Downscaling of South America present climate driven by 4

Transcrição

Clim Dyn (2012) 38:635–653
DOI 10.1007/s00382-011-1002-8
Downscaling of South America present climate driven
by 4-member HadCM3 runs
Sin Chan Chou • José A. Marengo • André A. Lyra • Gustavo Sueiro •
José F. Pesquero • Lincoln M. Alves • Gillian Kay • Richard Betts •
Diego J. Chagas • Jorge L. Gomes • Josiane F. Bustamante • Priscila Tavares
Received: 4 August 2010 / Accepted: 17 January 2011 / Published online: 12 February 2011
Ó Springer-Verlag 2011
Abstract The objective of this work is to evaluate climate simulations over South America using the regional
Eta Model driven by four members of an ensemble of the
UK Met Office Hadley Centre HadCM3 global model. The
Eta Model has been modified with the purpose of performing long-term decadal integrations and has shown to
reproduce ‘‘present climate’’—the period 1961–1990—
reasonably well when forced by HadCM3. The global
model lateral conditions with a resolution of 2.5° latitude 9 3.75° longitude were provided at a frequency of
6 h. Each member of the global model ensemble has a
different climate sensitivity, and the four members were
selected to span the range of uncertainty encompassed by
the ensemble. The Eta Model nested in the HadCM3 global
model was configured with 40-km horizontal resolution
and 38 layers in the vertical. No large-scale internal
nudging was applied. Results are shown for austral summer
and winter at present climate defined as 1961–90. The
upper and low-level circulation patterns produced by the
Eta-CPTEC/HadCM3 experiment set-up show good
agreement with reanalysis data and the mean precipitation
and temperature with CRU observation data. The spread in
the downscaled mean precipitation and temperature is
small when compared against model errors. On the other
hand, the benefits in using an ensemble is clear in the
S. C. Chou (&) J. A. Marengo A. A. Lyra G. Sueiro J. F. Pesquero L. M. Alves D. J. Chagas J. L. Gomes J. F. Bustamante P. Tavares
National Institute for Space Research (INPE),
Cachoeira Paulista, São Paulo 12630-000, Brazil
e-mail: [email protected]
G. Kay R. Betts
UK Met Office Hadley Centre, FitzRoy Road,
Exeter, Devon EX1 3PB, UK
improved representation of the seasonal cycle by the
ensemble mean over any one realization. El Niño and La
Niña years were identified in the HadCM3 member runs
based on the NOAA Climate Prediction Center criterion of
sea surface temperature anomalies in the Niño 3.4 area.
The frequency of the El Niño and La Niña events in the
studied period is underestimated by HadCM3. The precipitation and temperature anomalies typical of these
events are reproduced by most of the Eta-CPTEC/HadCM3
ensemble, although small displacements of the positions of
the anomalies occur. This experiment configuration is the
first step on the implementation of Eta-CPTEC/HadCM3
upcoming experiments on climate change studies that are
discussed in a companion paper.
Keywords South America Regional climate model Eta model Present climate Climate simulation
uncertainty
1 Introduction
Global Climate Models (GCMs) are the main tools for
studying long-term climate variability and change. However, these models have rather coarse resolution, which
poses limitations to the explicit simulation of mesoscale
processes and to the representation of topography, land use,
and land-sea distribution. Increases in spatial resolution can
be achieved by downscaling techniques: refining part of the
grid of a variable-resolution global model; nesting high
resolution Regional Climate Models (RCMs) in GCMs
conditions; and statistical downscaling of GCM simulations. Climate change projections derived from RCMs may
be considered useful for impact studies because of the
subcontinental nature of the patterns and magnitude of
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S. C. Chou et al.: Downscaling of South America present climate driven by 4-member HadCM3 runs
changes. The improvement of the description of orography
and other land surface characteristics helps to detail the
structure of weather systems. However, it should be noted
that the climate generated from a regional model is
strongly dependent upon the lateral boundary conditions.
Seth and Rojas (2003) and Rojas and Seth (2003) performed 5-month RegCM3 regional model for the South
American domain for two extreme wet and dry years, using
both reanalysis and atmospheric general circulation model
(AGCM) data as lateral boundary forcing with observed
sea surface temperatures (SST). They found that errors in
the low level circulation and moisture fields of the GCM
significantly degraded the nested model simulation, but that
the interannual signal was well reproduced.
The Eta version used at National Centers for Environmental Prediction has been used for process studies by
Berbery et al. (2003), Silva and Berbery (2006) and Collini
et al. (2008), among others.
The Brazilian Center for Weather Forecasts and Climate
Studies (CPTEC) has used the Eta regional model (hereafter referred as Eta-CPTEC) operationally since 1996 to
provide weather forecasts over South America. Due to its
vertical coordinate (Mesinger 1984), the Eta model is able
to produce satisfactory results in the regions containing
steep orography such as the Andes Cordillera. On prediction and predictability issues, Chou et al. (2000) did one of
the first experiments with a regional model for South
America, with 1 month of continuous integrations. The
Eta-CPTEC was used to investigate the precipitation predictability at different time scales–seasonal, monthly and
weekly–over South America (Chou et al. 2005). Comparisons of the CPTEC GCM seasonal climate forecasts with
those from Eta-CPTEC against observations show that this
regional model provides considerable improvement over
the driver model.
