Atmospheric and Climate Sciences, 2012, 2, 381-400
http://dx.doi.org/10.4236/acs.2012.24034 Published Online October 2012 (http://www.SciRP.org/journal/acs)
Climate Projections and Uncertainties over South America
from MRI/JMA Global Model Experiments
Josefina Blázquez1,2,3, Mario Nestor Nuñez1,2,3, Shoji Kusunoki4
1Research Centre for Sea and Atmosphere (CIMA-CONICET/FCEN-UBA), Buenos Aires, Argentina
2Department of Atmospheric and Oceanic Sciences (FCEN-UBA), Buenos Aires, Argentina
3Franco-Argentine Institute for Climate Studies and Its Impacts (UMI IFAECI/CNRS), Buenos Aires, Argentina
4Meteorological Research Institute, Tsukuba, Japan
Received March 15, 2012; revised April 20, 2012; accepted April 30, 2012
ABSTRACT
This paper analyses the climate change projected for the near and distant future in South America using MRI/JMA
(Japanese Meteorological Agency) global model simulations with resolutions of 20 and 60 km. Changes in mean cli-
mate, as well as in the annual cycles and interannual variability of temperature and precipitation are discussed. An
analysis is also made of the uncertainties of the 60 km resolution model experiments. For the near and distant future,
both, the 20 km and 60 km resolution MRI/JMA models project that temperature changes will be positive in all seasons.
The greatest values of change are over the Andes and over tropical and subtropical latitudes of the study region. In all
the subregions analysed, the 20 km model projects greater changes in the annual cycle of mean temperature than the 60
km model. Changes in summer precipitation are positive over most of the continent, except for southern Chile. Autumn
precipitation is projected to increase over northern Argentina and north-western South America and to decrease over
central Chile in winter, which might be due to the southward shift of the Pacific storm-track. The most significant posi-
tive change in Southeastern South America (SESA) is projected to occur in spring precipitation. In general, projected
changes in the annual cycle are greater in the rainy seasons of each subregion. No significant changes are expected in
the interannual variability of temperature and precipitation. La Plata basin is projected to experience increased runoff,
which would indicate that the projected rise in precipitation would have stronger effect than projected warming. The
analysis of climate projection uncertainties revealed that temperature projections are more reliable than precipitation
projections; and that uncertainty in near future simulations is greater than in simulations of the end of the century.
Keywords: Climate Projections; High Resolution Global Model; South America; Uncertainties
1. Introduction
This study presents climate change projections over
South America from MRI/JMA experiments. Recently,
[1] assessed present climate MRI/JMA simulations and
concluded that despite some deficiencies, the MRI/JMA
model represents the present-day climate of South Amer-
ica fairly well. This is why the present study analyses
future climate projections from the mentioned model.
Numerous studies were published on climate change
over South America, either using regional [2-9] or global
model simulations [10-14]. Out of the mentioned authors,
only [8] and [9] provide climate change projections for
the beginning and middle of the century, the rest study
the impact of increased greenhouse gas concentrations in
the atmosphere on the climate of the end of the 21st cen-
tury. Furthermore, in recent years, most papers have
analysed climate change projections from regional mod-
els, as regional models allow improving the spatial reso-
lution of the simulations. However, limited area models
require not only initial but also boundary conditions wh-
ich are obtained from global models. This implies that
the accuracy of regional model simulations is limited by
the accuracy of global models. Recently, several world
Centres have joined efforts to improve the resolution of
their global models. For instance, the Meteorological
Research Institute (MRI) and the Japan Meteorological
Agency (JMA) have significantly improved the spatial
resolution of their global model and achieved a resoluti-
on comparable to that of regional models (20 km and 60
km). The current computational cost of high resolution
global model simulations is high (need of using superco-
mputers), but hopefully in the next few years smaller ce-
ntres will also able to run high resolution global models.
This paper analyses climate change projections for the
near and distant future using MRI/JMA global model
experiments under a given emission scenario. In particu-
C
opyright © 2012 SciRes. ACS

