Post on 29-Mar-2023
Performance of a multi-RCM ensemble for South EasternSouth America
A. F. Carril • C. G. Menendez • A. R. C. Remedio • F. Robledo • A. Sorensson • B. Tencer •
J.-P. Boulanger • M. de Castro • D. Jacob • H. Le Treut • L. Z. X. Li • O. Penalba • S. Pfeifer •
M. Rusticucci • P. Salio • P. Samuelsson • E. Sanchez • P. Zaninelli
Received: 17 May 2011 / Accepted: 16 October 2012 / Published online: 31 October 2012
� Springer-Verlag Berlin Heidelberg 2012
Abstract The ability of four regional climate models to
reproduce the present-day South American climate is
examined with emphasis on La Plata Basin. Models were
integrated for the period 1991–2000 with initial and lateral
boundary conditions from ERA-40 Reanalysis. The
ensemble sea level pressure, maximum and minimum
temperatures and precipitation are evaluated in terms of
seasonal means and extreme indices based on a percentile
approach. Dispersion among the individual models and
uncertainties when comparing the ensemble mean with
different climatologies are also discussed. The ensemble
mean is warmer than the observations in South Eastern
South America (SESA), especially for minimum winter
temperatures with errors increasing in magnitude towards
the tails of the distributions. The ensemble mean
reproduces the broad spatial pattern of precipitation, but
overestimates the convective precipitation in the tropics
and the orographic precipitation along the Andes and over
the Brazilian Highlands, and underestimates the precipita-
tion near the monsoon core region. The models overesti-
mate the number of wet days and underestimate the daily
intensity of rainfall for both seasons suggesting a pre-
mature triggering of convection. The skill of models to
simulate the intensity of convective precipitation in sum-
mer in SESA and the variability associated with heavy
precipitation events (the upper quartile daily precipitation)
is far from satisfactory. Owing to the sparseness of the
observing network, ensemble and observations uncertain-
ties in seasonal means are comparable for some regions and
seasons.
A. F. Carril (&) � C. G. Menendez � A. Sorensson � P. Salio �P. Zaninelli
Centro de Investigaciones del Mar y la Atmosfera (CIMA),
CONICET-UBA, Ciudad Universitaria, Ciudad Autonoma
de Buenos Aires, Int. Guiraldes 2160, Pabellon 2, Piso 2,
C1428EGA Buenos Aires, Argentina
e-mail: carril@cima.fcen.uba.ar
A. F. Carril � C. G. Menendez � F. Robledo � B. Tencer �O. Penalba � M. Rusticucci � P. Salio
Departamento de Ciencias de la Atmosfera y los Oceanos
(DCAO), FCEN, Universidad de Buenos Aires,
Buenos Aires, Argentina
A. F. Carril � C. G. Menendez � A. Sorensson � O. Penalba �M. Rusticucci � P. Salio � P. Zaninelli
UMI IFAECI/CNRS, Buenos Aires, Argentina
A. R. C. Remedio � D. Jacob � S. Pfeifer
Max Planck Institute for Meteorology (MPI-M),
Hamburg, Germany
J.-P. Boulanger
LOCEAN, UMR CNRS/IRD/UPMC, Paris, France
M. de Castro � E. Sanchez
Universidad de Castilla-La Mancha (UCLM),
Toledo, Spain
H. Le Treut � L. Z. X. Li
Laboratoire de Meteorologie Dynamique (LMD), Institut-Pierre-
Simon-Laplace et Ecole Doctorale, Sciences de l’Environnement
en Ile de France, Paris, France
P. Samuelsson
Swedish Meteorological and Hydrological Institute (SMHI),
Norrkoping, Sweden
123
Clim Dyn (2012) 39:2747–2768
DOI 10.1007/s00382-012-1573-z
Keywords Regional climate models � Multi-RCM
ensemble validation � South Eastern South America �South America
1 Introduction
In the last Intergovernmental Panel on Climate Change
(IPCC) Report, global coupled climate models are gener-
ally able to capture the main features of the large scale
circulation (Randall et al. 2007), but their performance
deteriorates at regional scales (e.g., Vera et al. 2006;
Christensen et al. 2007a; Silvestri and Vera 2008; Vera and
Silvestri 2009; Rusticucci et al. 2010; Marengo et al.
2010a). In particular, the South American continent needs
to be resolved with the highest resolution possible due to
the influence by a number of local and regional atmo-
spheric phenomena and circulations such as mesoscale
convective systems (Velasco and Fritsch 1987), the South
American low level jet (Vernekar et al. 2003), and the
Chaco Low (Seluchi and Marengo 2000).
Global circulation models (GCMs) perform numerical
simulations at coarse horizontal resolution up to around
100 km (Meehl et al. 2007). On one hand, this resolution
limits the representation of the local forcings. Both the
complex physiographic features of South America and the
explicit simulation of the small scale processes, which
impact on the representation of the heat and momentum
fluxes, are poorly simulated at a typical GCM resolution.
The complex topography includes the narrow and sharp
Andes Mountains along the entire west coast and the
Brazilian plateau. On the other hand, model resolution
determines the nature and the extent to which the param-
eterizations are needed. However, due to high computa-
tional cost, climate integrations of GCMs at higher
horizontal resolution are still not quite feasible.
Regional climate models (RCMs) are complementary
tools that allow detailed simulations of regional and local
processes. The essential principles, potential and limita-
tions of RCMs are summarized in Rummukainen (2010).
RCMs integrated at horizontal resolution of about
20–50 km is expected to have the ability to generate added
value especially in regions where local processes dominate
(e.g., Rocha et al. 2009; Seth et al. 2007). Several research
groups have been working with RCMs over South Amer-
ica. A number of studies are for short-term integrations
ranging from several months of continuous simulations,
monthly reinitialized simulations, case studies, idealized
experiments and sensitivity to the model physics (e.g.,
Figueroa et al. 1995; Berbery and Collini 2000; Menendez
et al. 2001; Chou et al. 2002; Nicolini et al. 2002; Misra
et al. 2003; Seth and Rojas 2003; Rojas and Seth 2003;
Seth et al. 2004; Fernandez et al. 2006; Solman and
Pessacg 2011; Sorensson and Menendez 2011). Over the
years, the approach for dynamical downscaling has been
changed from reinitialization into continuous long-term
experiments i.e., from decadal to multi-decadal (e.g., Sol-
man et al. 2008; Silvestri et al. 2009; Nunez et al. 2009;
Marengo et al. 2009, 2011; Alves and Marengo 2010;
Pesquero et al. 2010; Sorensson et al. 2010; Chou et al.