Various papers have assessed simulations of present
time climatology (1961–90) of South America from different regional models using the HadAM3P global model
as lateral boundary conditions (Alves and Marengo 2009
for the HadRM3P, da Rocha et al. 2009 for RegCM3;
Pisnichenko and Tarasova 2009 for Eta CCS, and Pesquero
et al. 2009 for Eta-CPTEC). When compared to observations, the regional simulations exhibit systematic errors
which might be related to the physics of the RCMs (e.g.,
convective schemes, topography and land surface processes) and the lateral boundary conditions and possible
biases inherited from the global model. Dynamical downscaling experiments for the purpose of constructing climate
change scenarios in South America have started to become
available for various emission scenarios and time-slices
until the end of the 21st Century.
These examples have used various regional models
forced with the global future climate change scenarios as
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boundary conditions from various global climate models
(Marengo et al. 2009a, b; Núñez et al. 2006; Alves and
Marengo 2009, Solman et al. 2007, Garreaud and Falvey
2008, Cabré et al. 2010, Urrutia and Vuille 2009, Pesquero
et al. 2009, and Chou et al. 2009). Pisnichenko and Tarasova
(2009) describe two time-slice integrations of the Eta model
modified for climate change studies (the so-called Eta-CCS)
driven by the HadAM3P global model. Their results show
precipitation fields with strong negative bias over a large
part of South America during summer for present-climate
simulations and weak precipitation activity along the
Atlantic Intertropical Convergence Zone (ITCZ). Another
version of the Eta model for decadal runs was developed by
Pesquero et al. (2009) and shows a considerable improvement in the representation of the patterns of precipitation in
austral summer and winter. These results indicate that the
Eta model provides added value in comparison to the
HadAM3P global model. Other articles on South American
regional simulation of present climate include Rauscher
et al. (2006, 2007), Seth et al. (2007), Solman et al. (2007),
Alves and Marengo (2009), Chou et al. (2009), Menéndez
et al. (2010) and references quoted therein.
The studies listed above generally use a single model. In
this study, we run a four-member regional model ensemble
in order to address some of the uncertainties inherent in any
model simulation. The boundary conditions are taken from
four members of the HadCM3 ‘‘Perturbed Physics
Ensemble’’ (PPE) in which the standard model structure is
used and perturbations are introduced to the physical
parameterization schemes to produce variants of the same
model (Murphy et al. 2004; Stainforth et al. 2005, Collins
et al. 2006a, Collins et al. 2010). The objective of this work
is to evaluate this regional climate ensemble model system,
Eta-HadCM3 members, and give some measure of the
uncertainty of the results provided by this ensemble over
South America.
This Eta-CPTEC regional model was run as part of the
impact analyses and vulnerability assessments needed for
the preparation of the Second National Communication of
Climate Change of Brazil to the United National Framework Climate Change Convention UNFCCC. A companion
paper analyzes regional future climate change projections
over South America for the A1B scenario derived from the
Eta-CPTEC nested in HadCM3 runs, referred hereafter as
Eta-CPTEC/HadCM3.
2 The models
2.1 The HadCM3 global model
The lateral boundary conditions used to drive the EtaCPTEC regional model are supplied by the UK Met Office
Hadley Centre coupled climate model HadCM3 (Gordon
et al. 2000; Collins et al. 2001). The HadCM3 atmosphere
has a resolution of 2.5° 9 3.75° latitude-longitude, with 19
levels in the vertical (Pope et al. 2000). The HadCM3
model has shown to perform favorably amongst current
climate models in the simulation of the climate in the
Amazon, as well as important teleconnections with largescale modes of variability in the tropical Pacific and
Atlantic (e.g., Gedney et al. 2000; Li et al. 2006; Good
et al. 2008), although some biases may still exist (Harris
et al. 2008).
The uncertainties present in any model simulation can
be explored through ensemble experiment design. One way
in which this can be achieved is through the multi-model
ensemble method, in which a coordinated effort such as the
Coupled Model Intercomparison Projects (CMIP, e.g.
Covey et al. 2003; Meehl et al. 2007), which brings together GCM output from different contributing climate
modeling centers. An advantage of this method is that a
wide variety of model designs and configurations form the
ensemble, often described as an ‘‘ensemble of opportunity’’. However, as such, the ensemble has not been
designed to completely encompass the range of model
uncertainty. The other method follows the perturbed
physics ensemble (PPE) approach (Murphy et al. 2004;
Stainforth et al. 2005; Collins et al. 2006b, 2010), which is
designed to quantify the modeling uncertainty in the simulation or projections of climate that depends on the way
processes are represented in the model, i.e. in their physics
parameters.
