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Vol.:(0123456789)1 3
Clim Dyn
DOI 10.1007/s00382-016-3484-x
Evaluation of the regional climate response in Australia
to large-scale climate modes in the historical NARCliM
simulations
L. Fita1,2 · J. P. Evans1,3 · D. Argüeso1,3 · A. King1,3,4 · Y. Liu1
Received: 13 October 2015 / Accepted: 30 November 2016
© Springer-Verlag Berlin Heidelberg 2016
response to large-scale modes better than the driving rea-
nalysis, though no regional model improves on all aspects
of the simulated climate.
Keywords Regional climate modelling · Australia ·
Climate modes
1 Introduction
Regional Climate Modelling (RCM) projects are designed
to: improve the regional representation of climate obtained
from Global Climate Models (GCM) and to provide added-
value (Giorgi and Mearns 1991) at the regional scale to the
climate change signal. Global models are run at coarse spa-
tial resolutions and they do not properly represent regional/
local features such as: topography, coastlines and land
cover changes. Using GCM outputs as driving conditions
for RCMs, which are run at higher resolution over a speci-
fied smaller area, allows one to obtain consistent climate
information, but with a better representation of some of the
details of the area under study (Hong and Kanamitsu 2014).
RCM outputs (‘high’ resolution climatological data-
sets) are commonly analyzed using surface observations
(mostly precipitation and minimum/maximum tempera-
tures) from station measurements (Fernández et al. 2007)
or gridded versions of them (Corney et al. 2013; Jiménez-
Guerrero et al. 2013; Ji et al. 2016). A common analysis
is to present a comparison of the simulated fields with
the observed fields, by means of statistical values such
as bias, root mean square error (RMSE), pattern correla-
tion (Evans and McCabe 2010; Argüeso et al. 2011) and
quantile plots (García-Díez et al. 2012). Beyond the basic
evaluation statistics a wide variety of analysis has been
performed focused on other aspects of the simulations
Abstract NARCliM (New South Wales (NSW)/Austral-
ian Capital Territory (ACT) Regional Climate Modelling
project) is a regional climate modeling project for the Aus-
tralian area. It is providing a comprehensive dynamically
downscaled climate dataset for the CORDEX-AustralAsia
region at 50-km resolution, and south-East Australia at
a resolution of 10 km. The first phase of NARCliM pro-
duced 60-year long reanalysis driven regional simulations
to allow evaluation of the regional model performance.
This long control period (1950–2009) was used so that the
model ability to capture the impact of large scale climate
modes on Australian climate could be examined. Simula-
tions are evaluated using a gridded observational dataset.
Results show that using model independence as a criteria
for choosing atmospheric model configuration from dif-
ferent possible sets of parameterizations may contribute to
the regional climate models having different overall biases.
The regional models generally capture the regional climate
Electronic supplementary material The online version of this
article (doi:10.1007/s00382-016-3484-x) contains supplementary
material, which is available to authorized users.
* L. Fita
1 Climate Change Research Center, UNSW, Sydney, Australia
2 Present Address: Laboratoire de Météorologique Dynamique
(LMD), CNRS, UPMC-Jussieu, Universit de Paris 6,
Tour 45-55, 2nd floor, Postal Box 99 4, place Jussieu,
75252 Paris Cedex 05, France
3 ARC Centre of Excellence for Climate System Science,
UNSW, Sydney, Australia
4 ARC Centre of Excellence for Climate System Science,
School of Earth Sciences, University of Melbourne, Sydney,
Australia
L. Fita et al.
1 3
such as extremes (Herrera et al. 2010; Domínguez et al.
2013; Evans and McCabe 2013; Lee et al. 2014).
Before undertaking future climate projections, it is
important to know the systematic errors, accuracy and
overall performance of the RCMs. This is commonly
done by running the model for a ‘control’ period, where
the boundary conditions are obtained from a reanalysis
and the outcomes from the simulations can be compared
directly to observations. The information obtained from
such a comparison provides an evaluation of the models’
performance and a baseline of their reliability in repro-
ducing the climate of the region, which are required to
put future climate simulations in context.