2011). Recently, an example of dynamical downscaling
over South America using a hydrostatic atmospheric global
model (AGCM) integrated at a very high resolution of
about 20 km has been documented by Kitoh et al. (2011).
Transferability studies highlight the difficulties in sim-
ulating the regional climate over South America. Results by
Meinke et al. (2007) and Rockel and Geyer (2008) suggest
that the largest precipitation biases in RCMs are found
within the South American domain. Dynamic downscaling
experiments are expected to have a greater skill if a multi-
model ensemble approach is used (Palmer and Shukla
2000). The first coordinated regional climate experiment
over South America was documented by Roads et al.
(2003). They analyzed an ensemble of four RCMs driven by
the NCEP/NCAR I global reanalysis and concluded that
regional models did not provide significant improvement
over large-scale analyses. A second effort was made in the
framework of CREAS regional program (Marengo et al.
2010b) in which three RCMs (Eta CCS, RegCM3 and
HadRM3P) were nested within the HadAM3P global
model, to simulate regional present and future climate.
Nowadays, a new set of coordinated RCM simulations over
South America is available in the framework of CLARIS-
EU, A Europe South America Network for Climate Change
Assessment and Impact Studies Project (Boulanger et al.
2010). First initiatives analyzing these integrations were
reported in Menendez et al. (2010a, b). They have evaluated
the capability of a multi-model ensemble to reproduce
3 month-long case-studies (January 1971, November 1986
and July 1996) characterized by anomalous climate condi-
tions in the southern La Plata Basin (LPB), emphasizing the
representation of the precipitation.
In view of the growing need of regional climate pro-
jections, other various international projects have produced
coordinated dynamical downscaling experiments for
assessing climate variability, climate change, impacts and
vulnerability focusing on diverse regional domains: PRU-
DENCE and ENSEMBLES from Europe (pru-
dence.dmi.dk, Christensen et al. 2007b, Gao et al. 2006,
ensembles-eu.metoffice.com, Hewitt 2005); NARCCAP
from North America (www.narccap.ucar.edu, Mearns et al.
2009); ARCMIP from the Arctic region (curry.eas.ga-
tech.edu/ARCMIP/, Curry and Lynch 2002); RMIP from
Asia (Fu et al. 2005); and WAMME from Africa (wam-
me.geog.ucla.edu, Xue et al. 2010, Druyan et al. 2010). At
present the European Commission is funding the second
2748 A. F. Carril et al.
123
stage of CLARIS for La Plata Basin (EU FP7 CLARIS-
LPB, www.claris-eu.org), and complementary coordinated
experiments for studies of climate change over South
America have been performed.
The objective of this paper is to document the ability of
the CLARIS models and the multi-model ensemble mean
to simulate the present climate, which are integrations done
with so-called perfect boundary conditions (Christensen
et al. 1997) with emphasis on the representation of the
surface fields over Southeastern South America. The multi-
model multi-year simulation presented in this paper was
briefly introduced by Menendez et al. (2010b). As society
urgently needs reliable climate change information to
develop adaptation strategies, this work aims to contribute
to the development of a high quality regional system for
climate simulations. The data and methods used in the
coordinated experiment data are described in Sect. 2.
Sections 3 and 4 assess the models in terms of the mean
conditions of temperature (maximum and minimum) and
precipitation as well as their capability to represent indices
of extremes based on a percentile approach. In particular,
we discuss the spread among the individual models, the
added value of considering a multi-model ensemble and the
uncertainties. Section 5 concludes the study.
2 Models, data and methodology
The different models used in this comparison are briefly
summarized in Table 1. The models are abbreviated as
follows: LMDZ (Hourdin et al. 2006), PROMES (Castro
et al. 1993), RCA3 (Kjellstrom et al. 2005) and REMO
(Jacob 2001). The models calculate the primitive equation
at each grid points. These models have similar horizontal
resolution but each model uses its own numerical methods,
parameterizations and vertical resolution (see details in
Table 1). The ensemble of models (ENS) is composed of 3
RCMs (PROMES, RCA3, and REMO) and one stretched-
grid global model (LMDZ), which is used in its regional
configuration as in Zou et al. (2010) and Chen et al. (2011).
The coordinated approach is configured in terms of similar
domain, horizontal resolution and boundary conditions.
Figure 1 shows the model domain covering most of the
South American continent and surrounding oceans. The
Table 1 Models, grid configuration and main parameterizations
LMDZ PROMES RCA3 REMO
Institute Laboratoire de
Meteorologie
Dynamique, France
Universidad de Castilla La
Mancha, Spain
Swedish Meteorological and
Hydrological Institute,
Sweden
Max Planck Institute,
Germany
Reference Li (1999), Hourdin et al.
(2006)
Castro et al. (1993) Samuelsson et al. 2011 Jacob (2001)
Projection Lat/Lon Lambert conformal Rotated pole Rotated pole
Grid resolution 0.5� to 0.7� 50 km 0.5� 0.5�Grid
(Lat 9 Lon)
100 9 97 139 9 145 155 9 134 121 9 145
Vertical
coordinate
Hybrid Pressured-based sigma Hybrid Hybrid
Vertical levels 19 28 24 31
Advection Finite-volume scheme Cubic splines ? correction
(Bermejo and Staniforth
1992)
Semi-Lagrangian Semi-Lagrangian
Convective
scheme
Emanuel (1993) Kain and Fritsch (1993) Kain and Fritsch (1993) Tiedtke (1989), with
modifications after Nordeng
(1994)
Cloud
microphysics
scheme
Bony and Emanuel
(2001)
Hsie et al. (1984) Rasch and Kristjansson (1998) Sundquist (1978)
Radiation
scheme
Morcrette (1991) Stephens (1978), Garand
(1983)
Savijarvi (1990), Sass et al.