The HadCM3 ensemble was designed to quantify
uncertainty in projections of climate change derived from
uncertainty in model physics as per the second method
described above. Through expert elicitation, key uncertain
parameters were identified, primarily in the atmosphere but
also in the land surface, and their plausible ranges were
defined. These parameters were modified within these
plausible ranges to form a large (300 ? member) ensemble, run with a computationally-efficient slab ocean. From
this ensemble, a subset of 16 model variants, each with a
different combination of parameter settings, was selected
according to their performance in simulating current climate while still letting parameter sampling space widely
(Murphy et al. 2007). Together with the standard HadCM3
model, the 16 model variants were run in fully coupled
transient mode, forced with SRES A1B emissions scenariogenerated CO2 concentrations (Nakicenovic et al. 2000) to
the end of the 21st century. Even though each member of
the ensemble is forced with the same CO2 concentrations,
the effect of the different combinations of parameter settings alters the degree and to some extent the patterns of
climate change. The range in global mean temperature rise
by the end of the 21st century provided by variants of this
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one model is of a similar magnitude to the range given by
the AR4 multi-model ensemble (Collins et al. 2006b).
2.2 The Eta-CPTEC regional model
The Eta model (Mesinger et al. 1988; Black 1994; Janjić
1994) is a grid-point model based on the eta coordinate, g
(Mesinger 1984), which is defined as.
ð p pt Þ
ðpref ðZsfc Þ pt Þ
g¼
ðpsfc pt Þ
ðpref ð0Þ pt Þ
where p is the air pressure and Z is the height. The indices
t and sfc indicate model top and model surface, respectively. The index ref refers to values from a reference
atmosphere, therefore, pref(0) is the air pressure at the
height 0, and pref(Zsfc) is the air pressure at the surface,
both are taken from a reference atmosphere. The surfaces
of the g coordinate are approximately horizontal everywhere which is particularly adequate for regions with steep
orography such as the Andes Cordillera in South America.
The time scheme is the forward–backward scheme
modified by Janjić (Janjić 1979) for the adjustment terms
and a modified Euler-Backward scheme for the advection
terms. The space difference scheme prevents the two-grid
internal gravity wave separation. The prognostic variables
are temperature, specific humidity, horizontal wind, surface
pressure, the turbulent kinetic energy and cloud liquid
water/ice. These variables are distributed on the Arakawa
type E-grid.
Large scale forcing is defined in one row along the
lateral boundaries. The second row is a merge between the
outer and the inner third rows. Integration domain starts
from the third row. The lateral boundary formulation is
described by Mesinger (1977). This scheme does not adopt
the relaxation zones as the commonly used Davies (1976)
scheme. One-way nesting was used. No large-scale nudging is applied in the integration domain. Veljović et al.
(2010) have shown that the Eta Model maintains the large
scale features even using large domain without the need of
internal large scale nudging.
Model precipitation is produced by Betts-Miller-Janjić
cumulus parameterization scheme (Janjić 1994) and by the
Zhao cloud microphysics scheme (Zhao et al. 1997). The
land surface scheme (Chen et al. 1997; Ek et al. 2003) has
4 soil layers for temperature and moisture of depths, from
top to bottom: 10, 30, 60 and 100 cm.The scheme distinguishes 12 types of vegetation and 7 types of soil texture.
The vegetation map includes revised Amazon deforestation
arc (Sestini et al. 2002). The radiation scheme package was
developed by the Geophysical Fluid Dynamics Laboratory.
The scheme includes short-wave (Lacis and Hansen 1974)
and long-wave radiation (Fels and Schwarzkopf 1975). The
radiation tendencies are recalculated every 1 h and are
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applied every time step. The atmospheric turbulence
scheme has the turbulent kinetic energy as prognostic
variable. Monin–Obukhov similarity theory combined with
Paulson stability functions (Paulson 1970) are applied at
the surface layer.
2.2.1 Model setup
The Eta model has been used as the operational weather
and seasonal climate forecast model at INPE (Chou et al.
2005) for a few years. The seasonal climate version of the
Eta model was adapted to run decadal time range integrations, with focus on the study of climate change scenarios related to different levels of atmospheric CO2
concentration.
For the present climate studies, the CO2 concentration
was set to a constant value of 330 ppm. The Eta model was
set to have a calendar of 360 days in a year in order to
follow the HadCM3 models’ calendar. Boundary conditions for driving the Eta model were taken from the HadCM3 model ensemble, chosen to span the range of
uncertainty described by the model variants. The HadCM3
conditions were input at every 6 h, and linear update of the
Fig. 1 The top row refers to mean 200-hPa DJF streamlines for
1961–1990 from the Eta Model members a Low, b Medium, c High,
and d Unperturbed; and e HadCM3 ensemble mean. The bottom row
refers to mean 200-hPa JJA streamlines for 1961–1990 from the Eta
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values along the boundaries was applied at every time step.
Accordingly, three members were selected, which displayed high, medium and low sensitivity in global mean
temperature response. Together with the standard, unperturbed model, these provide the boundary conditions for
driving multiple realizations of the Eta-CPTEC regional
model, hereafter referred to as the high, medium, low, and
unperturbed experiments. Sea surface temperature values
were taken from the coupled ocean–atmosphere model
monthly means, sea surface temperature is updated daily
and linearly to the monthly values on the 15th day of each
month. Similar linear interpolation is applied to the vegetation greenness which is prescribed in fixed monthly mean
values. The results for 1961–90 present climate are
described here, and for 2010–2100 in the companion paper.
The model integration started on the 1st of January of
1960 and a one-year spin-up time was adopted. Therefore
the results are based on the period from 1st January 1961
until 30th December 1990. The integrations were continuous for 31 years. Initial soil moisture started from a January climatology and albedo started from a seasonal
climatology. The model was setup to run with 40-km
horizontal resolution, 38 vertical layers and 90 s time step.