J. BLÁZQUEZ ET AL.
382
lar, changes (relative to the present climate) in mean cli-
mate and temperature and precipitation interannual vari-
ability were analysed.
2. Data and Methodology
MRI/JMA atmospheric global model simulations were
used to analyse changes between present and future cli-
mate. 25 year time-slice experiments were conducted, for
the near (2015-2039) and the distant future (2075-2099),
at 20 and 60 km resolution (Table 1), i.e., one high reso-
lution model experiment (hereafter 20 km model) and 12
lower resolution experiments (hereafter 60 km model) for
each of the periods under study. The method of [15] was
used to construct the initial conditions of sea surface
temperature (SST). The method consists of overlapping
future-present changes, present observed SST variability
and future SST trends. A scheme of the method is shown
in Figure 1 of [14]. Experiments were conducted using
four SST datasets, which had been obtained from differ-
ent models: CMIP3 multi-model (SRNF, SRF, HRNF,
HRF), MK3.0 CSIRO (HRNF_csiro, HRF_csiro), MI-
ROCH3.2 (HRNF_miroc, HRF_miroc) and MRI-
CGCM3.2 (HRNF_mri, HRF_mri). In the case of ex-
periments with the 60 km model, each of the 3 members
was constructed by using different years for model ini-
tialisation (Table 1). A similar method to that of [15]
was applied to create datasets of sea ice concentration
and thickness. The future emission scenario used is SRE-
SA1B (Special Report on Emissions Scenarios, [16]).
Changes in annual cycles were calculated as the dif-
ference between areal averages of present and future
temperature and precipitation in each subregion. The
statistical significance of seasonal changes in mean cli-
mate was determined using Student’s test, with a signifi-
cance level of 10%. The standard deviation is a measure
of temperature interannual variability and the coefficient
of variation (standard deviation normalized by the mean)
is a measure of the interannual variability of precipitation.
As mentioned before, 12 simulations were conducted
with the 60 km model for each of the future periods and
only 3 simulations of the present climate. This is why
ensembles (average of the members) were generated for
the present and future periods in order to quantify climate
changes. This means that the changes projected by the 60
km model are the difference between future and present
ensembles.
3. Climate Projections
This section discusses climate change projections from
MRI/JMA model experiments and examines the changes
in the mean climate and the interannual variability of
temperature and precipitation.
3.1. Changes in Mean Climate
Figures 1-4 show the changes projected in mean tem-
perature in the near and distant future at 20 km and 60
km resolution for summer, autumn, winter and spring
respectively. For summer both models project positive
changes in both the near and distant future (Figure 1).
Values in the near future projections range from 0˚C to
1.5˚C. Projections from both models show a further in-
crease in temperature over the central Andes. Regarding
the distant future, maximum values of change are located
over the Amazon (between 2.5˚C and 3˚C) and the Andes
chain (greater than 3˚C). All those changes are statisti-
cally significant at a confidence level of 90%, except for
the 20 km model projection of the near future where sig-
nificant values are only found over northern and central
Brazil and over Peru. As in summer, the highest values in
the near future autumn projections (Figure 2) are found
over the Andes (around 1.5˚C). In particular, the 20 km
model presents a change of 0.5˚C over north-western and
central Argentina which is not statistically significant. In
this season both models also project for the end of the
21st century greater warming over tropical and subtropi-
cal latitudes of the continent, especially over the Andes
chain where values are greater than 3˚C. Changes pro-
jected by both models for the distant future are statisti-
cally significant. Near future projections for winter
(Figure 3) show positive values over the entire domain,
and minimum values, over eastern and central Argentina.
Table 1. Near and distant future simulations.
Name of the
experiment Period Initial condition
Number of
membe r s
SRNF 2015-2039Multi-model ensemble
CMIP31 1
SRF 2075-2099Multi-model ensemble
CMIP31 1
HRNF 2015-2039Multi-model ensemble
CMIP31 3
HRF 2075-2099Multi-model ensemble
CMIP31 3
HRNF_csiro 2015-2039CSIRO-MK3.0 global
model2 3
HRF_csiro 2075-2099CSIRO-MK3.0 global
model2 3
HRNF_miroch2015-2039 MIROCH3.2 (hires)
global model3 3
HRF_miroch 2075-2099MIROCH3.2 (hires)
global model3 3
HRNF_mri 2015-2039MRI- CGCM2.3.2
global model4 3
HRF_mri 2075-2099MRI- CGCM2.3.2
global model4 3
SR means “super-high resolution” (20 km) and HR “high resolution” (60
km). NF and F mean “Near Futur e” and “Future” respectively. 1Coupled
Model Intercomparison Project, third phase; 2Commonwealth Scientific and
Industrial Research Organisation, Atmospheric Research, Australia; 3Center
for Climate System Research (University of Tokyo), National Institute for
Environmental Studies, and Frontier Research Center for Global Change of
Japan Agency for Marine Earth Science and Technology, Japan; 4Meteoro-
logical Research Institute, Japan.