In this study, we present an evaluation of the control
period runs of the NARCliM project. Along with some
standard climatological statistics we also examine the
simulated impact of various large-scale climate modes
on Australian rainfall and temperature. It is assumed that
if the RCM is able to represent the regional effect of a
climate mode then longterm characteristics of the cli-
matology would be, at least partially, well captured by
the model. It is assumed that a proper representation of
the regional effect of a climate mode during the current
period ensures the good representation of the atmos-
pheric situation which the climate mode represents. Thus,
future changes in the intensity/frequency of the climate
modes and their related atmospheric effects will be also
well represented by the RCM. However, this will be lim-
ited by at least two factors: accurate representation of the
climate mode by the given GCM, and the assumption that
the atmospheric dynamics related to the climate mode
do not substantially change in the future climate. Only
climate modes that are known to affect Australian cli-
mate are investigated (Risbey et al. 2009), these are rep-
resented by the indices: Niño 1+2, Niño 3.4, IPO, SOI,
DMI, Blocking and SAM (see more details further in the
text and Table 2).
A number of large regional climate modeling projects
have been performed, including the current World Cli-
mate Research Program (WCRP) initiative CORDEX
(Giorgi et al. 2009). Similar previous exercises include
PRUDENCE (Christensen et al. 2007) and ENSEMBLES
(Hewitt 2005) over Europe, NARCAPP (Mearns et al.
2009) for north America, CLARIS (Boulanger et al. 2011)
for south America and RMIP for Asia (Fu et al. 2005).
Results from these experiments have shown added value
in terms of representing regional characteristics that the
global climate projections are not able to capture. Improve-
ments have been found in the representation of the dynam-
ics of the region such as sea-breeze and topographic effects
as well as improvements in the simulation of extreme
events (Feser et al. 2011; Luca et al. 2012; Corney et al.
2013; Solman 2013; Lee and Hong 2014).
Australia presents a richness of climates. The interior
is dominated by a dry and hot desert climate which covers
large areas of the country. To the north, a tropical area with
monsoon precipitation is found. On the east coast, in the
space between the Great Dividing Range and the sea, there
is an area of temperate climate which is also observed on
the southern island of Tasmania. The south-west displays
a Mediterranean type climate. This richness in climates
poses a challenge for RCMs due to the various dynamics
and features that shape the climate across the continent.
The representation of large-scale climate modes strongly
depends on the dynamics of the GCM. However, the impact
on a given region of the dynamics induced by the differ-
ent phases of the large scale mode might be influenced by
the RCM. Regional to local scale processes are likely better
represented by the RCMs due to their higher resolution and
higher complexity of parameterizations. These processes
include: eddy-induced circulations caused by SAM (Hen-
don et al. 2014), surface thermal anomalies influencing
precipitation (Hendon et al. 2007), realistic atmospheric
dynamics (Lim and Hendon 2015) and the generation and
interaction of Indian Rossby waves trains with Australian
baroclinicity and topography (Cai et al. 2011) among oth-
ers. The teleconnections between remote Indian and Pacific
ocean areas and their related indices, like IOD and ENSO
(Cai et al. 2012) will not be impacted by the RCM, since
these areas lie far outside its simulation area.
This article is structured as follows: the first section
describes the design of the experiment. The results of
a standard statistic analysis of model performance with
respect observations is shortly presented in Sect. 4. Fol-
lowed by Sect. 5 which examines the RCMs sensitivity to
the different climate modes, while the final section repre-
sents the conclusions.
2 The NARCliM project
The NARCliM (http://www.ccrc.unsw.edu.au/NARCliM/)
project is a collaboration with the NSW and ACT govern-
ments. It is intended to produce a comprehensive and con-
sistent set of regional climate change projections for use in
government planning processes. The experimental design
is detailed in Evans et al. (2014). Further information about
the project and access to the output data can be found on
the AdaptNSW website (http://climatechange.environment.