(1994)
Morcrette et al. (1986),
Giorgetta and Wild (1995)
Land surface
scheme
Krinner et al. (2005) Ducoudre et al. (1993) Samuelsson et al. (2006) Dumenil and Todini (1992)
Soil thermal
layers
11 7 5 5
Soil moisture
layers
2 2 2 1
Performance of a multi-RCM ensemble 2749
123
models are driven every 6-h by the large scale boundary
conditions from the European Centre for Medium Range
Weather Forecasts (ECMWF) 40-year Reanalysis or ERA-
40 (Uppala et al. 2005). The simulation period is integrated
with a horizontal resolution of about 50 km that includes
the period from 1991 to 2000. LMDZ is relaxed towards
the driven data (ERA-40) in the global grid outside South
America. Each simulation considers at least a period of
365-day as spin-up to allow stabilization of soil variables
from the land-surface model (Christensen 1999).
A new Southeastern South America (SESA) daily
gridded dataset of minimum and maximum surface tem-
perature (Tencer et al. 2011) is used to evaluate the model
simulations. The Tencer dataset has been compiled within
the framework of the CLARIS LPB project. This daily
dataset has a high spatial resolution of 0.5� and covers the
region 70�W–45�W and 40�S–20�S from 1961 to 2000. It
is based on daily temperature series from 265 meteoro-
logical stations distributed over Argentina, Brazil, Para-
guay and Uruguay. A kriging method is used during
interpolation, which is described further in Tencer et al.
(2011) and Haylock et al. (2008). The simulated seasonal
mean and extreme values of the daily maximum and
minimum temperature (hereafter TX and TN, respectively)
are evaluated using this recent dataset. Analysis is per-
formed for warm and cold season during December to
February (DJF) and June to August (JJA), respectively. The
extremes are calculated using the percentiles to define
extremes of diverse severity. Warm extremes are defined as
the daily TX exceeds the 75th-, 90th- and 95th-percentiles
(P75, P90 and P95, respectively) and cold extremes are
when the daily TN is lower than the 25th-, 10th- and 5th-
percentiles (P25, P10 and P5, respectively). Seasonal mean
maps and Taylor diagrams (Taylor 2001) are used to
summarize the ensemble mean performance and the
intermodel spread.
The evaluation of simulated mean precipitation over the
period 1991–2000 are compared with datasets from the
Climatic Research Unit (CRU) Version TS 2.0 (New et al.
1999, 2000) and the Climate Prediction Center’s Merged
Analysis of Precipitation or CMAP (Xie and Arkin 1997).
CRU is a gridded monthly dataset that includes precipita-
tion over land on a 0.5� 9 0.5� global grid from 1901 to
2001. CMAP is an analysis that merges information from
three sources: gauge-based monthly analysis, estimates
from satellite observations and precipitation forecast from
NCEP–NCAR reanalysis (Kalnay et al. 1996) produced by
assimilating quality controlled observations. CMAP is
available at a 1� 9 1� grid spanning the period from 1979
to 2002. We designate the CMAP dataset as ‘‘primary’’ or
reference dataset and CRU as ‘‘secondary’’ source. How-
ever, it is evident that verifying models with different
datasets would lead to different results. Unfortunately,
rigorous estimations of uncertainties in climate observa-
tions are difficult to obtain. The best available handle on
observational uncertainty is often a comparison of alter-
native datasets (as suggested in Covey et al. 2002). This
simple approach can provide a crude but useful measure of
observational uncertainty to put into context the magnitude
of the errors of the models and of the ensemble, which
means ideally models errors should be similar to or lower
than the observational uncertainty.
An additional high resolution dataset from the Tropical
Rainfall Monitoring Mission (TRMM) Multi-satellite Pre-
cipitation Analysis or 3B42 (Huffman et al. 2007) is used
for the 3-year period 1998–2000. This additional data
allows for further exploration of the observational uncer-
tainty and models’ biases. The TRMM 3B42 database
consists of simultaneous observations from the Precipita-
tion Radar, Microwave Imager, and Lightning Imaging
Sensor aboard the satellite. The 3-hourly TRMM precipi-
tation record is available in a 0.25� 9 0.25� regular grid,
starting in the late 1997s and given reliable values equa-
torward of about 40� where latitude is a limitation for some
sensors.
The evaluation of daily extremes in precipitation has
been limited by the lack of long-term reliable gridded daily
observations. Alternatively, RCMs are compared against
gauge observations. Figure 2 shows the precipitation data
from station points distributed all across La Plata Basin and
Southern South America bounded by 70�W–40�W and
40�S–15�S. The spatial distribution of rain gauges with less
than 10 % of missing data in the reference period corre-
sponds to 81 stations in Argentina, 4 in Uruguay, 201 in
Brazil, 9 in Bolivia and 5 in Paraguay. Substantial
Fig. 1 Domain and tospography (m) of the regional experiments
2750 A. F. Carril et al.
123
differences between the RCMs grid box means and the
station data can be found as a consequence of the subgrid
variability (Menendez et al. 2010a). Also, Gober et al.
(2008) emphasizes the need of comparing numerical
models against ‘‘up-scaled’’ observations by averaging
observations up to the model scale.
We use the following approach for evaluating precipi-
tation. First, the multi-model ensemble mean, TRMM and
CRU data are compared with the reference climatology
(CMAP). Biases and uncertainties of seasonal means are
discussed. Furthermore, a subset of TRMM (1998–2000) is
used to evaluate the model ensemble mean, intensity and
frequency of precipitation during summer/winter. Although
the period of verification is short, the analysis will give an
insight on the daily variability of precipitation. Second,
seasonal precipitation over sub-regions (Fig. 2) is charac-
terized using a series of box-plots. A heavy precipitation
event is defined when daily rainfall is greater than the 75th
percentile (P75). In each sub-region, the P75 for every grid
point (rain gauge station) are estimated for the entire
simulation period. For example during the summertime
series (about 810 days), percentiles were estimated only
from the subsample of rainy days. For each sub-region with
N grid points and M gauge stations, box plots illustrates the
statistics of the series made by the N(M) P75 elements
estimated for every grid point (gauge station). The same
analysis is repeated for the 3-year period, which the model
integrations and TRMM data overlaps.
3 Evaluation of the mean climate conditions
In this section, we analyze the capability of the ensemble
mean to capture selected key features of the mean sea level
pressure, surface temperature and precipitation fields. The
spread among the members of the ensemble and the
uncertainty related with the lack of reliable observations
are also discussed.