J. BLÁZQUEZ ET AL.
383
(c) (d)
Figure 1. Mean surface air temperature change for summer (˚C). (a) Near future minus present, 20 km. model; (b) Near fu-
ture minus present, 60 km ensemble mean model; (c) Distant future minus present 20 km model; (d) Distant future minus
present 60 km ensemble mean model. The regions that contain the blue contours indicate the 90% of confidence level.
(c) (d)
Figure 2. Same as Figure 1, but for autumn.

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(c) (d)
Figure 3. Same as Figure 1, but for winter.
(c) (d)
Figure 4. Same as Figure 1, but for spring.

J. BLÁZQUEZ ET AL.
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These changes are statistically significant. For the end of
the century, positive changes are projected throughout
the study region. The highest values (around 3˚C) are
found over central Brazil, the southern Atlantic Ocean
and the Andes. The results of [4] and [9] also project a
rise in temperature over the Andes. For spring (Figure 4),
positive changes are projected for the entire region under
study, similarly to the other seasons. In addition, the
highest values for the near future (up to 1.5˚C) are lo-
cated over the Andes and central Brazil. In most of the
study region, changes are statistically significant with a
confidence level of 90%. The highest changes projected
for the end of the century by both models are found in
tropical and subtropical latitudes (values higher than 3˚C).
At higher latitudes, values fluctuate between 1˚C and
2.5˚C. Changes for the distant future are statistically sig-
nificant in the entire domain.
In general, the patterns of change found for summer
and winter temperature in the distant future are in agree-
ment with the results of [9], although changes projected
by those authors are more intense, up to 5˚C over the
Amazon in 2100.
Changes in the annual cycle of temperature were also
analysed using MRI/JMA model simulations (Figure 5).
The domain was divided into the same subregions of the
present climate assessment [1], which followed the selec-
tion of [17] (see Figure 5 herein). The subregions are
Southeastern South America (SESA), Central Argentina
(CARG), Andes (AN), the Subtropical region (ST) and
Patagonia (PAT). The higher resolution model projects
greater positive changes for both future periods (Figure
5). A reason for this may be the smoothing of projected
changes caused by the averaging of 12 simulations in the
60 km model ensemble. There are some subregions
(SESA, ST and CARG) where the greatest changes are
projected for summer and the transition seasons, whereas
expected changes in other regions (PAT and AN) are
similar throughout the year. In particular, the north (ST)
presents a maximum change in September which agrees
with the results of [4], although they used the A2 emis-
sion scenario. In general, all changes are statistically sig-
nificant (with a confidence level of 90%), except for
some months of the near future period in CARG and
SESA (circle in Figure 5).
Projected changes in mean seasonal precipitation for
both future periods are shown in Figures 6-9. For sum-
mer in the near future, both models present positive
changes (around 0.5 mm/day) over the continent (Figure
6). Furthermore, it can be seen that the 20 km model
(Figure 6(a)) projects that the greatest changes will take
place over southern Chile and the northern Andes (1.5 - 2
mm/day). For the end of the century, both models project
changes greater than 2 mm/day in some subtropical and
tropical regions of the continent and over tropical lati-
tudes of the Atlantic Ocean. All these changes are statis-
tically significant with a confidence level of 90%. Figure
6 also shows a decrease in precipitation over southern
Chile. This result is in agreement with those of several
authors such as [4,9,13]. Over the South Atlantic Con-
vergence Zone (SACZ) and the Amazon, changes pro-
jected by the MRI/JMA model are positive. In particular,
this result is in agreement with some models used by [13],
but differs from the results of [9]. The changes projected
by both models for north-central Argentina may be due
to enhanced cyclonic circulation known as Chaco Low,
which together with the southward shift of the Atlantic
High [18,19] results in increased moisture transport to-
wards central Argentina [4,9]. In addition, [14] and [3]
found an intensification of the Low Level Jet (LLJ),
which would also explain the increased transport of
moisture into northern and central Argentina. Changes in
autumn (Figure 7) are positive over northern and central
Argentina, and over north-western Brazil; negative, over
southern and central Chile. Changes are greater in pro-
jections for the end of the 21st century, with values above
2 mm/day in central and northern Argentina, north-
western Brazil and Peru, and of -2 mm/day over southern
and central Chile. Changes are statistically significant,
especially in the 20 km model (Figure 7(c)). [13] also
obtained positive changes for north-western South
America and negative over southern and central Chile. In
winter (Figure 8), changes projected in both the near and
distant future over the continent are generally negative,
with values reaching −0.5 mm/day (result consistent with
[13]). For the end of the century, changes in southern
Chile are projected to be positive (above 2 mm/day), and
negative in central Chile (around –1.5 mm/day). Consid-
ering that the winter precipitation climatology in south-
ern and central Chile is characterized by a maximum in
this season (not shown), projections would be pointing at
an intensification of the mentioned maximum over
southern Chile and a weakening over the central part of
that country (especially in the 20 km model projections).
The mentioned pattern of change is statistically signifi-
cant only in the higher resolution projections and agrees
with the results of [9]. That pattern might be associated
with the poleward expansion of the Pacific subtropical
high and the consequent southward shift of the Pacific
Ocean storm-track [4,20]. Finally for spring (Figure 9),
near future changes projected by the 20 km model in
some regions (Misiones province, south of Paraguay and
south of Chile) are positive and greater than those pro-
jected by the 60 km ensemble model. For the end of the
21st century, both models project statistically significant
positive changes of up to 1.5 mm/day over north-eastern
Argentina in the case of the higher resolution model. On
the other hand, projected changes in southern and central
Chile are negative (around –0.5 mm/day).

