion at the convergence zones, aiding convective initiation, as well as an increase in the convective
available potential energy (CAPE) at the convergence zones
increasing the depth and organization of clouds [Garcia‐
Carreras et al., 2011] (hereafter GC11).
[3] Vegetation‐breezes have been directly observed by
aircraft measurements over Benin in West Africa, with convection occurring at the convergence zones over the cropland
boundaries [Garcia‐Carreras et al., 2010]. Satellite studies
have also extensively corroborated the link between land
cover and convection over deforested regions [Garcia‐
1
Institute for Climate and Atmospheric Science, University of
Leeds, Leeds, UK.
Copyright 2011 by the American Geophysical Union.
0094‐8276/11/2011GL049099
Carreras et al., 2010; Wang et al., 2000; Roy and Avissar,
2002; Wang et al., 2009], consistent with initiation of convection caused by land‐surface induced flows.
[4] There has been relatively little work looking at the
subsequent impact on rainfall amounts and distributions.
Mesoscale modelling of the impact of fishbone deforestation
in Amazonia has found that resolving mesoscale processes
is important to correctly characterize the rainfall [Ramos da
Silva and Avissar, 2006] and that mesoscale circulations
lead to increased cloud cover and rainfall over the deforested patches [Roy, 2009]. This could attenuate the decrease
in rainfall caused by deforestation predicted by coarser
resolution models [Ramos da Silva et al., 2008]. Satellite
rainfall measurements also show enhanced rainfall over the
deforested side of the land‐surface boundaries and reduced
rainfall either side [Negri et al., 2004; Knox et al., 2011].
These results are consistent with the presence of peak CAPE
at the convergence zone with subsidence either side, as
found by GC11.
[5] The impact of deforestation on rainfall distributions is
crucial to understand subsequent feedbacks back to the
surface, and thus the long‐term impact of deforestation. For
example, enhanced rainfall over deforested regions may
enhance the regeneration of the forest, thus acting as a
negative feedback by reducing the initial perturbation to the
surface. On the other hand, West African rainforests,
together with those of the Congo, have lower mean precipitation rates than any other rainforest in the world [Malhi
and Wright, 2004]. These regions may therefore, potentially,
be more sensitive to reductions in precipitation, leading to
vegetation degradation beyond the deforested region itself.
These possibilities highlight a non‐linearity of the feedback of deforestation on the water cycle which has not
been investigated in depth. Given that deforestation rates
in West Africa are particularly high [Food and Agriculture
Organization, 2009], understanding these feedback mechanisms is of particular importance for the region.
[6] The aim of this study is to determine the impact of
different vegetation heterogeneities on locally‐generated
rainfall distributions, in particular differences in rainfall
over cropland and forest. Various realistic distributions of
surface patterns are used to look at the impact of the size and
locations of the surface anomalies on the resulting mean
rainfall distributions.
2. Methodology
[7] Version 2.4 of the Met Office Large Eddy Model
(LEM) [Gray et al., 2001], a nonhydrostatic model with a
Boussinesq equation set, was run in 2D for the simulations,
using a similar setup to GC11. The main difference in this
study has been the inclusion of rainfall with a full 3‐phase
microphysics parameterisation. A 312.5 km horizontal and
13 km vertical domain was used, with a horizontal resolution
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of 250 m and a varying vertical resolution (14–240 m over
107 levels). The model was run from 06:00–24:00 LT.
[8] The use of a 2D model was imposed by computational
constraints, particularly given the large number of runs that
were performed. The impact of using 2D as opposed to 3D
is partly discussed in GC11. The use of 2D may emphasize
the presence of mesoscale circulations, as these are artificially confined in one dimension. Clouds in a 2D model
experience reduced entrainment of environmental air, but
also weaker updraughts due to a moister lower troposphere,
which is a result of lower detrainment from shallow clouds.
Overall cloud cover is generally similar for 2D and 3D
simulations [Petch et al., 2008]. The focus of this study is on
the distribution of rainfall and the impact of boundary layer
dynamics on this distribution, as opposed to quantifying total
rainfall amounts absolutely. Furthermore, a comparison of
2D and 3D in GC11 showed a similar cloud cover pattern,
although details differed, suggesting that the use of a 2D
model is justified.