Model members f Low, g Medium, h High, and i Unperturbed; and
j HadCM3 ensemble mean. Wind speeds greater than 25 and 30 m/s
are shaded
3 Results
The results focus on the climate features of the seasons
December–January–February (DJF) and June–July–August
(JJA), which are the austral summer and winter seasons,
respectively. For verification purposes, circulation fields
from the ERA40 reanalyses (Uppala et al. 2005) and
rainfall and temperature fields from the Climate Research
Unit CRU-University of East Anglia (New et al. 2000) are
used.
3.1 Large scale circulation
The downscaled large-scale flow is compared against the
driver global model and the ECMWF reanalysis data. This
comparison will show whether the nested model can
maintain the large-scale conditions input by the driver
model and whether the downscaled flow is accurate enough
for further use in impact studies. The average circulation
from the driver model is taken over the four HadCM3
members.
Figure 1a–d show the mean upper-level mean circulation simulated by the Eta-CPTEC for the austral summer
(DJF) season. The comparison with HadCM3 upper level
circulation (Fig. 1e) shows that the regional model follows
closely the conditions provided by the lateral boundaries by
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centering the upper level anticyclone over the Bolivian
Plateau and spanning it over most of the tropical part of the
continent. The downstream cyclonic vortex has its axis
along the coast of Northeast Brazil. These features were
transferred from the GCM. The correct description of the
large scale convection observed over the South American
continent during summer is strongly dependent on the
position and intensity of the circulation associated with the
anticyclone. During JJA (Fig. 1f–i), the upper level anticyclone is weak and has its centre positioned over northern
part of South America. This is in agreement with the
absence of deep convection in the central part of the continent during the JJA season. The subtropical upper level
jet is a clear feature with its mean magnitude exhibiting
values over 30 m/s.
Figure 2 shows the mean low level circulation, averaged
over the 30 years during the austral summer season.
Important features of the low level circulation are the
tropical Northeast trade winds, and the deflection of these
winds by the Andes. These winds to the east of the Andes
are important in the moisture transport from tropical into
subtropical latitudes, and on some occasions these winds
reach strong intensity and become the so called South
American Low Level Jet east of the Andes (SALLJ), particularly during summer (Saulo et al. 2000; Marengo et al.
2004, and references quoted therein). The intense
Fig. 2 Same as Fig. 1, but for 850-hPa streamlines
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Northeast trades contribute to maintaining the moisture
transport to the central and southeastern part of the continent where the maximum total precipitation occurs and
forms the South Atlantic Convergence Zone (SACZ).
Furthermore, the intense trades also contribute to the
rainfall and convective activity at the northern side of the
La Plata Basin. During winter, JJA, the Northeast trades are
weaker than in summer, but the SALLJ is still present,
exhibiting a strong northerly component, and contributing
to the frontogenesis over Argentina (Satyamurty et al.
1998). In a manner different from summer, this northerly
flow brings air masses from the subtropical South Atlantic,
compared to the summertime Amazonian air masses. The
Eta-CPTEC model shows an improved representation of
the maximum jet over the Andes, which is too wide in the
global model, through the increased spatial resolution and
consequent better representation of the complex Andes
orography.
Figure 3 shows the seasonal large scale circulation
averaged over the 30 year from ERA40 reanalyses, as well
as the CRU climatology of rainfall and temperature. At
upper levels, the anticyclones simulated by Eta are centered in about the same position in summer as the reanalyses; in winter, JJA, the intensity and position of the
subtropical jets in the Eta runs are also close to the
reanalysis data. The low level circulation features in the
regional model are comparable to the reanalyses, with a
slight model overestimation of the speed in the Northeast
trades. The Eta-CPTEC runs are able to add small scale
features that are absent in the global driving field or in the
ECMWF reanalyses (Figs. 2, 3), such as the details of the
low-level flow blocked in the surroundings of the Andes
Cordillera in Ecuador and Colombia or the flow convergence in the Serra do Mar mountains that are located along
the coast of southeast Brazil. Besides these small scale
features that which are mostly attributed to the difference
in resolution of mountain description between the EtaCPTEC (40 km), HadCM3 (2.5° 9 3.75° latitude-longitude) and the ERA40 reanalyses (2.5° 9 2.5° latitudelongitude), there are no significant differences in behavior
and spatial pattern of large-scale circulation of the EtaCPTEC runs as compared to the HadCM3 and ECMWF
Fig. 3 ERA40 wind reanalyses at 200 hPa for a DJF and e JJA; at 850 hPa for b DJF and f JJA. Wind speeds greater than 25 and 30 m/s are
shaded; CRU precipitation for c DJF and g JJA; CRU air temperature for d DJF and e JJA. Color scale is shown on the lower side of each panel
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Although the Eta CPTEC model reproduces very closely
the driver model large scale circulation patterns, the precipitation patterns of Eta and HadCM3 exhibit larger differences which may have several sources, for example the
different resolutions, the different cumulus and cloud
microphysics schemes, land-surface scheme, etc. Therefore, these differences can produce different precipitation
biases between the regional and global model.