J. BLÁZQUEZ ET AL.
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Figure 5. Changes in the annual cycle of mean temperature (˚C) for the subregions defined by [17]. Filled circles indicate
distant future minus present and asterisks near future minus present. Orange and maroon lines represent the 20 km model
and the light and dark green lines represent the 60 km ensemble mean model. The black circles indicate the months in which
the chang e is not stati st ically sig nificant considering a confidence level of 90%.
Central Andes (CA), Altiplano (AL), Paraguay (PA),
Southeastern Brazil (SEB), Subtropical Andes (SUA),
Cuyo (CU), La Plata Basin (LPB), Southern Brazil (SB),
Southern Pampas (SP), Southeastern Pampas (SEP),
Southern Andes (SA) and Argentinean Patagonia (AP).
These subregions were selected following [17] (Figure
10 herein). The greatest changes are expected to occur in
the rainy seasons of each subregion (see Figure 6 of [1]).
For example, negative changes are observed in SUA, and
positive in the transition seasons of LPB. Projected
changes are negligible in CA and AP where annual pre-
cipitation is low. Most changes are not statistically signi-
ficant; the few significant changes are indicated by an
arrow in Figure 10. Temperature and precipitation chan-
ges in mean climate have been discussed. In some re-
gions an increase in precipitation is projected (which

J. BLÁZQUEZ ET AL. 387
(c) (d)
Figure 6. Mean precipitation change for summer (mm/day). (a) Near future minus present, 20 km model; (b) Near future
minus present, 60 km ensemble mean model; (c) Distant future minus present 20 km model; (d) Distant future minus present
60 km ensemble mean model. The regions that contain the blue contours indicate the 90% of confidence level.
(c) (d)
Figure 7. Same as Figure 6, but for autumn.

J. BLÁZQUEZ ET AL.
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(c) (d)
Figure 8. Same as Figure 6, but for winter.
(c) (d)
Figure 9. Same as Figure 6, but for spring.

