INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 31: 1205–1221 (2011)
Published online 7 April 2010 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.2139
Assessing the dynamic-downscaling ability over South
America using the intensity-scale verification technique
F. De Salesa * and Y. Xuea,b
a
b
Department of Geography, University of California, Los Angeles, CA, USA
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA
ABSTRACT: The National Centers for Environmental Prediction (NCEP) ETA regional circulation model (RCM) was
one-way nested in the T62 NCEP general circulation model for a series of 3-month simulations of the austral summer and
winter over South America (SA). The intensity-scale verification technique (ISVT), based on the scale decomposition of
precipitation skill score and energy relative difference, was used to quantitatively assess the dynamic-downscaling ability
of seasonal precipitation and inter-annual precipitation difference. The ISVT showed that the RCM was able to add value to
summer and winter rainfall forecasts over southern South America. Largest improvements were associated to precipitation
events at spatial scales of about 400–800 km and rainfall rates above 4 mm day−1 . In general, downscaling failed to yield
better results over northern South America. In terms of inter-annual precipitation difference, the RCM produced better
results over southern South America, by simulating the increase in intense small-scale events in the wet years. Analysis of
meridional moisture flux associated with the South American low-level jet (SALLJ) suggested that the Andean topography
plays an important role in the RCM’s rainfall simulations over the La Plata basin. A sensitivity test with lowered Andean
topography heights produced weaker moisture advection by the SALLJ, and lower rainfall totals over that basin for both
seasons. During summer, results showed that the reduced rainfall was associated with deteriorated simulations of midto large-scale precipitation events, whereas during winter it was associated with deteriorated simulations of smaller-scale
events. Copyright  2010 Royal Meteorological Society
KEY WORDS
intensity-scale verification; seasonal and inter-annual climate simulation; precipitation verification
Received 5 October 2009; Revised 22 February 2010; Accepted 28 February 2010
1.
Introduction
The study of seasonal climate, more specifically
precipitation, with regional climate models (RCM) has
become increasingly popular over the past years (Fennessy and Shukla, 2000; De Sales and Xue 2006; Xue
et al., 2007; Rockel et al., 2008). The use of general
circulation model (GCM) results as lateral boundary conditions (LBCs) for RCM simulations is called dynamic
downscaling (Castro et al., 2005; De Sales and Xue,
2006). Pioneer studies in this area include Dickinson
et al. (1989) and Giorgi and Mearns (1991). The goal
of dynamic downscaling is to obtain a finer and more
realistic representation of regional-scale features associated with topography and land surface features. GCMs,
in most cases, do not have sufficient horizontal and vertical resolution to resolve local climate features induced
by smaller-scale variations in topography and land cover,
which in turn affect precipitation.
This is especially important over South America (SA)
where the continent–ocean interaction circulations (e.g.
SA monsoon system) and topography-induced circulations (e.g. SA low-level jet) have important regional-scale
* Correspondence to: F. De Sales, Department of Geography, University of California, 1255 Bunche Hall, Box 951524, Los Angeles, CA
90095, USA. E-mail: [email protected]
Copyright  2010 Royal Meteorological Society
features and play a crucial role in the formation and
distribution of tropical and subtropical precipitation. The
use of RCM to investigate SA’s seasonal precipitation
has shown encouraging results (Chou et al., 2000; Misra
et al., 2003; De Sales and Xue, 2006; Seth et al., 2007).
The work by De Sales and Xue (2006) indicated that, by
dynamically downscaling GCM’s outputs through oneway nested RCM simulations, one can reduce the root
mean squared error (RMSE) in seasonal mean predictions of precipitation and surface air temperature when
compared to GCM alone. The study showed that the
only region that did not experience improvement from
the downscaling was the eastern Amazon basin.
A less-studied topic associated with dynamicdownscaling techniques is the quantitative evaluation of
the downscaling impact at different spatial scales. In the
works of Castro et al. (2005) and Rockel et al. (2008),
the power spectrum decomposition of column-average
moisture flux convergence, kinetic energy, and precipitation were used to assess the added value by dynamic
downscaling for a series of simulations over North America. They concluded that the downscaling did not retain
the value of the large-scale features over and above
that which were already present in the GCM results.
In another North American downscaling study, however, Xue et al. (2007) found that the RCM has proper
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downscaling ability only under certain conditions, such
as proper domain boundary location among others. These
studies suggested that the purpose of the downscaling was
not to add skill to the large scale, but to add value to the
smaller-scale features, which have a greater dependence
on the land surface characteristics.
For the present study, we use the 80-km resolution
NCEP ETA RCM nested in the T62 NCEP GCM for a
series of 3-month simulations of the austral summers and
winters over South America. We focus the analysis on
the precipitation downscaling ability at different scales
as simulated by the GCM and RCM. The impact of
the Andes Mountain Range on the RCM results is also
investigated through a sensitivity test.
The La Plata basin is the focus region in this study.
It is analogous to the Amazon basin in terms of its
biological diversity, but far exceeds the latter in its
economic importance to south-eastern and central South
America in terms of hydroelectricity and food production
(Vera et al., 2006). The basin comprises almost all the
southern part of Brazil, as well as parts of Uruguay,
Paraguay, and an extensive part of northern Argentina.
The total human population depending on the basin is
estimated to be approximately 67 million (Organization
of American States). Therefore, the ability to forecast the
inter-annual variability of seasonal precipitation is critical
for this region.
We apply the intensity-scale verification technique
(ISVT) of precipitation (Casati et al., 2004; Casati, 2010)
to assess the role of dynamic downscaling on the simulation quantitatively. This verification method evaluates the
forecast skill score (SS) and energy of precipitation as a
function of different spatial scales and precipitation intensities. The scale components are obtained by a 2-D Haar
discrete wavelet transform, whereas different precipitation intensities are selected by thresholding. The ISVT’s
SS is a generalisation of the Heidke SS based on the mean
square error (Wilks, 2006). The precipitation energy is
assessed through the Frequency Bias Index (Jolliffe and
Stephenson, 2003), and it is used in this study to evaluate
the inter-annual difference in the models’ results.
Unlike the method utilised by Castro et al. (2005) and
Xue et al. (2007), the intensity-scale approach can simultaneously provide information about the downscaling performance of both the size and intensity of precipitation
features. Furthermore, results obtained from the waveletbased approach are bound to be more robust. In fact,
because of their locality, Haar’s wavelet transforms are
less susceptible to Gibb’s phenomena often triggered by
the Andes steep topography. In addition, wavelets are
also more suitable than Fourier transforms to deal with
spatially discontinuous fields, such as precipitation, especially in limited-area, non-global domains.
The ISVT has been successfully used in nowcasting
and probability forecasting studies (Mittermaier, 2006;
Casati and Wilson, 2007; Csima and Ghelli, 2008).