[9] The same initial and prescribed surface boundary
conditions as in GC11 were used, which in turn followed an
observational test‐case over Benin [Garcia‐Carreras et al.,
2010]. The initial conditions were taken from ECMWF
analysis data on 16 August 2006, and the diurnal cycle of
surface sensible and latent heat fluxes was estimated from
ground station data in Nangantchori, Benin. A run with a
homogeneous surface was used as a control (CTL). The
surface heterogeneity was imposed by varying the Bowen
ratio (lower over forest, higher over crop) but keeping the
net radiation constant. The domain mean sensible and latent
heat fluxes were the same as in the control run.
[10] One ensemble was run with the same surface flux
heterogeneity as in GC11, derived from remote‐sensing data
(HET). In addition to this, another 4 random land surfaces
(RN1–RN4) were generated by creating red‐noise distributions with variability at similar length‐scales to HET. This
was done by using an autoregressive function, i.e. a distribution where each value corresponds to a weighted sum of
its previous values with a white noise error, tuned to give
variability of the desired wavelengths. The range of the
distribution was then set to be the same as HET. The actual
land‐surfaces are shown in the top plots of Figure 1. The
largest heat‐flux gradient among all the simulated land
surfaces is found in HET, as the full range is covered by a
single boundary (50–60 km). To explore the impact of the
magnitude of the gradient at land‐surface boundaries, a final
ensemble (RN5) was run with the same surface distribution
as RN4 but doubling the Bowen ratio offset range. RN4 was
chosen as it had the smallest land‐surface gradients.
[11] For each land surface, an ensemble of 20 runs was
performed. The only difference between the ensemble runs
was a random perturbation to the initial conditions of 0.2 K
in temperature and 0.3 g/kg in specific humidity at each
point. For a 20 run ensemble, the mean rainfall in CTL
was relatively smooth, with few outliers. The domain‐
mean rainfall also converged to a single value between using
10 and 20 runs, suggesting that the average behaviour was
well represented by 20 runs.
3. Results
[12] The dynamical features of the simulations are similar
to GC11, with some differences due to the presence of
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rainfall‐generated cold‐pool outflows (not shown). As in
GC11, the heterogeneity in surface fluxes leads to temperature differences of up to 1 K between cropland and forest.
The temperature gradients drive the winds, producing convergence over the cropland boundaries and divergence over
the forest. As in GC11, the wind patterns organize the cloud
cover, with divergence suppressing cloud, and convergence
leading to “primary” triggering of convective clouds. Once
showers develop, low‐level cooling arising from downdraughts enhances the temperature gradients at the land‐
surface boundaries, and displaces these gradients further,
with “cold pools” of air [e.g., Simpson, 1999] propagating
across the domain. The cold pools lead to convergence,
causing “secondary” triggering of convective clouds at their
leading edge (or gust front).
[13] The effect of the cold pools is that the resulting cloud
cover distribution is less closely linked to the land‐surface in
these simulations compared to GC11, due to secondary
triggering by the cold‐pool outflows away from the land‐
surface boundaries, where they originate early in the afternoon. However, because rainfall is concentrated in the early
afternoon hours (14:00–16:00) and where clouds are deepest, the rainfall distribution is still closely linked to the
land‐surface features, as can be seen in Figure 1. Thus,
although rainfall has an impact on patterns of convergence
in the PBL via the presence of cold pool outflows, the cold
pools do not have a large impact on the observed land‐
atmosphere coupling.
[14] In order to better understand the link between the
land‐surface heterogeneity and rainfall amounts, both the
amplitude and spatial extent of the surface anomalies need
to be taken into account. To do this, wavelet analysis has
been used. Wavelet analysis decomposes a distribution into
wavenumber space as a function of domain position. In this
study, the wavelet chosen was the derivative of a Gaussian
function. The advantage of this wavelet is that it is well
suited for describing isolated peaks, which are characteristic
of rainfall distributions, and positive and negative signals
can be separated, so that the influence of cropland (positive
surface anomaly) and forest (negative surface anomaly) can
be assessed separately [Torrence and Compo, 1998]. In
Figure 1 the power of negative anomalies is multiplied by
−1 to differentiate them from positive anomalies.