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Figure 10. Changes in the annual cycle of mean precipitation (mm/day) for the subregions defined by [17]. Orange (marron)
bars indicate near future (distant future) minus present for the 20 km model. Light (dark) green bars indicate near future
(distant future) minus present for the 60 km ensemble mean model. The arrows indicate the months in which the change is
statistically significant considering a confidence level of 90%.
would contribute to increase soil moisture or river dis-
charge), but positive changes in temperature are also ex-
pected (which would contribute to an increase in evapo-
ration). For this reason it could be interesting analysing
the water balance as the difference between precipitation
and evaporation (P-E). Positive (negative) values of P-E
indicate positive (negative) values of runoff. Figure 10
displays the changes in the annual cycle of mean pre-
cipitation projected by the 20 km model and the 60 km
ensemble in the following subregions: Figures 11-14
show the P-E field projected by the 20 km model for the
four seasons.
Projected values of P-E in the 60 km ensemble are
similar (not shown). The most noticeable changes are
projected to occur over north-eastern Argentina, Para-
guay and southern Brazil in the summer, autumn and
spring of both future periods, where positive values of
P-E are projected to increase. This will in turn enhance
runoff in the central La Plata Basin, with consequent
increase in river discharges or soil moisture. On the other
hand, the annual cycle of precipitation in the mentioned
region presents a maximum between October and April
[21], which is when positive values of P-E are projected
to grow. This result is in agreement with those of [22]
and [23], who found larger discharges of the rivers be-
longing to La Plata Basin, the first ones, using some

J. BLÁZQUEZ ET AL.
390
models of the CMIP3 Project and the latter, a hydrologi-
cal model (fed with some CMIP3 models). Furthermore,
the results of this study also coincide with those of [14],
whose MRI/JMA model projections show positive
changes in soil moisture in northern and central Argen-
tina in all the seasons, but spring. [9] also obtained posi-
tive values of P-E for the future over South-eastern South
America (SESA). The results of the MRI/JMA model
and those of the authors mentioned previously allow
concluding that the effect of increased precipitation is
(a) (b) (c)
Figure 11. Precipitation minus evaporation (P-E) for summer (mm/day). 20 km model. a) Present; b) Near future; c) Distant
future.
(a) (b) (c)
Figure 12. Same as Figure 11, but for autumn.
(a) (b) (c)
Figure 13. Same as Figure 11, but for winter.

J. BLÁZQUEZ ET AL. 391
(a) (b) (c)
Figura 14. Same as Figure 11, but for spring.
greater than the effect of warming over some regions of
La Plata Basin. This would indicate that soil moisture or
river discharge is likely to rise over the mentioned area.
3.2. Changes in Interannual Variability
The analysis of changes in the interannual variability of
temperature and precipitation over South America is ba-
sed on the same subregions used in the previous sections.
Figure 15 displays the temperature standard deviation
for the present, and the near and distant future for both
experiments of the MRI/JMA model. The 20 km model
(60 km ensemble) projected smaller (greater) values of
temperature interannual variability for all the seasons and
subregions. The figure also shows that in general no ma-
jor changes occur in year-to-year variability, except for
the 20 km model projection for summer and autumn,
where—as was mentioned previously—future variability
is projected to be smaller. Figure 16 shows the precipita-
tion coefficient of variation (standard deviation normal-
ized by the mean) for the present, and near and distant
future at both resolutions. Values of interannual variabil-
ity projected by the 60 km ensemble are clearly greater in
the future periods in all the subregions. In contrast, val-
ues obtained from the 20 km model are either higher or
lower depending on the season and the subregion con-
sidered.
In general, no major changes in interannual variability
of temperature and precipitation are projected. A reason
for this may be the way initial conditions (SST and sea
ice) for the future model runs were constructed, since the
method applied includes the present climate variability to
initialise future experiments. On the other hand, it is
widely known that climate variability in many parts of
the study area is largely determined by the variability of
SST in the tropical equatorial Pacific Ocean caused by
the ENSO phenomenon (El Niño-Southern Oscillation)
([24-26]; among others). This could explain the small
change in precipitation and temperature interannual
variability that the MRI/JMA model projects for some
parts of South America.
4. Analysis of Uncertainties
It is widely known that climate models are important
tools to study future climate change. Equally known is
that model projections generate various types of uncer-
tainties. This is because simulations are not perfect, ei-
ther due to the model considered, the initial and/or
boundary conditions, the physics that models use or the
emissions scenarios considered in the projections. That is
why it is crucial to analyse the level of uncertainty of
climate change projections.
As mentioned in Section 2, the 60 km ensemble has 12
members, which were constructed using different initial
conditions (different SST databases or initialisation
years). Although members are not entirely independent
from each other (because simulations were run using the
same model or SST database, see Table 1), the agree-
ment or disagreement between them provides a sense of
the uncertainty level of this model’s future climate simu-
lations. To quantify initial condition uncertainties, the
number of model members projecting positive and nega-
tive changes over South America and adjacent Oceans
was determined. Since precipitation projections are the
most uncertain, the analysis of uncertainties in tempera-
ture projections will not be shown here. When consider-
ing the number of members that projected positive or
negative values of change, there was agreement in all
MRI/JMA model members: changes over the study re-
gion will be positive (not shown). In this case, changes
between future and present were calculated as the differ-
ence between each future simulation and the present cli-
mate ensemble (3 members).