This method has not been used to evaluate dynamicdownscaling performance at seasonal time scales. As
mentioned above, the method allows the model skills
Copyright  2010 Royal Meteorological Society
to be diagnosed as a function of the spatial scale of
the forecast error and intensity of the rainfall events.
Therefore, it is an efficient tool to evaluate large-scale and
mesoscale features, as well as intense and weak events
in downscaling studies.
Brief descriptions of the models, detailed experimental
design, as well as a concise explanation of the ISVT are
presented in the next section. The impact of dynamic
downscaling on the seasonal average precipitation is
examined in Section 3.1, while in Section 3.2 we assess
its role in the precipitation inter-annual variability. The
effect of the Andes Mountain Range topography on the
RCM simulation is described in Section 4. The final
discussions and conclusions are presented in Section 5.
2.
2.1.
Models and experimental design
Atmospheric models
The version of the NCEP GCM utilised in this study
was similar to the one used by De Sales and Xue
(2006). The model was set up on a triangular 62truncation horizontal resolution and 28 vertical levels.
The model includes Chou (1992) and Chou and Suarez
(1994) radiation scheme, Hong and Pan (1996) non-local
planetary boundary scheme, and the Moorthi and Suarez
(1992) convection scheme.
As for the higher-resolution RCM, we utilised the
NCEP limited-area ETA model. The ETA is a state-ofthe-art atmospheric model used for research and operational purposes. This model evolved from the earlier Hydrometeorological Institute and Belgrade University model with step-like mountain vertical coordinates (Mesinger et al., 1988; Janjic, 1990). The model’s
code has since been upgraded to include more advanced
schemes such as the Arakawa-style horizontal advection
scheme (Janjic, 1984), a radiation scheme based on Lacis
and Hansen (1974) and Fels and Schwartzkopf (1975); a
Kolmogorov–Heisenberg-type closure scheme to represent turbulence in the planetary boundary layer and in
the free atmosphere. In terms of precipitation, the model
utilises the Betts–Miller–Janjic scheme for deep and
shallow moist convection (Betts, 1986; Janjic, 1994), and
a grid-scale precipitation scheme based on Zhao and Carr
(1997).
For this study, the ETA model was set up on an 80-km
horizontal resolution and 38 vertical levels grid covering
most of the South American continent and the adjoining
Atlantic and Pacific Oceans. Further detailed information
on the GCM and ETA model can be found in the works
by Kanamitsu et al. (2002a) and Black (1994).
Both models were modified to include a more sophisticated land surface processes parameterisation scheme
(Xue et al., 2001, 2004). The Simplified Simple Biosphere model version 1 (SSiB-1, Xue et al., 1991)
was used as the land surface processes model in all
simulations. In addition to simulating processes, such
as runoff, direct vegetation and bare soil evaporation, and photosynthesis-controlled canopy transpiration,
Int. J. Climatol. 31: 1205–1221 (2011)
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this model also ensures energy, water, and momentum
conservation at the atmosphere–land surface interface.
The SSiB-coupled versions of GCM and ETA models have been extensively tested (Xue et al., 2004, 2006,
2007). Information regarding the ETA/SSiB-1 coupling,
vegetation classification, and vegetation parameters for
the SSiB-1 can be found in Xue et al. (2001). Hereafter, for simplicity, the coupled versions of the NCEP
GCM/SSiB-1 and ETA/SSiB-1 are referred to as GCM
and RCM respectively.
Gauge station distribution
2
2.2. Experimental design
The austral summer (December through February) and
winter (June through August) of 1988 and 1997 were
selected for this study because they exhibited very large
inter-annual differences in precipitation over tropical
and subtropical South America. The winter of 1997
and the summer of 1997–1998 (referred to as JJA97
and DJF97–98, respectively, hereafter) witnessed the
strongest El Niño of the twentieth century (Oceanic
Niño Index (ONI) equal to 1.7 and 2.4, respectively).
Similarly, the winter of 1988 and summer of 1988–1989
(JJA88 and DJF88–89, hereafter) were characterised by
a strong La Niña event (ONI equal to −1.2 and −1.7,
respectively). The investigation of such intense El NiñoSouthern Oscillation events would be very instructive in
demonstrating the GCM and RCM abilities to simulate
precipitation inter-annual differences.
Five-member ensemble integrations were performed
with the GCM and RCM for each season studied, starting
from slightly different initial conditions. Table I lists the
initial and final dates of each ensemble member. The
results presented herein are actually based on the fivemember ensemble mean of each experiment. The GCM
integrations were carried out first and results were saved
every 6 h. The GCM integrations were initialised from
NCEP-DOE AMIP-II global reanalysis (Kanamitsu et al.,
2002b) at 00 UTC of each starting date as shown in
Table I.
For each of the GCM integrations, a downscaling
integration was performed with the ETA model, starting
from the same initial date and initial conditions as the
GCM. The RCM’s LBCs were updated every 6 h of
simulation from the GCM simulations output. The initial
1
Figure 1. Distribution of rainfall gauge stations in CPC dataset for the
period covered in this study. Dashed lines indicate the areas where the
decomposition of precipitation SS and ERD were calculated.
conditions for soil temperature and wetness, initial snow
cover, as well as daily sea surface temperature (SST) and
sea ice concentrations for all experiments were also taken
from the NCEP-DOE AMIP-II global reanalysis.
To validate the simulations, the model results were
compared against the National Oceanic and Atmospheric
Administration (NOAA) NCEP Climate Prediction Center (CPC) observed daily precipitation analysis (Shi et al.,
2001; Silva et al., 2007). This dataset contains daily precipitation over land for the entire South American continent. Figure 1 shows the distribution of rain gauge for
the time period covered in this study. The minimum number of stations for the daily precipitation analysis is 250.
If the number of stations is less than the minimum, then
the analysis is not performed for that day. The CPC daily
Table I. Initial and final dates of ensemble members used in the GCM and RCM simulations.
Ensemble
Summer
Ensemble
Winter
DJF88–89
29 Nov 1988 to 01 Mar 1989
30 Nov 1988 to 01 Mar 1989
01 Dec 1988 to 01 Mar 1989
02 Dec 1988 to 01 Mar 1989
03 Dec 1988 to 01 Mar 1989
29 Nov 1997 to 01 Mar 1998
30 Nov 1997 to 01 Mar 1998
01 Dec 1997 to 01 Mar 1998
02 Dec 1997 to 01 Mar 1998
03 Dec 1997 to 01 Mar 1998
JJA88
29 May 1988 to 01 Sep 1988
30 May 1988 to 01 Sep 1988
01 Jun 1988 to 01 Sep 1988
02 Jun 1988 to 01 Sep 1988
03 Jun 1988 to 01 Sep 1988
29 May 1997 to 01 Sep 1997
30 May 1997 to 01 Sep 1997
01 Jun 1997 to 01 Sep 1997
02 Jun 1997 to 01 Sep 1997
03 Jun 1997 to 01 Sep 1997
DJF97–98
Copyright  2010 Royal Meteorological Society
JJA97
Int. J. Climatol. 31: 1205–1221 (2011)
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F. DE SALES AND Y. XUE
precipitation analysis is available on a 1° by 1° horizontal
grid. All model fields were interpolated to the CPC analysis grid for comparison purposes. As CPC precipitation
data only covers land points, our analysis is limited to
precipitation over the continent.