[15] Figure 1 shows, for all 6 land surfaces, the Bowen
ratio multiplier, which describes the factor by which the
Bowen ratio was varied from the mean at each point, the
ensemble daily mean precipitation (Figure 1 (top plots) red
and black lines respectively), and their respective wavelet
powers (Figure 1 (bottom plots), line and coloured contours
respectively). The wavelet power spectrum is given by the
absolute value of the wavelet transform squared, similar to
the power spectrum derived from a Fourier transform. The
precipitation wavelet was normalised by the domain mean
wavelet power at each wavelength (l) for the CTL ensemble, to account for the background bias towards longer
wavelengths. The grey shading in the top plots represents
the ensemble interquartile range in precipitation amounts.
[16] In all cases the heterogeneous land surface exerts a
strong control on locally‐generated precipitation, leading to
strong gradients in rainfall amounts. In CTL the average
rainfall is 1.02 ± 0.02 mm, homogeneously spread across the
domain (not shown). In the heterogeneous runs precipitation
is concentrated on the cropland boundaries, with mean
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Figure 1. (top) Ensemble mean daily rainfall and (bottom) Bowen ratio offset (black and red lines respectively) and their
wavelets (coloured and line contours respectively) for (a) HET, (b) RN1, (c) RN2, (d) RN3, (e) RN4 and (f) RN5. In the top
plots the grey shading represents the interquartile range. In the bottom plots the line contours correspond to powers of
0.1,1,2 and 5 W. Solid lines are for positive wavelets and dashed lines for negative wavelets.
rainfall amounts of between 3.5–6 mm. The interquartile
range is evenly distributed around the mean at these peaks,
consistent with persistence over many runs as opposed to a
single intense event dominating the mean. The precipitation
over the forest is strongly suppressed, with rainfall amounts
consistently below 1 mm (e.g., −160 – −100 km in HET),
decreasing to negligible amounts close to certain boundaries
(e.g., −15 and 80 km in HET). This contrast between
cropland and forest can lead to a persistent difference in
rainfall of an order of magnitude over a distance of just
10 km (c.f. 110–120 km in HET). This link between the land
surface and precipitation is robust for all ensembles, even
when the surface flux gradients are not particularly strong,
noting that the full Bowen ratio offset range represents a
maximum change in sensible heat fluxes of 60 W/m2.
[17] This rainfall pattern is consistent with the mechanisms identified in GC11. At the vegetation‐breeze convergence zones CAPE is higher compared to both adjacent crop
and forest. This is due to vertical redistribution at the convergence zone of high CAPE air originating from the forest.
The enhancement of CAPE causes increased convective
development, leading to enhanced rainfall at the convergence zones. This effect is particularly significant for the
ensemble runs given that the location of the convergence
zones is persistent over all the runs. Subsidence over the
forest forms a warm capping layer strongly suppressing
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Figure 2. Scatter plot of domain‐mean rainfall and mean
surface wavelet power averaged for wavelengths of 20–
200 km for all ensembles. The error bars represent the standard error in calculating the mean rainfall.
cloud cover, leading to the reduction in precipitation
observed in Figure 1. It is also worth noting that the rainfall
pattern cannot be attributed to the magnitude of fluxes
alone, but is associated with the presence of the boundaries.
This can be observed in the difference in rainfall amounts
between −20 km and 40 km in RN2, or −50 km and −10 km
in RN4 and RN5.
[18] Figure 1 (bottom plots) reiterate the strong link
between the land cover and rainfall, with the peaks in
wavelet power closely collocated, albeit with a positive
y‐direction shift of about 10 km in precipitation relative
to the surface. Thermally‐induced breezes are expected to
be shallower and more coherent with a head wind [e.g.,
Garcia‐Carreras et al., 2010] and the background winds
in all runs are directed from positive to negative y, with
an approximate strength of 1 m/s in the afternoon. Given
that the land‐surface configuration is different for all the
runs, this suggests that it is the background winds that
cause the shift in precipitation.