nsw.gov.au/Climate-projections-for-NSW/About-NAR-
CliM). Phase one of NARCliM involves using three RCMs
to simulate 60-years (1950-2009) with boundary conditions
obtained from the NCEP/NCAR reanalysis Project (Kal-
nay et al. 1996, NNRP 1). In the present article, Regional
Climate Model (RCM) refers to the use of a Limited Area
Model (LAM, here WRF) with a given specific physical
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
configuration. In this case, all RCMs share the dynamical
core and differ in the combinations of physical parameteri-
zations. It has been shown that different combinations of
physical parameterizations of a given LAM can provide
valuable information on the climate variability over a given
area (Fernández et al. 2007) and previous findings suggest
that the spread of results from models with different physi-
cal parameterization are as large as the spread from mod-
els with different dynamical cores (Jerez et al. 2013). The
RCMs are run in a two domain, one-way nested configura-
tion with the outermost domain at a coarse resolution of 50
km corresponding to the CORDEX AustraliaAsia AUS44
domain. A second domain focused on the main area of
interest covering NSW and the ACT is run at higher 10-km
resolution. Weak spectral nudging (von Storch et al. 2000)
of winds and geopotential down to around 500 hPa is also
used in the outer nest only. A second phase performed cli-
mate projections using 4 different GCMs from the CMIP3
(Olson et al. 2016).
The three RCMs used were chosen from an ensemble of
36 models created by choosing various physical parameter-
izations available within the Weather Research and Fore-
casting (Skamarock et al. 2008, WRF version 3.3) model-
ling framework. It has been shown that changes in physics
schemes provide an effective way to produce an ensemble
of RCM simulations (Fernández et al. 2007). It was desired
that these models should be able to simulate the climate of
the region well, while spanning as much of the variability
in the full ensemble as possible. Thus models were chosen
through a two-step process: first, the ’models’ performance
was evaluated across a collection of variables and metrics
(Evans et al. 2012; Ji et al. 2014); second, the models are
ranked using a metric that accounts for model independ-
ence based on the covariance of model errors (Bishop and
Abramowitz 2013). See more details in Evans et al. (2013).
Table 1 shows the physical parametrizations of the three
models selected using this method. As a complementary
analysis, the mean of the ensemble of the 3 WRF mod-
els will also be evaluated (labeled hereafter, Rmean). The
main focus of the article is the analysis on the large climate
simulated modes, for that reason, only results from monthly
means are used.
3 Datasets defining climate regions
Monthly precipitation, minimum and maximum tempera-
tures from a gridded observational dataset at 0.05◦ spa-
tial resolution and developed within the Australian Water
Availability Project (Jones et al. 2009, AWAP) were
selected as observational reference. This dataset has been
widely used in observational (King et al. 2013) and model-
ling studies in Australia (Evans and McCabe 2010; Pepler
et al. 2014). The density of surface observations used in
this gridded product varies substantially across Australia
(Jones et al. 2009, see discussion there in). To ensure that a
given grid point has a well observed climatology, we mask
the gridded data based on the availability of station data in
both space and time (see the supplementary material for
more information).
Many indices have been derived to characterize the
large-scale climate modes, particularly in the tropical
Pacific ocean where many different El Niño indices are
available. The selection of the indices is based in an inde-
pendence and atmospheric mechanism criteria. The inde-
pendence of the indices is based on a pair-wise pearson-
correlation analysis (see Fig. 2 in supplementary material)
and only indices with low correlations (below 0.5 among
all indices) were chosen. The mehcanism criteria will
include those indices which provide some insights from a
different atmospheric mechanism. Seven different climate
indices from observational or model derived sources have
been chosen (see Table 2, for more details). Note that over-
all, there are only two pairs of indices with larger than 0.5
pearson correlations: Niño 3.4 and dmi (correlation of 0.8),
blocking and sam (corr. 0.6).