2.3.
The intensity-scale verification technique
The ISVT (Casati et al., 2004; Casati, 2010) was used
to assess the models’ performances in terms of seasonal and inter-annual precipitation simulations. This
technique assesses the forecast skill and bias of precipitation features as a function of the spatial scale and
intensity. Unlike decomposition methods that use Fourier
transforms, the ISVT is based on a categorical distribution approach, which is a more suitable and robust
method to deal with spatially discontinuous and sparse
fields, such as precipitation, which are also characterised
by highly skewed intensity distributions. Furthermore,
discrete wavelet decompositions are orthogonal, which
allows for the decomposed verification statistics to be
additive (Casati, 2010).
Thresholding is initially used to convert the observed
and modelled precipitation fields into binary fields, Io
and Im respectively. These fields are transformed into
binary images on a precipitation/no-precipitation basis for
a given precipitation rate threshold (τ ). The difference
between binary modelled and observed precipitation is
defined as the binary error field (Z = Io − Im ). The 2-D
Haar-wavelet transform is then used to decompose the
binary error field into the sum of components at different
spatial scales (l). For a given threshold, the mean squared
error (MSE) of the binary field is defined by the average
of all the squared differences over all the grid-points
MSEτ =
L
MSEτ ,l
(1)
l=1
where MSEτ,l = Zl2 is the MSE of the lth spatial scale
component of the binary error field for threshold τ the
overbar indicates averaging of the squared values over
all the grid-points. For each precipitation threshold and
spatial scale, the SS is calculated as
SSτ ,l =
MSEτ ,l − MSErandom,τ
MSEbest − MSErandom,τ
(2)
where MSErandom,τ represents the MSE of a random
forecast calculated on the basis of the bias and base rate
for each threshold and equipartitioned across all scales
(Casati, 2010); and MSEbest is the MSE associated with
a perfect simulation (MSEbest = 0). The double overbars
represent temporal aggregation over the 3-month length
of simulation as described in Casati (2010).
Generally, intensity-scale decompositions of SS tend
to show low skill for very intense rainfall rates and
very small spatial scales, which represent small-scale
convective storms and are more difficult to simulate. In
Copyright  2010 Royal Meteorological Society
contrast, frontal and non-convective large precipitation
systems are more often properly simulated, thus yielding
higher scores. Negative scores are associated with the
model’s performances that are not better than a random
prediction.
The observed and modelled precipitation energies are
evaluated in a similar fashion as MSEτ ,l from the
thresholded precipitation binary fields decomposed into
different spatial scale components by the same wavelet
transform. For instance, PEτ ,l = I ol 2 is the precipitation
energy associated with the lth spatial scale component of
the observed binary field obtained for threshold τ . The
same notation is used for the model energy. Precipitation
energy is used in this study to evaluate the precipitation
inter-annual differences between the summer and winter
seasons of 1997 and 1988 through the precipitation
energy relative difference (ERD) as defined below.
ERDτ ,l =
88
PE97
τ ,l − PEτ ,l
(3)
88
PE97
τ ,l + PEτ ,l
where the double overbars represent temporal aggregation over the length of simulation. The precipitation ERD
decomposition is proportional to the number of precipitation events exceeding the threshold τ and is associated with features at scales l. The ERD thus provides
insightful information on the intensity-scale nature of the
mechanisms associated with the inter-annual variability
for the summer and winter precipitation. By comparing
ERD from observation and simulation, the downscaling
ability in inter-annual variation at different spatial scales
and precipitation intensity can be validated.
We applied the ISVT over two areas of interest, i.e.
southern and northern South America, area 1 and area
2 respectively, as depicted in Figure 1. Each area is 32°
latitude by 32° longitude wide, which yielded a total of 6
Haar-wavelet components, l = 1, 2, . . . 6, corresponding
to 1° , 2° , 4° , 8° , 16° , and 32° resolutions respectively. The
1° component is beyond the GCM’s resolving capability,
and, therefore, it is not included in the analysis. It is
important to note that the 32° component corresponds to
the average over the entire area of interest. The decision
to separate the results into two areas was based on the fact
that precipitation in southern (subtropical) and northern
(tropical) South America are associated with different
physical processes of different spatial scales. While in
area 2, convective storms are the prevalent source of
precipitation, a comparable mix of frontal and convective
precipitation occurred in area 1.
3.
Analyses of results
In this section, we discuss the validation results from
seasonal mean and inter-annual variability perspectives.
Summer (winter) seasonal mean precipitation in this
paper is referred to the average between DJF88–89
Int. J. Climatol. 31: 1205–1221 (2011)
DYNAMIC DOWNSCALING OVER SOUTH AMERICA
(a)
(b)
(c)
(d)
(e)
(f)
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Figure 2. Average precipitation for summer for (a) CPC observation, (b) GCM, (c) RCM; and for winter for (d) CPC observation, (e) GCM,
and (f) RCM. Unit: mm day−1 .
and DJF97–98 (JJA88 and JJA97) daily model forecasts, while summer (winter) seasonal difference indicates DJF97–98 minus DJF88–89 (JJA97 minus JJA88)
for models and observation.
3.1. Quantitative assessment of dynamic
downscaling for seasonal precipitation
Observed and modelled summer seasonal average
precipitations are shown in Figure 2(a)–(c). CPC data
show a typical wet-season rainfall pattern over South
America with precipitation totals above 6 mm day−1 over
the central portion of the continent, extending northward
towards the Amazon River mouth, and south-eastward
towards south-east Brazil. Areas with average precipitation above 4 mm day−1 extended southwards into the La
Plata basin. Dry areas include north-east Brazil, extreme
northern South America, and the southern Andes Mountain Range.
The GCM shifted the intense precipitation area northeastward, overestimating the rainfall over eastern Brazil.
On the other hand, the global model underestimated the
Copyright  2010 Royal Meteorological Society
rainfall over the La Plata basin and western Amazon
basin. Figure 2(b) also shows an area of intense rainfall
(>10 mm day−1 ) along the eastern slopes of the northern
Andes for the GCM, which only marginally appeared in
the CPC product.