The differences among the four Eta CPTEC simulations
are related to the different boundary conditions provided by
the driver model PPE. Here, each member of the EtaCPTEC regional model seasonal precipitation is shown and
compared against the ensemble mean HadCM3 seasonal
rainfall (Fig. 4), and should be looked together with the
CRU seasonal mean observations from Fig. 3. At first
glance, the precipitation patterns are very similar among
the 4 members and the HadCM3 model (Fig. 4). The
maxima of precipitation observed over the central part of
the continent, the SACZ, and the ITCZ over the Equatorial
Atlantic, are all features present in the downscaling runs,
and a common underestimation of precipitation can be
observed along the northern coast of the continent, where
the SACZ precipitation band would connect with ITCZ
band as seen in the CRU data (Fig. 3c). However, a closer
look reveals differences among the members in the quantity of precipitation. For example, over northern Argentina
and the Uruguay region, as well as over central Amazonia,
along the SACZ and Northeast Brazil, some members
underestimate precipitation more than the others. The
ITCZ also shows different precipitation amounts among
the members. However, verification of precipitation over
the oceans is difficult as satellite estimates are also questionable for such a long period. The dry bias in northern
Amazonia could have various sources, the stronger Eta
CPTEC/HadCM3 simulated Northeast trades may also
have a contribution to the bias, by pushing moisture farther
south and inland, which could displace the moisture saturation region.
In JJA, the ITCZ is displaced to the north as shown by
the maximum of precipitation. Within the four members,
the maxima of rainfall varies in location from nearer to the
coast to further inland, while the shape of the rainfall
maxima can be either narrow or wide, depending on the
member considered. All members show an area of no
precipitation in the central part of the continent, comparing
reasonably well with CRU data. Large overestimates of
precipitation are found over the Andes between southern
Fig. 4 The top row refers to mean DJF precipitation (mm/d) for
1961–1990 from the Eta Model members a aeyjb, b aeyjj, c aeyjo,
d aenwl, and e HadCM3 ensemble mean. The bottom row refers to
mean JJA precipitation (mm/d) for 1961–1990 from the Eta Model
members f aeyjb, g aeyjj, h aeyjo, i aenwl and j HadCM3 ensemble
mean. Shading intervals are 1, 3, 6, 9, and 12 mm/d
reanalyses. This shows the benefits of downscaling the
HadCM3 model over South America.
3.2 Precipitation
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Chile and Argentina. Regions such as the Andes Mountains
and the Amazon frequently lack observations, so verification in those areas is difficult given the uncertainties
introduced by the interpolation techniques used by the
CRU in the processing of their rainfall data set. The large
scale patterns simulated by HadCM3 in both summer and
winter are also simulated by Eta CPTEC, with perhaps the
main difference being in the rainfall over the Andes region,
where in the global model it is organized in a wide band
extending to the east of the Andes, while the regional
models display rainfall more concentrated over the Andes
themselves.
3.3 2-m temperature
The 2-m temperature is strongly dependent on the landsurface schemes and the surface layer scheme. These
schemes are different in the driver and nested models
which produce differences in the 2-m temperature pattern
and therefore different temperature bias between the
regional and driver models. Figure 5 shows the simulation
of seasonal air temperatures from the HadCM3 (ensemble
mean) and from each member from the Eta-CPTEC/
HadCM3 runs (observations are shown in Fig. 3). The
RCM simulations show the details of low temperatures
over the mountain areas, especially over the Andes Cordillera and the Brazilian Plateau, where both the observations and the Eta simulations show detail that the global
models do not capture. In DJF, all members show
underestimate of temperature over the Amazon region of
about 2°C. On the other hand, over the northern part of
Argentina, where precipitation is underestimated, the
temperatures are overestimated by all members by about
2°C. In JJA, although major temperature patterns are
similar to CRU observations, in the Amazon region the
members show larger dispersion, with some members
underestimating and others overestimating temperatures,
especially over western Amazon. Near the Central Brazilian Plateau, temperatures are underestimated by the
model members, with the magnitude of the errors varying
between 2 and 3°C. Over northern Argentina, where
temperatures are overestimated by the members during
austral summer season, in the winter, temperatures are
underestimated by almost 4°C. This is a region of frontal
passage and may suggest that the model generates much
colder air masses behind the cold fronts. Temperatures
over mountain areas are also much colder than observations during winter.
Fig. 5 Same as Fig. 4, but for 2-m temperature (°C). Shading interval is 2°C, and starts at 2°C
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3.4 Annual cycle
The model output annual cycle is verified for four regions
(Fig. 6): the Amazon, Northeast Brazil, the La Plata Basin
and a region centered in Brazil that includes portions of
Uruguay, Paraguay and Bolivia, and parts of Colombia,
Venezuela, Peru, Argentina and Chile. However, we will
refer to this region as ‘‘Brazil’’, for simplicity. These
regions were chosen particularly due to differences in their
precipitation regimes.