[19] For wavelengths of 10–100 km the peak power of
negative surface and rainfall anomalies occurs at the same
wavelengths. This means that suppression of rainfall occurs
over all the forest in the presence of mesoscale variability.
This is consistent with subsidence due to developing convection at the convergence zones, as its impact will extend
over large areas, as shown in GC11. For example, in HET
rainfall is suppressed (compared to the average rainfall in
CTL) for 80 km between 140 km and −100 km. The magnitude of the anomalies also track closely, with stronger
anomalies at the surface associated with stronger suppression of rainfall. For example there is stronger suppression at
70 km compared to −50 km in RN2, and at 130 km compared to 0 km or −70 km in RN3, in both cases associated
with more pronounced anomalies in the land‐cover wavelet
power. This implies that stronger heterogeneity increases the
extent of the rainfall suppression, at least relative to the
enhanced rain over the crop. Overall, the magnitude and
spatial extent of rainfall suppression closely matches the
land‐surface heterogeneity in all the simulations.
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[20] Although the presence of increased rainfall over
cropland is robust, the peak values of the positive wavelet
power anomalies (cropland and enhanced rainfall) do not
match in the same way as for the negative anomalies. There
is, in general, a shift of the power in the precipitation
wavelet towards smaller wavelengths relative to the land
surface, particularly for the larger surface flux anomalies.
This is explained by the presence of considerable small‐
scale variability in the precipitation patterns. For example in
HET, for l = 2–10 km, there are distinct peaks in the precipitation wavelet power at 55, 120 and 130 km. These
correspond to sharp peaks in the rainfall at these locations.
These peaks are also associated with land‐surface features,
albeit weak ones, at 50, 115 and 125 km (l = 10 km).
Similarly, in RN2 a large cropland region leads to two
separate rainfall peaks at 25 and 40 km, coinciding with
small changes in surface fluxes (on the order of 10 W/m2)
embedded within the larger cropland region. This is consistent with Roy et al. [2003], who argue that for large‐scale
heterogeneities, smaller perturbations within them will break
up the land‐surface induced flows, leading to a preferred
scale for such flows of 10–20 km.
[21] The timings of the rainfall maxima sometimes differ.
For example, in the above example from RN2, the rainfall at
40 km occurs between 14:00 and 16:00, whereas at 25 km,
which lies over a much weaker land‐surface feature, it is
between 16:00 and 18:00. This later convection is a result of
triggering from cold‐pool outflows which originate at 40 km.
Thus, even precipitation arising from secondary initiation is
consistent over a number of runs. Given that rainfall often
occurs over weak surface features, it is possible that although
the surface flux gradients might not be sufficient to trigger
convection, the combination of the small surface flux gradient
and the arrival of the cold pool leads to enhanced precipitation
over these regions.
[22] The total domain‐mean rainfall is affected by the
surface heterogeneity. The mean rainfall for all ensembles
(excluding CTL) is of 1.15 ± 0.01 mm as opposed to 1.02 ±
0.02 mm in CTL, a statistically significant increase (at the
0.01 level) of 13%. Furthermore, there is a positive correlation between the domain‐mean rainfall and the mean
wavelet power of the surface heterogeneities (averaged for
wavelengths between 20 and 200 km, the scales at which
thermally induced breezes are expected to be significant
[Baldi et al., 2008]). The relationship is linear expect for the
two extremes in the land‐cover wavelet power (Figure 2),
with a maximum increase in rainfall compared to CTL of
22% (in RN5). This suggests that there is a cutoff heterogeneity intensity above which the impact on rainfall
amounts does not vary. This is to be expected, as total
rainfall amounts will ultimately be limited by the total
moisture fluxes in the model. It is also likely that the relationship has low sensitivity of rainfall to surface heterogeneity for low wavelet powers, where the heterogeneity may
not be large enough to initiate breezes.