The RCM downscaling improved the results over the
subtropical section of the continent, especially along
the eastern slopes of the Andes and southern Brazil,
where the regional model increased the total rainfall for
the summer season (Figure 2(c)). However, the regional
model was not able to simulate the rainfall over northern
South America very well, which was better represented
by the GCM. Both models captured the dry areas over
the south-west well. Chou et al. (2005) reached similar
results based on 4-month long simulations (November to
February) with a 40-km resolution version of the ETA
model and a different GCM for the 2002–2003 wet
season.
The lack of rainfall in the RCM over northern South
America is also noticeable in a number of studies
(Berbery and Collini, 2000; Rojas and Seth, 2003; Chou
et al., 2005; De Sales and Xue, 2006; Seth et al., 2007).
Int. J. Climatol. 31: 1205–1221 (2011)
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F. DE SALES AND Y. XUE
It has been suggested to be caused by mass imbalance
led by the one-way nesting configuration (De Sales
and Xue, 2006), as well as deficient parameterisation
of convective processes and biases in the initial soil
moisture distribution over the tropical areas (Berbery and
Collini, 2000). Seth et al. (2007), in a set of multi-annual
downscaling simulations, also found a similar dry bias
over the Amazon region. They concluded that the bias
was associated with weak moisture transport from the
ocean due to improper representation by the regional
model of the sea-level pressure over the equatorial
Atlantic, which in turn reduced the strength of the trade
winds in the region. In addition, Seth et al. (2007) also
showed that the Amazon dry bias is very sensitive to the
RCM’s convective scheme.
A detailed investigation of the mechanisms behind
this issue is beyond the scope of this article and is
not discussed further in this paper. Nevertheless, the
intensity-scale verification analysis of the RCM SSs,
discussed next, illustrates the nature of the precipitation
associated with the model performance over northern
South America.
Winter seasonal average precipitations are shown in
Figure 2(d)–(f). The observation indicates that most of
the precipitation was concentrated over the northern sections of the continent between 10 ° S and 10° N. Significant
precipitation was also measured over the La Plata basin
region, southern Andes, and the north-eastern littoral of
Brazil. Most of the continent interior centre received only
less than 1 mm day−1 of precipitation (Figure 2(d)).
The global and regional models were able to capture
the main features of the wintertime precipitation. The
GCM overestimated the rainfall over in the north-eastern
South America, while generally underestimated it in the
La Plata basin (Figure 2(e)). The downscaling results
were more comparable to the observation over southeastern and north-eastern South America. The regional
model also produced some precipitation along the eastern
slopes of the central Andes, which is in the CPC
product but is absent in the GCM results (Figure 2(f)).
The wintertime differences between GCM and RCM,
especially the rainfall increase over the La Plata basin, are
similar to the results in Chou et al. (2005), which describe
40-km ETA model simulations for May to August of
2002.
On the basis of Figure 2, the average summer precipitation MSEs over area 1 for the GCM and RCM
were respectively 3.33 and 1.56 mm2 day−2 . As for area
2, the average MSEs were 10.34 and 16.89 mm2 day−2 ,
respectively. These numbers represent a decrease in MSE
of approximately 53% over southern South America and
an increase of about 63% in MSE over northern South
America due to downscaling. Winter precipitation MSE
results for the GCM and RCM were respectively 1.24 and
0.85 mm2 day−2 over area 1 (31% decrease) and 4.14 and
6.53 mm2 day−2 over area 2 (58% increase).
To provide a quantitatively and detailed measure of
the dynamic-downscaling performance, we utilise the
ISVT. The intensity-scale decompositions of summer
Copyright  2010 Royal Meteorological Society
precipitation SS for the GCM and RCM over southern
South America (area 1) are shown in Figure 3(a) and (b).
Lighter grey areas indicate higher scores; negative scores
are blackened and indicate the portion of the spectrum
for which the model’s performance is not better than
random prediction. Scores equal to or higher than 0.8
appear white.
In general, for both models, the scores decrease as precipitation thresholds increase and spatial scales decrease.
Large thresholds at small scales are associated with
intense localised convective storm, which are more difficult to simulate at the model resolutions utilised in
this study. As the spatial scale increases, the scores
for a given rainfall threshold also increase. The higher
scores are found for very large and weak rainfall thresholds, which include frontal and non-convective synoptic rainfall features. At 32° spatial scale, both models
show reasonably skilful results (above 0.8) for all rainfall
intensities. As previously mentioned, this scale represents the SSs for the average precipitation over the entire
area 1.
The black shaded areas on the SS spectra indicate
the spatial scales and thresholds for which the models
show no skill in terms of precipitation simulation. These
include thresholds higher than 10 mm day−1 at scales
smaller than 8° for the GCM and 4° for the RCM.
Therefore, the no-skill spectrum for the RCM is smaller
than that for the GCM, indicating an improvement in
simulating intense summer rainfall events over area 1
with the downscaling.
Figure 3(c) and (d) exhibits the summer precipitation’s
intensity-scale decompositions of SS over northern South
America (area 2). Similar to area 1, higher scores are
associated with weak rainfall thresholds and large rainfall
events. However, RCM’s scores at area 2 exhibit a significant decrement at weaker rainfall thresholds when compared to that of GCM. The intensity-scale SS spectrum
shows that the RCM had some difficulties in simulating
the average rainfall over area 2, especially at higherintensity thresholds. There was still some reduction in
the no-skill (negative scores) region of the spectrum by
the downscaling.
To clearly delineate the comparison between models’
performances, Figure 4(a) and (b) display the difference
between RCM and GCM scores for the summer simulations over the two areas of interest. To highlight the major
differences between models, only differences higher than
0.1 and smaller than −0.1 are shown. For area 1, the
downscaling improvement was concentrated for spatial
scales between 4° and 16° at different thresholds. Such
scales correspond to nearly 2 to 8.5 times the GCM’s
horizontal resolution, which is approximately 1.875° . The
RCM’s largest improvement was for rainfall events at 8°
spatial scale. Meanwhile, improvement was also observed
in high-precipitation events around 4° and in middle-tohigh precipitation events around 16° .