Monthly mean precipitation values are taken over the
period 1961–1990 for the four regions (Fig. 7). The annual
cycle of the Amazon region shows a large underestimation
of precipitation by the members during the rainy period
from December until May, but good agreement during the
drier period, between June and November. There is small
inter-member dispersion. In northern part of Northeast of
Brazil, where the rainy season occurs between February
and May, some model members overestimate precipitation
during that season but underestimate in the transition period, from October to December. Members show larger
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spread over Northeast Brazil than over Amazon region, but
this may also be affected in part by the difference in sizes
of the two regions. The benefits of running even a small
ensemble instead of relying on a single realization are
apparent, as some of those members lie closer to the
observations than the standard member, hence reducing the
mean error. In the La Plata Basin, the precipitation annual
cycle has smaller amplitude and varies from about 5 mm/d
in rainy season to 2 mm/d in drier season. Except in the
rainy months of January, February and March, the model
members generally follow closely the annual cycle of
precipitation in this region. The Brazil region summarizes
the evaluation of the other three regions. The maximum
and minimum of the annual cycle show agreement in time
with the observed CRU cycle. The underestimation in the
rainy period is largely due to negative errors produced in
the Amazon region and partly in the La Plata Basin.
The annual cycle of temperature for the four regions is
shown in Fig. 8. In the Amazon region, model temperature
tends to follow the observations, except in September and
October when mean temperatures are overestimated. In
Fig. 6 Selected regions of
South America where the
annual cycle is assessed
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Northeast Brazil, although the model reproduces reasonably
the timing of the warm and cold season, the temperatures
are underestimated throughout the year. In the La Plata
Basin, the model members reproduce the larger amplitude
of the annual cycle, although most of the members overestimate the temperatures between September and
November. Overall, the model members are successful in
reproducing the temperature annual cycles, with an spread
among members of less than 2°C. For rainfall, the observed
and simulated annual cycles are comparable, with perhaps
the caveat that there is an underestimation of observed
rainfall in Amazonia by at least 2.5 mm/d, with a spread
among members of less than 1 mm/day in this region.
3.5 Errors and spread
Figure 9a, d show the seasonal mean RMSE of precipitation
from the Eta CPTEC. The error of each ensemble member is
Fig. 7 Annual cycle of precipitation (mm/d) mean of 1961–1990
period for the a Amazon region, b Northeast Brazil, c La Plata Basin,
d and the entire Brazil. Color curves refer to: Eta Model members:
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calculated by subtracting the CRU observed climatology of
1961–1990 from the 30-year mean ensemble member precipitation. Observations are only available over land areas. In
DJF, the largest precipitation errors are found in southeastern
Brazil, the tropical Andes, southern Chile, and along the
northern coast of Brazil and Guyana. Except for the latter
region, the errors are related to model overestimation of
precipitation near mountain areas. More observations over
these mountain areas could help this verification. Errors along
the northern coast are due to model systematic underestimation of rainfall. Some errors occur in the Amazon, Paraguay and northern Argentina regions, and they are related to
underestimation of precipitation. In JJA, largest errors are
found in the northern Amazon near 2–3°N where the ITCZ is
positioned at this time of year. In this season the errors in
Chile increase and spread to the central part of the country.
Figure 9b, e show the ensemble mean of precipitation
and the spread among the members in DJF and JJA from
aeyjo (red), aeyjj (blue), aeyjb (green) and aenwl (orange); and CRU
observations (black)
Eta. The spread is calculated as the standard deviation of
the 30-year mean of the four members. This spread of the
four members can be considered as some sort of measurement of the Eta model’s internal variability. While the
maximum RMSE range about 2 and 6 mm/d, in both DJF
and JJA, the largest ensemble spread ranges from 0.5 to
1.0 mm/d over land, and reaches 2.0 mm/d over the ocean.
Large precipitation spread is also found in southern Chile.
In JJA, in southern Brazil, which forms part of the La Plata
Basin, although there is no significant increase in precipitation mean or RMSE, a small increase in the ensemble
spread occurs. In the construction of the ensemble members, it is desirable that the RMSE be of comparable
magnitude of the ensemble spread. However, here we have
found ensemble precipitation spread to be smaller than
model RMSE, particularly over the continent where
observations are available. Figure 9c, f show the ensemble
mean of precipitation and the spread among the members
in DJF and JJA from the HadCM3 driver model. The
magnitude of the spread as shown by the STDV is larger
645
over the Andes in the global than in the regional model. It’s
greater over the Andes in the GCM, and it’s greater along
the SACZ, Chile, ITCZ too In general the magnitude of the
ensemble spread in precipitation from both regional and
global models is smaller than the model rainfall RMSE
errors over the continent.
Figures 10a, d show the mean RMSE of 2-m temperature in DJF and JJA. The largest temperature errors occur
over the Andes Mountains, and are generally underestimates. As observations are scarce over mountain areas,
CRU observations there contain large uncertainty. In DJF a
major error area is found over northern Argentina and
Paraguay, where the model overestimates the temperatures.