Figure 3 shows the seasonal mean sea level pressure for
austral summer and winter from the ERA-40 reanalysis and
the ensemble biases. The observed sea level pressure over
the continent is lower than over the neighboring oceans
throughout the year, while the southern part of the domain
is characterized by a strong meridional gradient especially
in winter. These high pressure systems are the so-called
subtropical anticyclones over the South Pacific and South
Atlantic Oceans, which tend to move towards the pole
during summer. The small biases over the southernmost
oceanic area of the domain indicate that this annual cycle is
simulated well by the models. In both seasons, the
ensemble mean has a bias towards higher sea level pressure
over tropical western South America, enhancing the low-
level convergence over the western Amazon basin. A high
pressure bias occurs at the east of extratropical South
America. The subtropical high over the South Atlantic
appears higher in the ensemble mean compared to the
reanalysis. These biases suggest that the northerly low-
level jet to the east of the Andes in the subtropics is too
weak in the models, which is in accordance with Wang and
Fu (2004). This is also consistent with previous results in
Menendez et al. (2010a, b) who found that the meridional
moisture transport is underestimated by regional models.
The analysis of the sea level pressure in the individual
models (not shown) indicates that too strong zonal gradi-
ents that arise from the low bias on the tropical continent
and the high bias on the South Atlantic Ocean are more
evident in RCA3 and PROMES.
Figure 4 shows the observed mean daily maximum
(minimum) temperature for summer (winter) and the
ensemble mean biases in SESA. The observed dataset from
Tencer et al. (2011) have regions mostly located over rel-
atively flat and low terrain. In summer, high values of TX
occur inland over the northern part of the domain, which
suggest the role of the ocean as a moderator of regional
temperatures. During winter when the westerlies intensify
and the systems migrate northward, the observed temper-
ature gradient becomes north–south. RCMs are warmer
than observations with higher root mean square error
-70 -60 -50 -40
-50
-45
-40
-35
-30
-25
-20
Fig. 2 Distribution of rain gauges available for this study (blackdots) and regions (colored boxes) defined to study the extremes in
precipitation: Northwest La Plata Basin or NWLPB (pink); Northeast
LPB or NELPB (light blue); the South American Converge Zone or
SACZ (purple); Cuyo or CU (brown); Central West LPB or CWLPB
(yellow); Central East LPB or CELPB (red); and South LPB or SLPB
(green)
Performance of a multi-RCM ensemble 2751
123
(RMSE) over winter than summer (2.45 and 1.14 �C,
respectively). Generally, the ensemble mean bias for the
mean TX/TN varies from 0 to 2 �C with the exception of
central Argentina in summer and near the Andes in winter
where the ensemble performance deteriorates.
The seasonal mean precipitation for DJF and JJA from
CMAP, CRU and ENS are shown in Fig. 5. During the
wettest three-month period (DJF), CMAP show large
rainfall values over Amazonia extending southeastwards
(Fig. 5a). During austral winter (JJA), the largest seasonal
precipitation occurs in the region affected by the tropical
convection, which is mainly over western Amazonia
(Fig. 5f). SESA and the southern Andes are characterized
by secondary maxima of precipitation in winter, while
large areas over central and western South America are
dry. The density of observing stations is relatively low over
large regions in South America and therefore the interpo-
lation procedure used in developing gridded precipitation
datasets might affect the fine scale structure of the actual
field. Consequently some caveats should be pointed out
concerning current observational datasets such as CRU
(New et al. 2000). In order to estimate the intrinsic
uncertainty between observations, we compare CMAP
dataset with CRU (Fig. 5b, g). The relative difference
between these two datasets (CRU-CMAP) is shown in
Fig. 5d, i. The multi-model mean is shown in Fig. 5c, h for
DJF and JJA, respectively. ENS reproduce the major
regional characteristics of the observed precipitation field
(a) (b)
(c) (d)
Fig. 3 Seasonal mean sea level pressure for the period 1991–2000
from ERA-40 reanalysis (left column) and the ensemble mean bias
from ERA40 (right column). The units are in hPa less 1,000. Top
panels are for DJF (a, b) and bottom panels for JJA (c, d). Shades in
every column refer to the bottom color bar
2752 A. F. Carril et al.
123
including the maxima over the tropical rain forest and parts
of Brazil, and the relatively dry areas such as northeastern
Brazil and eastern Patagonia. However, ENS overestimates
precipitation in the northern part of the domain and along
the Andes, and tends to underestimate precipitation near
the core monsoon region in central South America. The
wet bias is associated with convection in low latitudes and
the forced lifting of air masses over the foothills of the
Andes including the central and southern Andes where the
westerlies move air masses towards the continent and over
the Brazilian Highlands. The dry bias could be associated
with monsoonal convection. In general, the ensemble bias
exceeds the observational bias with some exceptions for
example during DJF over SESA where the magnitude is
comparable (Fig. 5d, e).
The ensemble mean precipitation is then compared to
the high-resolution TRMM database during the
overlapping period from 1998 to 2000. Figure 6 displays
the relative differences of the 3-year seasonal mean pre-
cipitation of TRMM, CRU and ENS compared to the pri-
mary dataset CMAP. The domain of these maps is
constrained by the reliability of TRMM observation. As
stated in Knutti et al. (2008), the confidence in climate
models may be measured with the consistency of the
ensemble mean against the available data within the
observational uncertainty. The central panels (CRU-
CMAP) provide a comparison of CRU and CMAP during
this 3-year period. As in Fig. 5, the ensemble bias (ENS-
CMAP) usually exceeds the observational uncertainty
(Fig. 6b, e) but both measures have similar magnitudes for
some regions and seasons. The left panels (TRMM-CMAP)
suggest that rain data derived from satellite products
compare relatively well in summer with smaller differences
to the north of about 25�S as opposed to CRU-CMAP and
(a) (b)
(c) (d)
Fig. 4 Top panels show the mean maximum temperature for the
period 1991–2000 from Tencer et al. (2011) dataset (a) and the
ensemble mean bias (b) for DJF. Bottom panels show the mean
minimum temperature from Tencer climatology (c) and ensemble
mean bias (d) for JJA. Units are �C. Shades in panels b and d refer to
the same color bar. In all the panels, contours indicate topography
(m). Temperatures are obscured when topography is higher than
1,200 m. Period is 1991–2000
Performance of a multi-RCM ensemble 2753
123
ENS-CMAP. Comparing the left panels (TRMM-CMAP)
with the right panels (ENS-CMAP), similar features can be
found in terms of magnitude and sign of the differences e.g.
in the tropical and subtropical Andes throughout the year,
northern South America (0–10�S) in JJA, and the shores of
the Rio de la Plata in DJF. This suggests that the bias of the
ensemble may be lower in these regions if taken as refer-
ence TRMM climatology instead of CMAP.