On the other hand, SS differences over northern South
America indicate that the downscaling improved the
simulation results for only a small fraction of the rainfall
Int. J. Climatol. 31: 1205–1221 (2011)
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DYNAMIC DOWNSCALING OVER SOUTH AMERICA
Area 1 - GCM
(b)
Area 1 - RCM
32
32
(a)
0.8
8
8
0.6
0.4
4
0.2
4
spatial scale (degree)
16
16
1.0
2
2
0.0
0.1 0.5
1
1.5
2
3
4
6
8
10
12
15
0.1 0.5
1
1.5
2
threshold (mm/d)
Area 2 - GCM
4
6
8
10
12
15
Area 2 - RCM
32
(d)
32
(c)
3
threshold (mm/d)
16
0.8
8
8
0.6
0.4
4
0.2
4
spatial scale (degree)
16
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2
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0.0
0.1 0.5
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12
0.1 0.5
15
1
1.5
2
3
4
6
8
10
12
15
threshold (mm/d)
threshold (mm/d)
Figure 3. Decomposition of summer precipitation SS in area 1 for (a) GCM and (b) RCM; and in area 2 for (c) GCM and (d) RCM. Negative
scores are blackened.
(a)
(b)
Figure 4. Difference between RCM and GCM precipitation SS decompositions for summer over (a) area 1 and (b) area 2. Negative values are
hatched.
spectrum. The improvement associated with downscaling
was mostly restricted to intense rainfall events (above
4 mm day−1 ) of spatial scale of 4° , which correspond
Copyright  2010 Royal Meteorological Society
to about two times the GCM’s resolution. For most
of the spectrum, however, the GCM exhibits higher
forecasting skills, especially for weak rainfall thresholds,
Int. J. Climatol. 31: 1205–1221 (2011)
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Area 1 - GCM
(b)
Area 1 - RCM
32
32
(a)
0.8
8
8
0.6
0.4
4
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4
spatial scale (degree)
16
16
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0.0
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2
3
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8
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12
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threshold (mm/d)
2
3
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threshold (mm/d)
Area 2 - GCM
Area 2 - RCM
32
(d)
32
(c)
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spatial scale (degree)
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15
threshold (mm/d)
0.1 0.5
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3
4
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8
10
12
15
threshold (mm/d)
Figure 5. Decomposition of winter precipitation SS in area 1 for (a) GCM and (b) RCM; and in area 2 for (c) GCM and (d) RCM. Negative
scores are blackened.
below 4 mm day−1 . The GCM’s scores over area 2
were significantly higher for nearly all rainfall rates.
This result agrees with Figure 2(c), which shows a
large precipitation bias over the Amazon basin in the
RCM results. The SS decomposition clearly indicates
that the biases were due to the RCM’s poor simulation
of mid-intensity to weak precipitation thresholds events
(between 0.1 and 4.0 mm day−1 ), which comprise a large
part of the rainfall over the region.
Winter precipitation SS decompositions were also
calculated for the same two areas (Figure 5). Observation
of Figure 5(a) and (b) shows a shrinking of the no-skill
portion of the spectrum after downscaling over area 1.
This is more evident for rainfall events at scales between
4° and 8° at almost every precipitation intensity event.
The same occurred for precipitation rates at and above
12 mm day−1 , which the GCM could not produce at all
over this area in the 1988 simulations. Figure 5(c) shows
that, over area 2, such an improvement was not as evident
Copyright  2010 Royal Meteorological Society
by the RCM. Overall, GCM exhibits higher score in this
area during winter (Figure 5(c) and (d)).
The difference between winter scores of RCMs and
GCMs over area 1 (Figure 6(a)) shows that downscaling
improvement was more widespread spectrum wise, than
that during summer. Most of the positive differences were
not concentrated at the centre of the spectrum as for
summer, but reached nearly all rainfall rates and scales.
The largest differences occurred for scales between 4°
and 8° . Again, the SS decomposition corroborates with
the winter average maps, which indicated better results
by the RCM over area 1. Unlike seasonal average
maps, the SS decomposition explicitly indicates which
rainfall events actually benefited from downscaling; in
this case, mostly events of sizes between about 4°
and 8° .
Figure 6(b) shows the difference between RCM and
GCM winter precipitation scores over area 2. The most
distinct differences are located at very large and very
Int. J. Climatol. 31: 1205–1221 (2011)
DYNAMIC DOWNSCALING OVER SOUTH AMERICA
(a)
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(b)
Figure 6. As in Figure 4 but for the winter season.
small rainfall spatial scales, for which the GCM produced better simulations. Downscaling improvements are
restricted to rainfall features at 4° of size for thresholds between 6 and 10 mm day−1 . Figure 2(f) indicates a negative bias in winter rainfall simulated by
the regional model over area 2. While both observation
and GCM exhibit large areas of seasonal rainfall over
6 mm day−1 , the RCM barely shows signs of rainfall
over 4 mm day−1 . Winter SS decomposition suggests
that the dry bias resulted from RCM’s deficiency in
simulating large- and small-scale rainfall features.
3.2. Assessing RCM’s downscaling of inter-annual
precipitation differences
The simulated and observed average precipitation difference over land between DJF97–98 and DJF88–89 is
shown in Figure 7. The CPC product exhibits a typical
El Niño/La Niña summer/winter precipitation difference
pattern in South America (Zhou and Lau, 2001). Negative differences are found over northern South America,
including in the Amazon basin. Some locations exhibited
over −4 mm day−1 in precipitation difference. In contrast, positive differences were found over north-western
South America, between 10 ° S and the equator, the La
Plata basin, as well as over central-eastern South America between 20 ° S and 10 ° S. The La Plata basin has the
largest positive differences.
The GCM and RCM precipitation inter-annual difference is shown in Figure 7(b) and (c) respectively. The
shaded areas in the figure indicate that the differences
were consistent over the 95% confidence levels, based
on a 2-tailed Student’s t-test. The GCM captured the
negative difference north of the equator and the positive difference along the coastal Ecuador. The model also
gives some indication of negative difference in the Amazon basin. However, it does not reproduce the differences
over the La Plata basin. Moreover, the GCM results
exhibit positive differences over the north-eastern tip of
South America, which is inconsistent with observations.
Copyright  2010 Royal Meteorological Society
The downscaling significantly improved the spatial
pattern and intensity of the precipitation inter-annual
difference in the La Plata basin. The average precipitation
differences (DJF97–98 minus DJF88–89) over area 1
(Figure 1), which includes the La Plata basin, are 0.08
and 0.55 mm day−1 for the GCM and RCM respectively.
The same average equals to 1.65 mm day−1 for the CPC
product. In general, the RCM produced better results than
the GCM over eastern and subtropical South America.
In contrast, the RCM did not capture the large area of
negative differences over the Amazon basin and northern
South America. The average precipitation differences
over area 2 (Figure 1), which consists of the Amazon
basin and northern South America, for the CPC, GCM,
and the RCM are −1.20, −1.11, and −0.06 mm day−1
respectively.
Figure 7(d) shows the observed precipitation difference between JJA97 and JJA88. While both models were
able to simulate the negative difference in rainfall in
northern South America, only the RCM was able to
simulate the precipitation difference in the south-east.