These errors reach about 4°C. In JJA, errors in this area in
northern Argentina fall to a minimum, and large errors are
found in the Northeast Brazil region. Figure 10c, e show
the ensemble mean temperature and the spread among the
four members from the Eta CPTEC, in DJF and JJA,
respectively. The spread varies by about 0.5°C over most
of the continent, and the smallest spread is found over the
Fig. 8 Same as Fig. 7, but for 2-m temperature (°C)
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646
ocean, opposite to the pattern of spread in precipitation,
and which is probably a consequence of the small variability of sea surface temperatures. In summer, areas over
Bolivia, Paraguay and northern Argentina, northern Amazon and Guyana, and coastal areas in Peru exhibit largest
spread in temperature, whereas in winter, the largest temperature spread occurs in the central part of the continent
and over the Andes Mountains. The spread among members from HadCM3 (Fig 10c, f) is comparable in magnitude and distribution with that from Eta- CPTEC for both
summer and winter. In general the magnitude of the
ensemble spread in temperature is smaller than the model
temperature errors over the continent. Evaluation carried
out here show an under-dispersive character of the
ensemble as the spread is smaller than the RMSE. In the
calculation of the errors, we must also consider the
uncertainties in the CRU data set over regions such as
Amazonia and the Andes, due to the poor data coverage in
those regions, as compared to the well covered Northeast
Brazil of Southeastern South America regions.
Fig. 9 Root mean square errors of Eta Model precipitation of
1961–1990 for a DJF and d JJA. Ensemble mean (shaded) and spread
(contours) of Eta Model precipitation for b DJF and e JJA; Ensemble
mean (shaded) and spread (contours) of the HadCM3 model
precipitation for c DJF and f JJA; Mean precipitation shaded intervals
are 1, 3, 6, 9 and 12 mm/d. Spread contour interval is 0.5 mm/d
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647
Fig. 10 Same as Fig. 9, but for 2-m temperature (°C). Shading interval is 2°C, and starts at 2°C. Contour interval is 0.2°C
Table 1 Number of El Niño
and La Niña events observed
and simulated by the 4 EtaCPTEC/HadCM3 runs
La Niña El Niño
Observed
7
9
Unperturbed 8
4
High
4
5
Mid
4
5
Low
7
6
3.6 El Niño and La Niña events
A crucial question frequently posed by users of model
output for impact studies is how reliable are the HadCM3
model and Eta CPTEC/HadCM3 downscaled simulations
of El Niño and La Niña events, which are major climate
signals and whose impacts on different socio-economic
sectors have been studied around the world (e.g. see
Trenberth 2007 and references therein).
To answer the question, El Niño and La Niña events
were identified according to the criterion adopted by the
US Climate Prediction Centre. This criterion identifies El
Niño (La Niña) when the 3-month average of sea surface
temperature anomalies along the equatorial Pacific in the
region tagged as Niño 3.4 (120 W–170 W and 5 N–5 S) is
larger/smaller than (0.5°C/- 0.5°C) in 5 consecutive
overlapping seasons (Trenberth 1997). Based on this
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648
criterion, between 1961 and 1990, about 9 El Niño and
about 7 La Niña events were identified from observed SST
(Reynolds et al. 2002).
The HadCM3 sea surface temperature anomalies tend to
underestimate the frequency of occurrence of these events.
Table 1 shows the number of events for each member in
the present climate simulation. This table is based on
Fig. 11, which shows the time series of sea surface temperature anomalies from Niño 3.4. The magnitude of the
SST anomalies varies considerably among the members.
The unperturbed member has some El Niño events with
anomalies reaching almost 4°C, and although La Niña
events are more frequent then El Niño, the negative
anomalies are weaker for this member. The low sensitivity
member produces similar numbers of El Niño and La Niña
events, and the magnitude of the positive and negative
anomalies are more comparable, reaching about ±2°C. The
medium and high sensitivity members have similarities as
both exhibit weak anomalies, reaching about 1°C, and both
have similar numbers of El Niño and La Niña events. The
strength of these anomalies and also the number of events
will affect the temperature and precipitation anomalies of
the downscaling runs. It should be noted, however, that
coupled model simulated ENSO events can vary in magnitude and frequency through natural internal climate
variability, and the particular characteristics of the events
in the 1961–1990 period may be representative of these
variations rather than the parameter modifications. It does,
however, reinforce the benefits of running an ensemble,
such that the anomalies connected with a range of ENSO
types can be assessed.
The DJF periods during established El Niño or La Niña
events were selected based on Fig. 11 to show the impacts
of these events on temperature and precipitation.
Figure 12a–d shows the mean precipitation anomalies for
each member of the Eta-CPTEC ensemble El Niño events.
Negative anomalies are found over the Atlantic region of
ITCZ, whereas positive anomalies are found in the region
of SACZ. These anomalies often cause droughts in
Northeast Brazil and the Brazilian Amazon, and floods in
Southern Brazil (Ropelewski and Halpert 1987, Villar et al.
2008, Grimm and Tedeschi 2009), which are typical features during El Niño years. However, not all droughts in
those regions are related to El Niño, especially in tropical
South America east of the Andes (Marengo et al. 2008).
The precipitation anomalies of El Niño events from the
unperturbed member are more consistent as the negative
anomalies reach the Amazon and Northeast Brazil regions
and positive anomalies are present in southeast Brazil. The
positive precipitation anomalies in the eastern part of the
continent in the perturbed members is positioned too far
north for an accurate reproduction of El Niño impacts.
During La Niña years (Fig. 12e–h), the precipitation
123
anomaly pattern is roughly opposite to El Niño years, that
is, rainier along the ITCZ region and drier in the SACZ
region.