The biases in temperature and precipitation are not
independent of each other. The model ensemble tends to
simulate too warm climate in summer over areas of SESA
corresponding to a dry bias as can be seen by comparing
Fig. 4b with Fig. 5e. This correlation suggests the occur-
rence of a drying phenomenon i.e. positive feedback of soil
moisture drying over areas of southern La Plata Basin. The
models especially in the RCA3 tend to overestimate
maximum temperature on areas with negative bias of pre-
cipitation. The drying out of the soil does not only depend
on deficient rainfall, but also on the soil properties and
physiographic factors affecting evapotranspiration and heat
fluxes in the regional models such as roughness length,
vegetation fraction, leaf area index, albedo, rooting depth.
In addition, SESA has been identified as a hotspot of strong
coupling between land and atmosphere in summer
(Sorensson and Menendez 2011). Thus, a realistic repre-
sentation of the land–atmosphere interaction would be
particularly critical for this region. Another factor to con-
sider is cloudiness as it has a radiative influence on the
surface energy balance. During summer in SESA, all
models except RCA3 underestimate the total cloud cover
(compared to CRU data, not shown). So the positive tem-
perature bias could also be induced by decreased cloudi-
ness, causing an increase in insolation reaching the land’s
surface.
Climate models are expected to simulate not only the
average rainfall (e.g. seasonal means) but also its daily
distribution. In this regard, Figs. 7 and 8 depict the geo-
graphical distribution of seasonal mean precipitation
diagnostics: the mean seasonal precipitation (panel a), the
mean daily precipitation intensity (panel b) and the number
of days with precipitation (panel c). We defined the mean
daily precipitation intensity as the total seasonal precipi-
tation divided by the number of days with precipitation
(precipitation greater than 1 mm/day). The seasonal per-
centage of days with precipitation is also defined as the
percentage of days with precipitation greater than 1 mm.
These metrics are evaluated using the TRMM dataset for
the period 1998–2000. We note that the mean daily
intensity of precipitation has a rather different geographical
pattern than mean seasonal precipitation for both seasons.
During the rainy season in DJF (Fig. 7a), the central South
America have a large number of rainy days (Fig. 7c) but
relatively weak mean intensity (Fig. 7b). Likewise, the
northern part of the domain in austral winter shows high
frequency of wet days but with a relatively low degree of
daily intensity (Fig. 8, first row panels). In contrast, the
percentage of wet days is smaller (about 10–30 % in JJA
and 20–40 % in DJF) but the daily intensity (approxi-
mately 20 mm/day for both seasons) is larger in SESA than
in other regions with larger mean seasonal values. The high
values of rainfall-per-wet-day maxima occur around the
Argentina-Paraguay border in the Chaco region in summer
and over Uruguay in winter. Different factors contribute to
the development of heavy precipitation in parts of SESA:
strong low-level wind shear, and an enhanced low-level jet
bringing moist air, with occasional midtropospheric
troughs moving over the Andes that act to lift the low-level
air and release convective instability (Zipser et al. 2006).
SESA is characterized by a large number of mesoscale
convective complexes (Velasco and Fritsch 1987; Laing
and Fritsch 1997). Frontal systems are also important in
producing intense rainfall events in this region (Silva and
Berbery 2006). In a global perspective, according to Zipser
et al. (2006), the SESA region has unusually intense storms
throughout the year.
The analyzed models tend to overestimate the number of
wet days and to underestimate the daily intensity of rainfall
for both seasons (Figs. 7, 8, second row panels). Over
SESA, the relatively good performance of ENS in simu-
lating the seasonal mean precipitation may result from the
cancellation of offsetting effects in these parameters where
light precipitation may fall too frequently. ENS fails to
capture the intensity of convective precipitation in summer
in SESA. The overestimation of the number of wet days
and the underestimation of the daily intensity of rainfall
correspond to a well-known problem in numerical models
(e.g. Menendez et al. 2010a, b for SESA). The involved
physical processes including the associated feedbacks
along with the typical related errors in models are dis-
cussed in Trenberth et al. (2003). Two major factors
involved are the premature triggering of convection and the
sub-grid parameterization of convection. Here mainly the
updrafts and downdrafts as well as their interaction with
entrainment and detrainment are insufficiently simulated.
These processes lead to weak but frequent precipitation.
Subsequent errors in models could also include associated
errors in cloudiness, surface heating, soil moisture, and
convergence or divergence patterns. This fact highlights
Fig. 5 First row panels mean precipitation (mm/day) during DJF
1991–2000: CMAP (a), CRU (b) and the ensemble mean (c). Secondrow panels CRU minus CMAP (d), bias of the ensemble mean
compared to CMAP (e), for DJF. Precipitation differences are
expressed in percentage of the seasonal mean values. Third rowpanels are the same as the first row panels but for JJA 1991–2000
(f–h). Fourth row panels are as the second row panels but for JJA
(i–j). There is one color bar for each row
b
Performance of a multi-RCM ensemble 2755
123
two points. First, the ‘‘triggers’’ as well as the life-cycles in
parameterizing convection in models must be better sim-
ulated. Second, a good agreement with the observations for
the mean climate over a given region does not necessarily
imply a good depiction of the climate variability and/or a
correct representation of involved physical processes.
We now analyze how consistent are the geographical
patterns of precipitation anomalies and daily maximum and
minimum temperature anomalies from the four individual
models and the ensemble. Figures 9 and 10 provide a sta-
tistical summary of how well the simulated patterns of
precipitation and daily maximum and minimum tempera-
ture anomalies match the reference climatology in terms of
root mean square difference (RMSD), correlation coeffi-
cients (R) and standard deviation (STD) using Taylor
diagrams (Taylor 2001). These diagrams provide a visual
framework to easily compare results between models and
gridded observations. The radial distance from the origin is
proportional to STD, the radial distance from the reference
climatology (point REF) is proportional to the RMSD and
the correlation between the single model/ensemble and
REF is given by the azimuthal position of the tested field.