The winter average precipitation differences over area 2
from the CPC observation, GCM, and RCM were −1.33,
−1.71, and −1.24 mm day−1 , respectively. On the other
hand, over area 1 the average precipitation differences
were 1.19, 0.46, and 1.08 mm day−1 , respectively.
As discussed in Section 2.3, the energy of precipitation
is directly related to the amount of rainfall events
during a given period. Therefore, the intensity-scale
decomposition of precipitation ERD can be used to
assess quantitatively the intensities and spatial scales
of precipitation features that contributed to the total
difference between two given time periods. As the spatial
scale is intimately related to physical processes behind a
precipitation event, the comparison between observation
and model precipitation ERD can provide an assessment
on the model’s ability to simulate such processes. Only
differences larger than ±0.1 are shown to highlight the
major intensity-scale differences.
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(a)
(b)
(c)
(d)
(e)
(f)
Figure 7. Precipitation difference between DJF97 and DJF88 for (a) CPC observation, (b) GCM, (c) RCM, and between JJA97 and JJA88 for
(d) CPC observation, (e) GCM, and (f) RCM. Unit: mm day−1 . Grey shaded areas indicate differences consistent over the 95% confidence
levels.
Figure 8(a) shows the observed ERD of precipitation,
as defined in Equation 3, between DJF97–98 and
DJF88–89 over area 1. Positive (negative) values indicate a larger number of rainfall events in DJF97–98
(DJF88–89). The observation data exhibits the largest
difference between the two summers at larger rainfall
thresholds. This indicates that intense rainfall events contribute more to the overall inter-annual differences.
The GCM shows very weak ERD (Figure 8(b)),
indicating that the model could not simulate the difference in intense rainfall between the two summers. On the
other hand, the RCM improved the results by correctly
simulating the partition of precipitation ERD towards
higher rainfall thresholds. The 32° spectral component
shows that the RCM simulated the average rainfall difference in area 1 better by increasing the number of intense
rainfall events, which corroborates with the average difference shown in Figure 7(c).
For area 2, the overall negative values indicate a larger
number of intense precipitation events in the summer of
1988 over that region (Figure 8(d)). The largest observed
Copyright  2010 Royal Meteorological Society
differences occurred at very intense rainfall thresholds
throughout the spatial scale spectrum. Even at very small
scales, the differences were significant. The global model
simulated the increase in intense rainfall events during
DJF88–89 over area 2 generally well (Figure 8(e)). The
downscaling results also show differences for intense
precipitation events. However, the RCM’s differences are
rather weak (Figure 8(f)). In terms of the average over
the entire area, the downscaling underestimated the mean
precipitation difference, which agrees with the results
shown in Figure 7(c).
Winter precipitation ERD over area 1 can be found in
Figure 9. Observation shows an increase in JJA97 rainfall
for most of the decomposition spectrum. The largest
differences occurred at high rainfall thresholds. Both
models exhibit an overall positive difference between
JJA97 and JJA88 in area 1 (Figure 9(b) and (c)). The
GCM was not able to simulate the rainfall events with
intensities above 8 mm day−1 in 1988 over this area;
therefore, it was impossible to compare the precipitation
ERD for this threshold. The RCM, on the other hand,
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DYNAMIC DOWNSCALING OVER SOUTH AMERICA
(a)
(b)
(c)
(d)
(e)
(f)
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Figure 8. Precipitation ERD between DJF97 and DJF88 over area 1 for (a) CPC observation, (b) GCM, and (c) RCM results. (d–f) as in
(a–c) but over area 2. Negative values are hatched.
captured the precipitation ERD at such high rainfall rates
well.
The winter precipitation ERDs, in area 2, were similar to the summer results. In general, there were a larger
number of intense rainfall events in 1988 when compared to 1997. Weaker rainfall events also played a role
on the overall JJA88 higher rainfall totals. The GCM
captured the increase in intense convective events well
but tended to overestimate the difference at very high
thresholds, which led to the larger-than-observed seasonal
rainfall difference (Figure 7(e)). The RCM exhibits positive differences for thresholds larger than 12 mm day−1 ,
compared to observation and GCM, indicating downscaling deficiency in simulating the inter-annual difference of
very strong convective events over this area (Figure 9(f)).
The above-mentioned inter-annual difference analysis indicates that, in general, the downscaling approach
improved the results over southern South America,
whereas the GCM provided better results over northern
South America. While the poor results over the northern
latitudes by the RCM can probably be explained by the
model’s deficiency in reproducing the inter-tropical convergence zone (ITCZ) rainfall and its inter-annual variability as indicated by the seasonal average precipitation
Copyright  2010 Royal Meteorological Society
results, and corroborated by the intensity-scale analyses
of precipitation SS; the RCM improvement over subtropical South America, both in terms of higher scores
and better simulation of precipitation inter-annual variability, as shown by the ERD, is thought to be directly
linked with the RCM’s more realistic representation of
the topography and land surface characteristics (De Sales
and Xue, 2006).
4.
RCM topography and precipitation simulation
The precipitation SS and ERD decompositions proved
that the dynamic downscaling improved the precipitation
simulation over the La Plata basin. It also showed that the
same was not true for the Amazon basin. The works of
Nogués-Paegle and Berbery (2000) and Marengo et al.
(2004) showed that the South American low-level jet
(SALLJ) is an important source of moisture to the La
Plata basin region. The SALLJ can be characterised
as a narrow stream that channels the moisture flux in
the lower troposphere between the Amazon and the La
Plata basins, resulting from the deflection of easterlies
winds by the Andes Mountain Range (Marengo et al.,
2004).
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(a)
(b)
(c)
(d)
(e)
(f)
Figure 9. As in Figure 8 but for the winter season.
(a)
(b)
(c)
(d)
(e)
(f)
Figure 10. Pressure-longitude cross sections of meridional moisture flux difference between DJF97 and DJF88 for (a) ERA-40, (b) GCM, and
(c) RCM along 25 ° S; and between JJA97 and JJA88 for (d) ERA-40, (e) GCM, and (f) RCM along 20 ° S. Unit: 10−2 kg m s−1 kg−1 .
De Sales and Xue (2006), using a similar downscaling
configuration, showed that the RCM produced better
simulations of strength and position of the SALLJ
Copyright  2010 Royal Meteorological Society
seasonal average when compared to the GCM’s results
alone, which led to improved simulation of seasonal
average rainfall at the SALLJ’s exit region. In this study,
Int. J. Climatol. 31: 1205–1221 (2011)
DYNAMIC DOWNSCALING OVER SOUTH AMERICA
we further explore the possible cause of this improvement
by exploring the impact of the Andes topography on the
RCM’s precipitation inter-annual differences over the La
Plata basin.