Niño indices measure the sea surface temperature anom-
aly in different equatorial Pacific regions. The Southern
Oscillation Index (SOI), is computed as a pressure differ-
ence between Darwin (AUS) and Tahiti, and is a comple-
mentary way to observe the flow anomalies over the Equa-
torial Pacific Ocean. These indices provide a measure of the
El Niño/Southern Oscillation (ENSO) phenomena which
can significantly impact Australian rainfall. The Inter-dec-
adal Pacific Oscillation (IPO) represents a multi-decadal
sea surface temperature pattern in the Pacific Ocean. Dipole
Mode Index (DMI), provides a measure of the sea surface
temperature dipole in the Indian ocean. The DMI has been
found to influence precipitation mostly in southern Aus-
tralia (Ummenhofer et al. 2009, 2011). The blocking index,
Table 1 WRF physical schemes selected to generate the ensemble of
RCMs
pbl/sfc Planetary boundary layer and surface schemes, cu cumulus,
mp microphysics, sw/lw rad short and long wave radiation schemes,
MYJ Meyor–Yamada–Janjić (Janjić 1994), Eta scheme used in the
NCEP-eta simulations (Janjić 1996), YSU Yonsei university (Hong
et al. 2006), KF Kain-Fritsch (Kain 2004), BMJ Betts–Miller–
Janjić (Janjić 1994, 2000), WDM5 WRF Double-Moment 5 class
(Hong et al. 2004; Lim and Hong 2010), Dudhia (Dudhia 1989),
RRTM Radpid Radiative Transfer Method, (Mlawer et al. 1997) and
CAM Community Atmospheric Model, CAM (Collins et al. 2004)
Name pbl/Sfc cu mp sw/lw rad
R1 MYJ/Eta KF WDM5 Dudhia/RRTM
R2 MYJ/Eta BMJ WDM5 Dudhia/RRTM
R3 YSU/MM5 KF WDM5 CAM/CAM
L. Fita et al.
1 3
measures the strength of an almost persistent high pressure
zone in the southeastern area of Australia, which dominates
the flow in this part of the continent. It has been computed
by P. Maher (Climate Change Research Center, Sydney)
using the NOAA 20CR-reanalysis (Compo et al. 2011) fol-
lowing Bureau of Meteorology definition (Pook and Gib-
son 1999, BI index). The Southern Annular Mode (SAM),
provides information about the atmospheric flow around
Antarctica which affects the southern hemisphere storm
tracks.
Risbey et al. (2009) analyzed the influence of each of
these indices on Australian precipitation using observa-
tions. They explicitly analyze the relative contribution to
precipitation variability of these climate modes, depending
on season and location. Other studies have investigated the
impact of ENSO on Australian temperatures (Power et al.
1998; Jones and Trewin 2000). In this study the influence
of different large-scale modes on precipitation, and mini-
mum and maximum temperature was investigated.
A set of climate divisions were identified to study
regional characteristics of the model performance from a
temporal perspective. Such regions were determined using
a multi-step regionalization based on the method proposed
by Argüeso et al. (2011) and applied to daily precipita-
tion, minimum and maximum temperature. The multi-step
methodology consists of seasonal Probability Distribution
Function (PDFs) calculated for each of the variables, an
agglomerative clustering and a non-hierarchical k-means
clustering. The methodology is designed to limit the num-
ber of subjective choices to the minimum possible, make
the most of the strengths of each step while reducing their
weaknesses. The method removes, as much as possible, the
information redundancy through PDFs, provides a suitable
number of regions and a first guess via the agglomerative
clustering, and allows for recombination of clusters through
k-means. As a result, it was possible to define 14 areas
for Australia (Fig. 1) with climate similarities offering an
appropriate way of analysing model outputs at regional
scales. The regions obtained are comparable to previously
defined climate areas (Stern et al. 2000), and those defined
in CCIA, (Climate Change in Australia, http://www.climat-
echangeinaustralia.gov.au/en/) with some differences due to
the addition of other information such as: biophysical fac-
tors and expected climate change signals.
4 Basic statistic analysis
A standard evaluation of model outputs over the long term
simulated period, using AWAP data-set as reference, is pre-
sented in this section. The most important results are here
described, whilst the full description can be found in the
supplementary material.
Spatial distribution of bias (see in supplementary mate-
rial figure 2) show mixture of results that depend on: geo-
graphical zone, variable and model. Coastal and tropical
area precipitation is over estimated by the RCMs, with bet-
ter results for the R2 physical parametrizations (the unique
RCM with BMJ as cumulus scheme) with a signature of
the role of orography (the Great Dividing Range along
the Eastern side of the continent). R3 (the unique run with
YSU/MM5 and CAM/CAM as pbl/surface layer and sw/lw
radiative schemes) has the smallest bias in minimum tem-
perature with a mixture of areas with positive and negative
bias. A north-south pattern in the bias is also obtained by
R1 and R2 with little bias in the north and an increasingly
negative bias in the south. All 3 RCMs produce an oppo-
site signed bias for maximum temperature with respect to
the driving re-analysis. No signature of the orography is
observed in the bias of temperatures. Overall the collection
of biases across the three variables differs for each RCM
demonstrating some independence in their model errors as
expected given the method for their selection (Evans et al.