The Taylor diagram for temperature/precipitation is
calculated over the SESA/full South American domain
(Figs. 9, 10). As mentioned before, CMAP and the Tencer
dataset are used as the reference data sets for precipitation
and temperature, respectively. All the statistics are calcu-
lated after removing the spatial mean value of each dataset.
In general, ENS (red point) has a good performance rela-
tive to each individual model. It is further away from the
outlier point and near to the cluster of best models. The
outliers in these diagrams are points lying far away from
the verification data (point REF). This result suggests that
combining the models in a multi-model ensemble gives in
general a good climate depiction independently of the
variable and season, which corroborates previous outcomes
(e.g. Menendez et al. 2010a, Hagedorn et al. 2005). In the
case of precipitation (Fig. 10), note that the spread between
CMAP and CRU could compromise the assessment of the
model quality. However, the models are clustered among
(a) (b) (c)
(d) (e) (f)
Fig. 6 Bias of the seasonal mean precipitation (%) for the 3-year
period 1998–2000. Left column bias of TRMM compared to CMAP;
middle column bias of CRU compared to CMAP; right column bias of
the ensemble mean compared to CMAP. Precipitation biases are
expressed in percentage of the seasonal mean values. Top (bottom)
panels are for DJF (JJA)
2756 A. F. Carril et al.
123
each other except for PROMES (green dot), which has the
largest values for STD and lowest values for R during
summer and winter.
In general, models and the ensemble mean are more
accurate in simulating the temperature anomaly patterns of
TN in winter than those of TX in summer where RCA (blue
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Fig. 7 Top panels Austral summer (DJF) mean precipitation (mm/
day) (a), mean daily intensity of precipitation (mm/day) (b), and
percentage of number of days with precipitation greater than 1 mm/
day (c) from TRMM climatology, for 1998–2000. Middle panels are
the same as the top panels but for the ensemble mean. Bottom panels
are the bias of the ensemble mean compared to TRMM to reproduce
the DJF mean precipitation (g), the intensity of precipitation (h) and
the number of days with precipitation (i), for the same 3-year period.
Bottom panels refer to the same color bar
Performance of a multi-RCM ensemble 2757
123
dot) and PROMES (green dot) behave as outliers (Fig. 9).
The spread between models for TX is larger than TN. Most
models overestimate the spatial STD of the reference
(exception is LMDZ) and ranges from 0.4 to 0.95
(0.95–0.98) for the spatial R for TX in summer (TN in
winter). RMSD is lower than 2 �C in winter but in summer
RCA and PROMES (blue and green dots) present values
greater than 3 �C. The winter climate is dominated by
synoptic conditions which increase the influence of the
lateral boundary conditions on the simulations. During
summertime, the boundary forcing is weak and thus the
climate is influenced more by the small-scale forcing and
internal variability in the regional models. In addition, the
stronger local processes (such as convection) could also
explain the higher spread between simulations and the
lower correlation between models and observations in
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Fig. 8 As Fig. 7, for Austral winter (JJA)
2758 A. F. Carril et al.
123
summer. The relevance of local scale processes for summer
and the seasonality of the internal variability in climate
models have been reported in other regions (e.g. Caya and
Biner 2004; Jacob et al. 2007; Kendon et al. 2010). These
factors add a strong element of randomness and nonlin-
earity to the models, thereby maximizing the anomaly
errors during summer (Menendez et al. 2010a).
4 Extreme indices
The SESA region is sensitive to the occurrence of extreme
events. Magrin et al. (2007) describes how climate
variability and extreme events have severely affected large
areas of South America in recent years. Natural and socio-
economic systems are particularly vulnerable to potential
changes in the frequency and intensity of extreme events
(agriculture, forestry, ecosystems, water resources, human
health, industry, urban settlements, among others). Under
these circumstances, it is of real practical interest to eval-
uate how well models can simulate extreme indices. In
what follows, we present the capability of the regional
ensemble to represent temperature and precipitation
extremes, which is defined in terms of percentiles. Tem-
perature values above/below the 75th/25th percentiles are
considered extremes in TX/TN (as e.g., in Carril et al.
2008). The simulated values for temperature are evaluated
using the Tencer dataset over the SESA region only
(Fig. 4).
Figure 11 presents the observed P75/P25 in daily TX/
TN for summer/winter (left column), the corresponding
ensemble mean biases (middle column) and the interquar-
tile range (IQR) biases (right column). The IQR is the
difference between the third and first quartiles. The IQR
0 1
2 3
4 5
6 7
8 9
0 1 2 3 4 5 6 7 8 9 101
0.99
0.95
0.9
0.8
0.70.6
0.50.40.30.20.10-0.1-0.2-0.3-0.4
-0.5-0.6
-0.7
-0.8
-0.9
-0.95
-0.99
-1
Standard Deviation
Co r r e l a t i o n(a) PP anom., DJF
REF
CRU
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
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0.30.20.10
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ndar
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evia
tion
Co
rr
el
at
io
n
(b) PP anom., JJA
REF
CRU
Fig. 10 As Fig. 9, for precipitation in DJF (a) and JJA (b). The
reference climatology (REF, black point) is CMAP, the alternative
climatological dataset is CRU (CRU black point) and domain is the
entire model domain
0
0.5 1
1.5 2
2.5 3
3.5 4
4.5 5
0
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1
1.5
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Sta
nd
ard
De
via
tion
Co
rr
el
at
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n
(a) TX anom., DJF
(b) TN anom., JJA
REF
ENS
LMD
PRRCA
RE
0 0
.5 1
1.5
2 2
.5 3
3.5
4 4
.5 5
0
0.5
1
1.5
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3
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0.30.20.10
Sta
nd
ard
De
via
tion
Co
rr
el
at
io
n
REF
Fig. 9 Statistics displaying the spread of individual models (coloredpoints PROMES in green; RCA in blue; LMDZ in orange; and
REMO in violet) and the bias of the ensemble mean (red point)compared to the reference climatology (REF, black point). Panel
a (b) is the Taylor diagram for maximum (minimum) temperature
anomaly in DJF (JJA), reference climatology is Tencer dataset and
domain is SESA for the 1991–2000 period
Performance of a multi-RCM ensemble 2759
123
bias is a measure of how models (ENS) capture the
observed range of daily variability. In comparison with
Fig. 4, the observed pattern of the P75/P25 for TX/TN
fields displays east–west/north–south gradients and the
ENS biases remain positive almost everywhere. The main
difference between the bias in extreme thresholds and bias
in mean temperatures is that the errors increase in magni-
tude as analysis moves to the tails of the distributions. The
ENS mean bias for the index P75 of daily TX varies from 0
to 4 �C. The error magnitude in the P90 of TX (P10 of TN)
increases further to 6 �C (8 �C) while the pattern of the
error remains almost invariant (figure not shown). The right
column in Fig. 11 presents a measure of the statistical
dispersion. The IQR of the ensemble mean for TX is higher
than the observed one (Fig. 11c), which suggests that the
simulation of TX presents a platykurtic distribution (i.e., a
flatter peak around its mean, which causes thin tails within
the distribution due to its large variability). However, TN
values behave somewhat differently. Although the
ensemble mean overestimates the mean values of TN in
SESA as shown in Fig. 4, the IQR northward of 35�S is
underestimated (Fig. 11f), which suggest a leptokurtic
distribution (i.e., data is highly concentrated around its
mean value).