4. Discussion
[23] The above results demonstrate that there is a significant coupling between the land‐surface heterogeneity and
locally‐generated rainfall, with a consistent (over a 20 run
ensemble) 4–6 fold increase in rainfall over cropland
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boundaries compared to the homogeneous case, and a strong
suppression of rainfall over the forest. These results were
remarkably consistent for a range of randomly generated
land‐surfaces of the same spectral distribution. These results
are also consistent with satellite observations of rainfall
[Knox et al., 2011], and with the processes described in
GC11. Subsidence over the forest acts to suppress cloud
initiation, thus considerably limiting rainfall amounts. The
cropland boundaries, on the other hand, are regions where
CAPE is maximized (GC11) leading to increased cloud
depth and organization. This mechanism leads to a persistent increase in rainfall over the cropland boundaries.
[24] The magnitude and spatial coverage of the suppression in rainfall follows the size and intensity of the surface
anomalies closely. In contrast the location and magnitude of
peak rainfall, although always occurring over a cropland
boundary, appears to be hard to predict. There are three
main factors which appear to determine where peak rainfall
occurs (although this list is not necessarily exhaustive):
[25] 1. Heat flux gradient, manifest in the wavelet power
for heat flux. The amplitude of the local gradients in surface
heat flux controls the strength of the vegetation‐breeze,
which in turn promotes convective triggering.
[26] 2. Size of patch related to the wavelet wavelength.
Land‐surface breezes tend to organise in certain preferential
length‐scales [Roy et al., 2003], and therefore patches close to
these scales will tend to cause stronger convective triggering.
[27] 3. Distance to other patches, related to the distance
between peaks in the wavelet analysis. As convection in one
location may suppress convection nearby, we anticipate that
proximity to other surface features will influence the statistics of convective triggering at each location.
[28] There are a number of factors which are not covered
by the modelling framework used in this study, a topic for
further research. The background wind will in reality vary in
space on the mesoscale, and in a 3D environment can
reorient, as well as advect, mesoscale rolls [Weaver and
Avissar, 2001; Weaver, 2004a]. Extending this study to
include the interaction between the mesoscale flows and the
background winds, particularly in a realistic 3D environment, would therefore be of interest.
[29] This study focuses on locally‐generated rainfall.
There is evidence that large‐scale organised systems, relatively insensitive to convective inhibition (CIN), rain more
over humid surface anomalies [Clark et al., 2003]. The
change in total rainfall at a location could therefore depend
on the proportion of local versus large‐scale propagating
convection it receives.
[30] The final factor involves land‐surface feedbacks.
Cloud shading and increased precipitation over the cropland
will decrease the Bowen ratio, and potentially lead to
accelerated vegetation regrowth. Complex interactions with
the larger scale can occur, as the surface soil moisture patches will interact with future storms, thus producing new
patterns in surface heterogeneity [Nykanen et al., 2001;
Weaver, 2004a, 2004b; Ramos da Silva and Avissar, 2006].
Observations show that land‐surface induced breezes are
climatologically significant in various regions despite these
feedbacks. Their impact must, however, be better understood to correctly quantify the climatological impact of
deforestation on rainfall.
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[31] Acknowledgments. LGC was funded by a NERC studentship
NE/F007477/1. The work was also supported by NERC grants NE/
B550538/1 and NE/G018499/1. The authors would like to thank John
Marsham and Chris Taylor, as well as the two anonymous reviewers, for
constructive comments on the results and manuscript. Based on a French
initiative, AMMA was developed by an international scientific group and
funded by a large number of agencies, especially from Africa, European
Community, France, UK and USA. More information on the scientific
coordination and funding is available on the AMMA International web site:
http://www.amma-international.org.
[32] The Editor thanks two anonymous reviewers for their assistance
evaluating this manuscript.
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L. Garcia‐Carreras and D. J. Parker, Institute for Climate and
Atmospheric Science, University of Leeds, Leeds LS2 9JE, UK. (eelgc@
leeds.ac.uk)
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