The temperature anomalies during El Niño DJF
(Figs. 13a–d) show warm anomalies over northern and
central part of the continent and cold anomalies around
northern parts of Argentina and the south of Brazil. This is
consistent with anomalies observed during El Niño events
in South America. The unperturbed member also shows the
largest amplitude of anomalies. In La Niña years
(Figs. 13e–h) the temperature anomalies are opposite, with
colder conditions in the northern and central parts of
Brazil. The members show large disagreement in the
temperature anomalies over Argentina.
4 Conclusions
The regional Eta Model was configured over South
America and applied to downscale HadCM3 members of
the PPE experiment for the present climate, considered
here the period between 1961 and 1990. As a step prerequisite for employing these downscaling conditions for
impact studies, the ability of the regional model to reproduce present climate, the estimate of the errors and the
ensemble members spread, was evaluated here. The evaluation focused on DJF and JJA seasons, which are the
austral summer and rainy season and the austral winter and
dry season, respectively. The results show that the upper
and low level large-scale circulations reproduce closely the
circulation from the global model without the need of
internal large-scale nudging and using a single row at the
lateral boundaries, which shows the efficiency of the lateral
boundary scheme. The continuous long–term integrations
show robustness and suitability of the model for climate
studies.
The low level flow shows that Eta-CPTEC could
reproduce the driver model patterns and was able to add
small scale features that were absent in the HadCM3 fields
due to its coarse resolution. The downscaled climatology of
precipitation and temperature is close to CRU observations.
Major continental precipitation and temperature errors
have been identified in areas of Amazonia and northern
Argentina and Paraguay. Mountain area verification would
require more in situ data as CRU observations do not show
high precipitation values over orography.
In austral summer in the Amazon region there is an
underestimation of rainfall, which is a characteristic common to various simulation experiments (Marengo et al.
1994, 2003, Stern and Miyakoda 1995, Cox et al. 2000,
among others), and which has been linked to the convection, land-surface and planetary boundary layer schemes.
In the subtropical latitudes of northern Argentina, the
649
Fig. 11 Times series of
3-month running mean
HadCM3 sea surface
temperature anomalies (°C)
from 1960 to 1990 for the Niño
3.4 region, for the a aenwl,
b aeyjb, c aeyjj and d aeyjo
members. Red shading refers to
El Niño events, blue refers to La
Niña events and black are
neutral periods
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650
Fig. 12 Precipitation anomalies (mm/d) for austral summer (DJF) of
El Niño and La Niña years. Anomalies are calculated subtracting the
1961–1990 climatology of the respective member. Maps are for the
laeyjo a, e, aeyjj b, f, aeyjo c, g and aenwl d, h members. Blue
shading refers to positive anomalies and red to negative anomalies
underestimation of precipitation is accompanied by model
overestimation of temperature. The ensemble members
from the Eta-CPTEC/HadCM3 simulations exhibit a small
spread when compared against model rmse. However, the
annual cycle of precipitation and temperature in some
areas, such as Northeast Brazil, shows some dispersion
among the members. Verification of model ability to
reproduce the regional signatures of El Niño and La Niña
events shows precipitation and temperature anomaly signs
typical of those events for summer, although some anomaly
patterns appear displaced, such as during El Niño events in
summer in Northeast Brazil. The HadCM3 SST generally
underestimates the number of El Niño and La Niña events,
which may have caused some weaker signal in the regional
Eta CPTEC/HadCM3 simulations.
The strengths and weaknesses identified in the EtaCPTEC should not be regarded as permanent defects, since
the model is undergoing continuous improvement. Besides
some regional systematic biases, especially in the convectively active equatorial regions, it is clear that some areas
exhibit systematic biases, such as the underestimation of
rainfall in northern Amazonia. Model physics require further
investigation in order to identify and reduce the model errors.
The results showed an under-dispersive character of
these downscaling runs when compared with model errors.
The regional model climatology results from the combination of the long series of atmospheric conditions coming
from the lateral boundaries and regional model internal
dynamics and physics. The small spread in the regional
model climatology suggested that for this time period, the
final climate has stronger dependence on the regional
model internal characteristics given similar large-scale
conditions provided by the driver model. Tests with
another global model long-term integration outputs are
ongoing. In addition, perturbations of regional model
physics parameters are also being tested. These tests can
help to give a measure of the magnitude of the regional
model uncertainty.
The evaluation carried out here shows that these regional climate outputs can be employed for climate change
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651
Fig. 13 Same as Fig. 12, but for 2-m temperature anomalies (°C). Orange to red shadings refers to positive anomalies and blue shading refers to
negative anomalies
studies, and errors should be considered when these outputs
are used to drive further impact studies. The Eta-CPTEC/
HadCM3 experiments on climate change projections are
discussed in a companion paper.
Acknowledgments The authors thank the UNDP Project BRA/05/
G31 and the FCO GOF-Dangerous Climate Change DCC project
from the UK. SC and JM were funded by the Brazilian National
Research Council CNPq. Additional funds came from the Brazilian
programs Rede-CLIMA, the National Institute of Science and Technology for Climate Change (INCT-CC), and from the European
Community’s Seventh Framework Programme (FP7/2007–2013)
under Grant Agreement no. 212492 (CLARIS LPB—A Europe-South
America Network for Climate Change Assessment and Impact
Studies in La Plata Basin).
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