In order to quantify the spread of the ensemble mem-
bers, Fig. 12 presents the Taylor diagrams estimated for the
indexes P75/P25 in TX/TN. In this case, we analyze how
consistent are the geographical patterns of the indices of
daily maximum and minimum temperature extremes from
the four individual models and the ensemble. The ENS (red
point) performs better than each individual model however
each model is more accurate in simulating the P25 of daily
TN during winter than daily TX during summer. Some
models have large RMSE during summer but offsetting
errors cancel each other (figures not shown). As in the
simulation of the mean TX and TN, RCA (blue) and
PROMES (green) models behave as outliers in summer.
Results are almost invariant when considering indices for
extremes at higher/lower (e.g., TX P90/TN P10)
percentiles.
(a) (b) (c)
(d) (e) (f)
Fig. 11 Top panels 75th percentile of daily maximum temperature
from Tencer et al. (2011) climatology (a), the ensemble mean bias
(b) and the ensemble mean bias to simulate the interquartile range
(c) in DJF. Bottom panels the same as the top panels but for 25th
percentile of daily minimum temperature in JJA. Units are �C
2760 A. F. Carril et al.
123
In the case of precipitation, only the surplus values
based on the index P75 is discussed. The simulated pre-
cipitation values like all other grid values should not be
considered as representing the weather conditions at the
exact location of the grid point but as a time–space average
within the grid box (Gober et al. 2008). Consequently, we
expect that heavy rainfall values (P75) from station data are
higher than those from models representing the mean
precipitation over an area determined by the models’ res-
olution. With this in mind, we verify how the individual
models and the ensemble are able to capture the range of
variability of P75 values in different regions in SESA.
For each individual model, we evaluate the upper
quartile of daily precipitation at every grid point in each
region defined in Fig. 2. The range of values representing
the highest 25 % daily precipitation is an estimate of the
variability of extreme precipitation in each model at sub-
regions. Figures 13, 14, 15 and 16 display basic statistical
information of these quantities (median, maximum and
minimum values, and the 25th and 75th quartiles). The IQR
estimates the spatial variability of heavy precipitation
events within each region. We repeat the calculation for the
observed station data. These boxplot figures compare ran-
ges of P75 grid point values with both the rain gauge and
TRMM observational datasets. We consider a simulation as
‘‘satisfactory’’ if its IQR intersects at least part of the
corresponding IQR of the observations. This criterion is
stricter than just comparing the min–max ranges.
The majority of models and the ensemble do not match
the IQR of the observed P75 values for most regions and in
both seasons (Figs. 13, 14). In general, models thoroughly
underestimate the intensity of observed strong or heavy
precipitation events. The exception is PROMES that match
at least part of the observed IQR in various situations.
However, PROMES is an outlier as shown in the Taylor
diagrams (Fig. 10) and its seasonal mean rainfall shows
significant differences from either CMAP or CRU (map not
shown). The spatial variability of the precipitation is much
higher for PROMES than for the other models and obser-
vational data (Fig. 10). This finding is also consistent with
Figs. 13 and 14 in which PROMES generally has an IQR
greater than the other models. So even though PROMES
seems to capture the intense precipitation events better than
other models in these regions, this could be a reflection of a
general noisy simulated precipitation field.
The models’ deviation from the observations is larger
for summer as more severe events have to be simulated
(e.g. CWLPB in summer), and becomes less significant for
winter especially in regions with relatively small precipi-
tation amounts (e.g. CU). Although the ensemble mean P75
values in both seasons are found outside the observed IQR
for all regions, during JJA at least part of the ensemble
range (i.e., maximum minus minimum value) often match
part of the corresponding observed range. Nevertheless, as
already mentioned, models simulate grid box means
whereas observations (station data) are point values.
Within the grid box, the precipitation amounts can assume
quantities well above or below the box mean especially in
the case of severe weather events. Thus, substantial dif-
ferences are expected between grid box means and point
values depending on the subgrid-scale variability (Men-
endez et al. 2010a).
Some studies underline the imperative to compare
models’ grid-box means against up-scaled observation (e.g.
Gober et al. 2008). However, we have compared models
0
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(b) P25 TN, JJA
REF
0
0.5 1
1.5 2
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1
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2
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0.30.20.10
Sta
ndar
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evia
tion
Co
rr
el
at
io
n
(a) P75 TX, DJF
REF
Fig. 12 Statistics displaying the spread of individual models (coloredpoints PROMES in green, RCA in blue, LMDZ in orange, REMO in
violet) and the bias of the ensemble mean (red point) compared to the
reference climatology (REF, black point). Panel a (b) is the Taylor
diagram for the 75th (25th) percentile of daily maximum (minimum)
tesmperature in DJF (JJA). Reference climatology is Tencer dataset
and domain is SESA for the 1991–2000 period
Performance of a multi-RCM ensemble 2761
123
with point observations (Figs. 13, 14) because the network
of gauges is not sufficiently dense in the region of interest.