Figure 10 shows the cross sections of meridional
moisture flux inter-annual differences for both summer
and winter seasons. The moisture flux differences by The
European Centre for Medium-range Weather Forecasts
(ECMWF) reanalysis (Uppala et al., 2005), hereafter
ERA-40, agree with the CPC rainfall observation and
show an increase in the northerly flow associated with the
SALLJ for the summer and winter of 1997 (Figure 10(a)
and (d)), which contributed to more precipitation over
the La Plata basin for DJF97–98 and JJA97. A second
core in moisture flux difference appears over the Atlantic
in both seasons. The differences are larger over the
continent (>4 × 10−2 kg m s−1 kg−1 ). While the GCM
could not simulate the moisture flux differences, the RCM
simulations captured the position of the two difference
maxima well, despite producing weaker inter-annual
differences.
The GCM and RCM used in this study have different
moist physical processes, radiation, and planetary boundary layer (PBL) schemes, which control the formation of
precipitation, and thus the complete causes for the RCM’s
improvements cannot be explained solely by the topography difference. However, we can speculate that the Andes
topography played an important role in RCM’s precipitation results over the La Plata basin. To verify the impact
of Andes Mountain Range topography on the RCM’s precipitation simulation in the La Plata basin, a sensitivity
test was performed in which the topography height (Zs )
west of 60 ° W was lowered by a factor (f ) according to
following rule:
33% 3500m ≤ ZS < 4000m
(4)
f =
50% ZS ≥ 4000m
(a)
1217
Topography heights lower than 3500 m were not
changed. This rule was chosen so that the resulting topography resembled that from the GCM model. Figure 11(b)
shows the difference in topography height between lowered topography experiments (RCM-tp) and the original
RCM setup. Next, we describe the results of these experiments on summer and winter seasonal rainfall over the
continent.
Figure 11 shows the RCM-tp’s summer and winter
seasonal precipitation. Overall, the RCM-tp produced less
precipitation over western Amazon River basin, southern
and central South America than original topography for
both seasons. The dry bias over most of north-west is
still present in the RCM-tp summer results (compare
Figures 2(c) and 11(a)), which suggests that this bias is
probably not associated with the model’s topography. The
summer and winter rainfall amounts along the eastern
slopes of the central Andes are significantly reduced in
the RCM-tp results. Such a difference is more remarkable
for winter between 5 ° S and 20 ° S, where precipitation is
absent in the low-topography simulations (Figure 11(b)).
The lower Andes topography also reduced the rainfall
over south-eastern South America and over western
Amazon basin during winter. During summer, the RCMtp produced significantly weaker moisture flux over land
and over South America’s Atlantic coast than the original
topography results (Figure 12(a)). RCM-tp northerly jets
were not only weaker but also shallower than those with
original topography. Similar weaker moisture flux results
were also obtained for the RCM-tp winter simulations
(Figure 12(b)).
It should be highlighted, however, that the precipitation reduction over the Plata basin in the RCM-tp
experiments cannot be solely explained by the moisture
flux reduction associated with the lower Andean topography. Other mechanisms such as baroclinic instability and
(b)
Figure 11. RCM-tp experiment average precipitation for (a) summer and (b) winter. Unit: mm day−1 . Dashed isolines in 11(b) indicate
the topography height difference between RCM-tp and RCM (contour lines −2000, −1500, −1000, −500, and −100). Unit: m.
Copyright  2010 Royal Meteorological Society
Int. J. Climatol. 31: 1205–1221 (2011)
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(a)
Summer
(b)
Winter
Figure 12. Pressure-longitude cross section of RCM-tp meridional moisture flux for (a) summer along 25 ° S and (b) winter along 20 ° S. Unit:
10−2 kg m s−1 kg−1 .
(a)
(b)
Figure 13. Precipitation SS difference between RCM-tp and RCM for (a) summer and (b) winter over the La Plata basin.
topography-induced cyclogenesis, which are intimately
related to the topography height, could also play an
important role on the precipitation reduction at the lee
side of the Andes. The investigation of these mechanisms
is beyond the scope of this study.
To examine the impact of the Andes topography on the
RCM’s precipitation downscaling performance over the
area 1 in more detail, we compare the precipitation SS
decompositions between RCM-tp and RCM. Figure 13
shows the precipitation SS difference between RCM-tp
and RCM for summer and winter over the La Plata basin.
Summer differences indicate worsening of the scores for
rainfall events larger than 4° , especially at mid-intensity
thresholds. Very small scale features do not seem to
be strongly affected by the Andes topography height,
except for high-intensity thresholds for which the RCMtp produced higher scores.
For the winter season, RCM-tp worsened the
precipitation scores for small-scale events at high thresholds, while RCM-tp large-scale rainfall events exhibited higher scores than those with original topography
(Figure 13(b)). These results indicate that the proper
Copyright  2010 Royal Meteorological Society
representation of the Andes Mountain Range is a crucial contributor to this RCM’s improvement in the
La Plata basin. Higher Andes topography produced
stronger moisture-laden SALLJ from tropical latitudes
towards the La Plata basin, thus promoting rainfall events
there. Precipitation SS decompositions showed that a
stronger SALLJ improved the simulation of mid- to
large-scale rainfall events over the basin during summer, and improved the simulation of small-scale precipitation events during winter. This result suggests that
the SALLJ’s role as a source of moisture for the La
Plata basin’s rainfall differs significantly between seasons.
The number of rainfall events, as indicated by the
precipitation energy over the La Plata basin, was also
impacted by the lower Andes topography in RCM-tp.
Precipitation energy spectra indicated that nearly all
rainfall spatial scales exhibited a reduction in the number
of events in RCM-tp runs (not shown). The reduction
in precipitation events was not, however, homogeneous
throughout the rainfall intensity rates. Figure 14 shows
the impact of the Andean topography height on the
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DYNAMIC DOWNSCALING OVER SOUTH AMERICA
0.5
OBS
RCM
RCM-tp
0.0
0.0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
OBS
RCM
RCM-tp
Winter
0.5
(b)
Summer
0.4
(a)
0.1 0.5 1 1.5 2
3
4
6
8
10 12 15
threshold (mm/d)
0.1 0.2 0.5 1 1.5 2
3
4
6
8
10 12
threshold (mm/d)
Figure 14. Distribution of precipitation energy per intensity threshold for CPC observation, RCM, and RCM-tp results during (a) summer and
(b) winter over the La Plata basin. Unit: mm2 day−2 .
precipitation energy per intensity rate. Summer results
exhibited a reduction in the number of rainfall events for
all thresholds. The largest reduction occurred for rainfall
thresholds between 2 and 8 mm day−1 . Overall, RCM
results were more comparable to observation than RCMtp for all rainfall thresholds.