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Figure 15. Seasonal standard deviation of surface air temperature (˚C) averaged over the subregions defined [17]. 20 km
model (red) and 60 km ensemble mean model (green). The squares indicate present, circles near future and triangles distant
future simulations.
Figure 16. Seasonal coefficient of variaton of precipitation averaged over the subregions defined [17]. 20 km model (red) and
60 km ensem b le m e an model (green). The squares indicate present, circles near fu t ur e a nd t ri an g le s di st a nt fut u re s im u la t ion s.

J. BLÁZQUEZ ET AL. 393
Figure 17 shows the number of members of the 60 km
model projecting positive (Figures 17(a) and (b)) and
negative changes (Figures 17(c) and (d)) for summer
precipitation in the near (left panel) and distant future
(right panel). Most members projected increased precipi-
tation over the continent, except over Chile -where the
opposite occurs. In general, this pattern repeats in both
future periods. [13] achieved the same result using some
CMIP3 models. Projections are positive over the Atlantic
Ocean, especially at high latitudes and negative over Pa-
cific subtropical latitudes. Figure 18 displays the number
of members projecting positive and negative precipita-
tion changes for autumn. The autumn pattern is similar to
that of summer, over both the continent and the oceans.
On the one hand, there is agreement among members in
projecting future precipitation increase over most of
South America (except some regions of Brazil, Chile and
southern Argentina), and on the other, a decrease in pre-
cipitation over the subtropical Pacific and increases in
several areas of the Atlantic Ocean. It is worth noting
that there is a certain degree of uncertainty in the projec-
tions by various members of the model for Buenos Aires
province in the near future.
Some members project an increase in precipitation and
others, a decrease over the mentioned region (Figure 18).
Figure 19 shows the number of members of the 60 km
model projecting positive and negative precipitation
changes for the winter. Contrary to what happens in pre-
vious seasons, in this case negative changes are projected
for most of the continent. Although agreement among
members of the model is not so high in near future pro-
jections, projections for the end of the century by most of
the members clearly show negative changes for north-
eastern and central Argentina (except Misiones province),
central Brazil and Chile. However, there are some re-
gions such as SESA, where most members projected
positive precipitation changes in winter, especially in the
distant future period. It is worth mentioning that there is
a maximum in the present climatology of precipitation
over this region of South America, so this result would
indicate that precipitation would increase over SESA in
the future. According to [27] the mentioned maximum is
associated with synoptic scale activity, so the previous
result indicates greater frequency and/or intensity of
frontal systems. Interestingly, Figure 19 shows some
areas of uncertainty in near future projections (north-
eastern Argentina, Uruguay, north-eastern Brazil) be-
cause some model members projected and increase in
(a) (b)
(c) (d)
Figure 17. Number of members of 60 km model projecting positive changes (top panel) and negative (bottom pane l) in sum-
mer precipitation. (a) and (c) Near future, (b) and (d) distant future.

J. BLÁZQUEZ ET AL.
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(a) (b)
(c) (d)
Figure 18. Same as Figure 17, but for autumn.
(a) (b)
(c) (d)
Figure 19. Same as Figure 17, but for winter.