We repeated the box plots but including the high-resolution
satellite information (TRMM) up-scaled to the models
resolution. TRMM data were interpolated from the 0.25�native resolution to the 0.5� models grid using a triangle-
Fig. 13 Box plots showing the
distribution of daily
precipitation (mm/day) in heavy
rainy days (defined as the 75th
percentile series of the daily
precipitation for every grid
point/station data, see text for
details). Every panel is for a
particular region. Every boxstands for a specific dataset (an
individual model, the ensemble
mean and the observed
climatology) and represents the
interquartile distance (IQD,
25–75th percentiles); the
median is indicated by the redline inside the box and the mean
by the stars inside box. Values
farther from the median more
than 1.5 times the IQD
(horizontal bars outside boxes)
are considered outliers (littleblue crosses). Calculations are
for DJF 1992–2000 period
2762 A. F. Carril et al.
123
based cubic interpolation (Demaria et al. 2010). As we
have only 3 years of TRMM data, we recalculated the
statistics for each model and the station observations for
the period 1998–2000. Figure 15 shows an example of the
box plots for the SLPB region in summer and winter. In
general, the underestimation of P75 rainfall amounts is
Fig. 14 As Fig. 13, for JJA
Performance of a multi-RCM ensemble 2763
123
reduced when comparing against up-scaled observations
instead of precipitation gauge measurements. However, the
simulated IQR do not fully intersect the observed IQR or at
least the observed minimum to maximum range. In contrast
to SLPB, the NWLPB region has a lesser density of rain
gauge stations (Fig. 2). The uncertainty of the observations
is then illustrated by calculating the statistics of the refer-
ence dataset (OBS3) when the observation network is less
dense (Fig. 16). Statistics of the observations in the left
panel are estimated considering a low density network
(only 10 stations) while the OBS3 in the right panel has a
denser network (60 stations) available for the 3-year per-
iod. Although the results depend on the variability of the
precipitation in the selected region, Fig. 16 evidences how
the variability can increase when finer scales are
considered.
5 Conclusions
Regional climate simulations over South American are
carried out by four regional models forced by the ERA-40
reanalysis for the period 1991–2000. The models have been
evaluated with focus on near surface minimum and maxi-
mum temperatures and precipitation. We used several
observational datasets from Tencer et al. (2011) for tem-
perature, and from CRU, CMAP, upscaled TRMM and rain
gauge observations for precipitation.
Overall, the model ensemble depicts the regional cli-
mate better in comparison to individual ensemble member.
This result is independent of the variable and the season,
stressing the benefit of combining models in a multi-model
ensemble. However, the ensemble mean tends to be too
warm over large areas of SESA with the greatest seasonal
mean biases in minimum temperature during winter and the
smallest in maximum temperature during summer. Using
the Taylor diagrams to compare the geographical patterns
of daily temperature for the individual models, the
ensemble and the Tencer climatology, results show a rel-
atively large spread between models in summer but with
offsetting errors in the ensemble. It is also noted that errors
are generally larger at the tails of the probability distribu-
tion (e.g. P25 and P75) than in the mean field, especially in
summer.
The large-scale precipitation is reproduced in the
ensemble mean but models overestimate the convective
precipitation near the equator, the orographic precipitation
along the Andes and over the Brazilian Highlands, and
underestimate the precipitation near the core monsoon
region in central South America. The multi-model
Fig. 15 As Figs. 13 and 14 for
the region S-LPB, but period of
estimations is 1998–2000. A
box for the TRMM climatology
is included
Fig. 16 As Fig. 13 for the NW-
LPB region, but period of
estimations is 1998–2000. A
box for the TRMM climatology
is included. Difference between
both panels is the density of
precipitation stations considered
to estimate the statistics of
OBS3 boxes. In the right panel(b) statistics are from a
particularly high density of rain
gauges available over the 3-year
period
2764 A. F. Carril et al.
123
ensemble overestimates the number of wet days and
underestimates the daily intensity of precipitation, sug-
gesting that the relatively good performance for seasonal
means in SESA is due to the cancellation of offsetting
errors in these parameters. This result also highlights the
importance of investigating further the role of convective
parameterizations in simulating the clouds and its life-
cycles accurately.
Large areas in South America are characterized by a
relatively high level of uncertainty in the observations as
shown in difference plots between CMAP and CRU. Even
though the ensemble bias in general exceeds the observa-
tional bias, uncertainties are of the same magnitude over
large regions of SESA. Therefore, the observational limi-
tations in South America reduce the capacity to analyze
models biases.
The range of spatial variability of heavy precipitation
events is analyzed within different subregions of southern
South America. In most cases individual models and the
ensemble systematically underestimate the intensity of
events derived from gauge data. However, the range of
variability of P75 observed values may be too sensitive to
the number and distribution of gauge stations in regions
where the stations do not adequately sample the spatial
variability of extreme values. Models are also compared
against satellite information up-scaled to the model reso-
lution. In this case, the underestimation of P75 rainfall
amounts is less pronounced. The range of values of heavy
rainfall events simulated by the models is closer to the
range derived from up-scaled satellite data than to the
range derived from gauge data.
In brief, this paper denotes that existing uncertainties for
studying extreme temperature and precipitation events
using state-of-the-art climate models participating in
CLARIS are still quite high over vast areas of South
America including La Plata Basin. In this regard, we
emphasize that in using multi-RCM ensemble mean for
impact studies, the regional biases of the individual models
must be carefully taken into account. Difficulties may arise
from the complexity of the regional feedbacks and pro-
cesses and from observational uncertainties. Under these
circumstances, new initiatives such as the ongoing
CLARIS LPB FP7 European Project (www.claris-eu.org)
or the WCRP CORDEX framework (http://wcrp.ipsl.
jussieu.fr/SF_RCD_CORDEX.html, Giorgi et al. 2009)
are imperative to coordinate international strategies for
advancing the understanding of the physical reasons behind
model errors, for improving the skill of regional models
and for promoting the linkages between climate scientists
from this region with the modeling community. In this
sense, future work will focus on the evaluation of a new
ensemble of regional models, which are currently being
integrated in the framework of the CLARIS LPB project
following CORDEX standards and capitalizing on the
lesson learned from the present downscaling exercise.
Acknowledgments The research leading to these results received
funding from the European Community’s Sixth Framework Pro-
gramme (CLARIS, Grant Agreement No 1454) and from the Euro-
pean Community’s Seventh Framework Programme (CLARIS-LPB,
Grant Agreement No 212492). The first author was partially sup-
ported by PIP 112-200801-01788 (CONICET, Argentina) and PICT
2008-00237 (FONCYT, Argentina). Thanks are also due to the
anonymous reviewers for their helpful comments, which helped us to
improve the manuscript.
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