As for the winter results, RCM-tp tended to reduce
the number of weak rainfall events more than the
strong ones. The precipitation energy distribution indicates that original topography RCM runs overestimated
the number of weak rainfall thresholds over the La Plata
basin. RCM-tp results were more comparable to observation for those thresholds (Figure 14(b)). However, for
stronger rainfall (over 3 mm day−1 ), the original topography RCM generated better rainfall counts, leading to
better overall average precipitation simulation. These
results suggest that the SALLJ has a different role on
the formation of strong and weak rainfall system over
the La Plata basin during summer and winter respectively.
5.
Discussion and conclusions
The dynamic-downscaling method was used to investigate the simulation of the seasonal precipitation and
inter-annual precipitation difference over South America. The NCEP regional circulation model (RCM) ETA,
at an 80-km horizontal resolution, was nested in the T-62
NCEP GCM on a one-way nesting setup for a series of
3-month-long simulations. The austral summer (December through February) of 1988–1989 and 1997–1998 and
winter (June through August) of 1988 and 1997 were
selected for this study because these two years exhibited very large inter-annual differences in precipitation
over tropical and subtropical South America. NCEP-DOE
AMIP-II global reanalysis was used as an initial condition
for all model runs.
Copyright  2010 Royal Meteorological Society
The ISVT was used to quantitatively assess the seasonal precipitation and precipitation inter-annual difference between the two summers and winters at different spatial scales and precipitation intensities over two
areas of interest. One area was located over southern South America, while the second was centred over
north-western South America. Area 1 encloses the La
Plata basin, which is of great economic importance to
south-eastern and central South America in terms of
hydroelectricity and food production; whereas area 2 is
dominated by the Amazon basin, which is characterised
by its abundant biodiversity and crucial role on South
America’s climate.
The summer precipitation intensity-scale analysis for
the La Plata basin indicated that, in general, the RCM produced higher scores for some rainfall features with scales
mainly around 8° . The RCM’s largest improvements
were for rainfall thresholds higher than 4 mm day−1 .
Also, the RCM reduced the no-skill portion of rainfall
spectrum, indicating improvement in the simulation of
intense localised events. For winter, the RCM improvement was more widespread spectrum wise. The largest
improvements were associated with rainfall thresholds
at almost every category at 4° to 8° spatial scales. On
the other hand, over the Amazon basin, the GCM generated better results for all precipitation rates during both
summer and winter experiments, except for intense rainfall events at 4° . According to the SS intensity-scale
decomposition, the prevalent dry bias over northern South
America in the RCM runs resulted from the model’s deficiency to simulate large spatial scale weak rainfall events
in that area.
On the basis of multi-decade downscaling simulations
over South America, Seth et al. (2007) concluded that,
where large-scale SST-forced variability was strong and
GCM performed well, RCM had difficulties in improving
the large-scale precipitation. On the other hand, in regions
where local physical processes were important and GCM
Int. J. Climatol. 31: 1205–1221 (2011)
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F. DE SALES AND Y. XUE
performed less well, downscaling showed potential to
improve the GCM results. Although our study covers
only four seasons, the results presented herein generally
agree with those in Seth et al. (2007). ISVT showed that
the RCM’s largest improvements occur for spatial scales
associated with local physical processes (400 to 800 km).
On the other hand, for larger spatial scales, the RCM
overall results did not show consistent improvement
when compared with the GCM runs, expect over the La
Plata basin during winter, where RCM’s improvement
also included some large spatial scales.
In terms of inter-annual differences between two
summers (DJF97–98 and DJF88–89) and two winters
(JJA97 and JJA88), the RCM significantly improved
the precipitation difference predictions in some parts
of South America. Overall, downscaling produced better results than the GCM over eastern, north-western,
and subtropical South America for both summer and
winter. In contrast, the RCM underestimated the negative
differences for northern South America in both seasons.
The intensity-scale analysis of precipitation ERD made
evident which rainfall rates and spatial scales contributed
more to the overall rainfall inter-annual differences.
For both areas and for either season, the precipitation
differences between the wet and dry years were mainly
associated with differences in the number of intense
precipitation events. This was especially evident for the
summer season. Weaker rainfall events contributed more
to winter rainfall differences over area 1. Analogous
to the SS decomposition analysis, in general, the RCM
produced better results over southern South America by
simulating the increase in intense precipitation events in
the wet year correctly, when compared with the dry one
for both seasons.
The comparison between meridional moisture flux
cross sections across central South America indicated
that the enhanced precipitation in JJA97 and DJF97–98
in the RCM simulations over the La Plata basin was
caused by augmented moisture advection associated with
the SALLJ during those months compared to JJA88 and
DJF88–89. The moisture transport analyses corroborate
the inter-annual precipitation difference analysis, and
raised a question about the role of the Andean mountains
topography on the RCM simulations.
A sensitivity test was carried out to examine the
impact of the Andes Mountain Range on the downscaling
results. Topography elevations along the western coast of
South America were lowered to similar heights to that
generated by the GCM. In general, the lower topography
experiment (RCM-tp) produced less precipitation over
western Amazon basin, southern, and central Brazil than
the original topography simulations for both summer
and winter seasons. The dry bias over northern South
America was still present in the summer results with low
topography, which suggests that this bias is probably
not associated with the model’s topography. For both
seasons, the lower topography experiments produced
weaker and shallower SALLJ, which contributed to the
rainfall reduction in the La Plata basin.
Copyright  2010 Royal Meteorological Society
The intensity-scale distribution of precipitation SS
and precipitation energy over the La Plata basin also
exhibited strong sensitivity to the Andes topography. In
the summer, RCM-tp deteriorated the scores at mid- to
large-scale rainfall features over the area (Figure 13(a)),
while in winter it lowered the SSs for small-scale and
intense rainfall events. Furthermore, the sensitivity test
reduced the total number of rainfall events throughout
the spectrum of scales; however, such reduction was
not uniform with respect to rainfall rates. The RCM-tp
test reduced the number of mid-to-high intensity rainfall
features more during summer, but especially tended to
reduce the number of weak rainfall events during winter.
The precipitation SS and ERD decomposition analyses
presented in this paper have indicated that the ISVT can
be a valuable tool in the analysis of dynamic-downscaling
simulations, by allowing the models’ performances to
be diagnosed as a function of the spatial scale and
intensity of the rainfall events. In general, the results
showed that the downscaling can be useful in improving
the simulation of South America’s seasonal rainfall for
certain scales and intensities.
Acknowledgements
The authors would like to thank Dr Barbara Casati
for the invaluable discussions and insightful advices.
This research was sponsored by the NOAA’s grants
NA05OAR4310010, NA07OAR4310226, and NA08
OAR4310591. The model simulations were carried out
at National Center for Atmospheric Research (NCAR’s)
supercomputers.
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