J. BLÁZQUEZ ET AL.
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395
precipitation and others a decrease. Finally, Figure 20
displays the number of model members projecting posi-
tive or negative changes in precipitation for the spring.
Most members projected a decrease (increase) in pre-
cipitation in the south (north) of the continent in the near
future. For the end of the century, there is agreement
among all members of the model in projecting negative
(positive) values in southern and central-eastern Argen-
tina, Chile and the subtropical Pacific Ocean (SESA,
SACZ and southern oceans). [13] also found that most
CMIP3 models projected negative changes over southern
South America.
To continue with the analysis of the uncertainties as-
sociated with future climate projections, the signal to
noise ratio was calculated using the following equation:
x

SR (1) 

where Δx is the signal, defined as the difference between
the future (average of the 12 members) and the present
(average of the 3 members) simulations and σ is the
standard deviation among the 60 km model members. If
this ratio is greater than 1 then the signal of climate
change is greater than the variability among model
members, coinciding with areas of low uncertainty. Fig-
ure 21 shows the signal to noise ratio for temperature
changes for all the seasons and for the near and distant
future. The signal is greater than the noise over the con-
tinent in all seasons. Furthermore, the signal is statisti-
cally significant with a confidence level of 90%, except
in near future projections for winter over north-eastern
Argentina. Values of SR are higher for the end of the
century. Figure 22 displays the SR of precipitation for
all the seasons and both future periods. As expected, SR
values are higher in the distant future. This is because the
signal of climate change is greater at the end of the cen-
tury. Contrary to what happens with temperature, the
signal over most of the study domain is not statistically
significant, especially in the near future. For the end of
the century, the signal over some regions of the continent
is larger than the noise: central Brazil and northern Ar-
gentina in summer, northern Argentina, Peru and north-
western Brazil during the autumn and winter, over SESA
in spring. The high latitudes of the Atlantic and Pacific
oceans as well as the central Pacific Ocean also have SR
values greater than 1 in all the seasons.
(b)
(a)
(c) (d)
Figure 20. Same as Figure 17, but for spring.

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396
(g) (h)
Figure 21. Signal to noise ratio for surface air temperature for the near future (left panel) and for the distant future (right
panel). (a) and (b) summer, (c) and (d) autumn, (e) and (f) winter and (g) and (h) spring. The shaded areas are statistically
significant at a confidence level of 90%.

J. BLÁZQUEZ ET AL. 397
(g) (h)
Figure 22. Signal to noise ratio precipitation for the near future (left panel) and for the distant future (right panel). (a) and (b)
summer, (c) and (d) autumn, (e) and (f) winter and (g) and (h) spring. The regions that contain the blue contours indicate the
90% of confidence level.

J. BLÁZQUEZ ET AL.
398
The above allows concluding that uncertainty in pro-
jected temperature changes is less than in precipitation
change projections. Indeed, all members of the 60 km
model agree in projecting temperature rise in the future
in the entire domain under study. Furthermore, projec-
tions of the near future have more uncertainty because
the climate change signal is not as clear as it is in the end
of the 21st century.
5. Conclusions
This paper discusses future climate projections over
South America. Changes were analysed in mean climate
and in interannual variability of temperature and precipi-
tation. Uncertainties in 60 km model projections were
also analysed.
Regarding temperature, positive changes were found
(for both the near and distant future) for all the seasons in
both models. The biggest changes were projected for
tropical and subtropical latitudes. In all seasons, tem-
perature is projected to rise up to 3˚C in the Andes, and
between 2.5˚C and 3˚C in the Amazon in the end of the
21st century. The temperature rise for the winter over the
Andes was also documented by [4] and [9]. Most pro-
jected changes in mean seasonal temperature were statis-
tically significant. The annual cycle of temperature re-
flected the seasonal mean, with positive values of change
in all the subregions analysed. In addition, the 20 km
model presented higher values of change than the 60 km
ensemble. A reason for this may be that the average of
the members of the 60 km model ensemble tends to
smooth the changes in the annual cycle.
Projected precipitation changes are positive or nega-
tive depending on the season and the region analysed. In
summer, the changes projected by both models were
positive in most of the continent, with the highest values
(around 2 mm/day) projected over the Amazon, the
northern Andes, the SACZ, Paraguay and northern Ar-
gentina in the end of the century. Negative values were
found over southern Chile. The positive change projected
for northern Argentina may be associated with the
southward shift of the Atlantic high and the associated
intensification of the LLJ and the Chaco low. Changes in
these circulation patterns lead to an increase in the mois-
ture transport from the Amazon to northern Argentina
[4,9,14], which would explain the increase in summer
precipitation over that region. In autumn, southern and
central Chile presented negative values (up to –2 mm/day)
and the highest positive values were found in the end of
the century over the northwest of the continent and
northern Argentina (1 - 2 mm/day).
Regarding changes in winter precipitation, the highest
values were found in southern and central Chile. The
higher resolution model projected positive changes in the
south of Chile (above 2 mm/day) and negative in central
Chile (up to –1.5 mm/day), while only the negative
changes for central Chile were projected by the 60 km
ensemble. This indicates a decrease in precipitation over
the north of the area of the current climatological maxi-
mum and an increase in the south. This result is in
agreement with those of [4] and might be explained by
the southward shift of the Pacific Ocean storm-track. The
rest of the continent presents small negative values of
change (–0.5 - –1 mm/day). For spring, models project
significant positive changes over SESA in the distant
future (greater than 1.5 mm/day in the 20 km model pro-
jections). Major changes are expected in the annual cycle
of precipitation in the rainy season of each subregion,
with negative changes over SUA in winter and positive
changes over LPB in transition seasons. In general
changes in the annual cycle of precipitation were not
statistically significant. In the La Plata Basin region, an
increase in the values of P-E is projected for the warm
season (which coincides with the rainy season of the re-
gion). This increase in P-E will enhance runoff (river
discharge and/or soil moisture), which may have effects
on human activities given the relevance of this region in
terms of economic and population development.
Regarding interannual variability of temperature, the
20 km model (60 km ensemble) projected a decrease
(increase) in variability in all the seasons and subregions.
However, for precipitation, the 60 km ensemble pro-
jected an increase in year-to-year variability in all the
subregions considered and the 20 km model projected
higher or lower values depending on the season and the
subregion analysed. It should be noted that in general not
great changes in values of interannual variability of both
variables were observed in the MRI/JMA model projec-
tions. This behaviour could be attributed to the method
used to obtain SST datasets, which takes into account the
present climate variability to initialise future simulations.
The uncertainties associated to the initial conditions of
the 60 km model were also assessed in this study. For
summer, most members projected an increase in precipi-
tation over the continent, except over Chile in the end of
the century. For the South Atlantic Ocean most model
members projected positive changes, while at subtropical
latitudes of the Pacific Ocean projected changes were
negative. In autumn, the areas with less uncertainty were
found over most of South America (positive changes in
precipitation across the continent except Chile, part of
Brazil and southern Argentina); on the Pacific subtropi-
cal (negative changes); south and north Atlantic (positive
changes). In winter, most members of the ensemble pro-
jected negative changes in precipitation for the end of the
century, except for SESA, where most model simulations
projected positive changes. In spring, regions with less
uncertainty in near future projections were located over
north-central and southern Argentina, Chile and mid-

J. BLÁZQUEZ ET AL. 399
latitude of the Pacific Ocean (negative values of change)
and SESA, central Brazil and southern Atlantic and Pa-
cific oceans (positive values of change).
The analysis of the signal to noise ratio of mean tem-
perature showed low uncertainty in the projections for
both the near and distant future, since most of the SR
values were greater than 1 and statistically significant.
Precipitation showed the opposite behaviour. Few re-
gions were identified where the signal was statistically
significant and greater than the noise in the end of the
century: central Brazil and northern Argentina in summer,
northern Argentina, Peru and north-western Brazil in
autumn and winter, over SESA in spring, the southern
oceans and central Pacific Ocean in all seasons. These
results support two claims. On one hand, projections of
temperature changes presented less uncertainty than
those of precipitation and on the other; the greatest un-
certainty was found in the near future, where the signal
of climate change was not as obvious as at the end of the
century. This result indicates that the internal variability
of the atmosphere is equally important as the climate
change signal in the first decades of the 21st century and
this leads to an increase in the uncertainty of projections.
6. Acknowledgements
The authors thank the Meteorological Research Institute
for providing the model data. This study was partially
supported by UBACYT-1028, PIP CONICET 112-
200801-00195 and CLARIS-LPB (A Europe-South
America Network for Climate Change Assessment and
Impact Studies in La Plata Basin).
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