2014).
Analysis of the seasonal bias (figures 8 and 9 in supple-
mentary material) shows a stronger seasonal sensitivity of
Table 2 Table with the period
covered and origin of the index
data used. The standardized
version of SOI has been used
KNMI Koninklijk Nederlands Meteorologisch Institut (Holland), MO-HCCC Met. Office Hadley Centre
for Climate Change, NOAA National Oceanic and Atmospheric Administration (USA), JAMSTEC Japan
Agency for Marine-Earth Science and Technology (Japan), CCRC Climate Change Research Center (Aus-
tralia), BAS British Antarctic Survey (UK)
Index Period Source
Niño 1+2 1950–2009 NOAA, http://www.esrl.noaa.gov/psd/data/correlation/nina1.data
Niño 3.4 1950–2009 KNMI, http://climexp.knmi.nl/data/inino5.dat
IPO 1950–2007 MO-HCCC www.iges.org/c20c/IPO_v2.doc
SOI 1951–2009 NOAA, http://www.cpc.ncep.noaa.gov/data/indices/soi
DMI 1958–2009 JAMSTEC, http://www.jamstec.go.jp/frcgc/research/d1/iod/
DATA/dmi_HadISST.txt
Blocking 1950–2009 CCRC, P. Maher using NOAA 20CR re-analysis
SAM 1957–2009 BAS, http://www.nerc-bas.ac.uk/icd/gjma/sam.html
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
the re-analysis compared to the RCMs. For precipitation,
one of the RCMs (R2) shows a stronger seasonality in the
bias, and corresponds to the run with the overall lowest
bias. Except for the winter season (JJA), the RCMs show
an overall overestimation of the convection even in the
north during the dry season. For minimum temperature, R3
shows higher seasonal sensitivity in the bias, and is also the
RCM run with the overall lower bias (also true for all sea-
sons). There are no remarkable differences between RCM
runs for the bias in maximum temperature as they all show
a sensitivity with season dependency.
Examination of the precipitation distribution through
region based q-q plots (see in suppl. material figures 10–20)
reveals a mixture of results among seasons, regions, and
variables. While no RCM consistently performs better than
the other RCMs they do in general perform better than the
driving reanalysis. The best performance tends to occur in
summer (DJF) rather then winter (JJA). In particular they
show underestimations of precipitation in the south-west
and in western Tasmania and the highest mountains likely
due to mislocation of storm tracks and under-representation
of topographic effects respectively. They also tend to pro-
duce too much precipitation in the dry season (JJA) in the
northern areas affected by the Monsoon.
Time-series analysis for all period (figures 21–28 in
suppl. material) shows a good match between simulated
and observed time evolutions of the most significant events
(dry/wet spells, cold/warm periods). For temperature,
agreement between WRF simulated outcomes and observa-
tions is better than with the driving re-analysis. However,
precipitation tends to be higher in the models, particularly
in the tropical regions for R1 and R3 which use the Kain-
Fritsch convection parameterization.
Due to the long period simulated, a trend analysis has
been performed (see tables 2–4 in suppl. material). In gen-
eral RCM runs show a mix of results on the precipitation
trends, with no clear improvement over the driving reanaly-
sis. For temperature, WRF models consistently improve on
the trends found in the driving reanalysis, even though they
tend to overestimate trends in the minimum temperature
and underestimate them for the maximum temperature.
A complementary fourier-based spectrum analysis (see
in suppl. material figures 29 and 30) shows clear peaks on
5 and 10 year periods for all-variables and data-sets used in
the study, indicating the importance of considering decade
scale variability in precipitation over much of the Austral-
ian continent.
5 Response to climate modes
In this section we analyse the long term climatological
response of model outputs to various large-scale climate
modes. This is done through correlations of local precipita-
tion and temperature fields with indices related to the cli-
mate modes. The indices examined are described in Sect. 3
and Table 2. It should be noted that the centres of action for
the climate modes occur largely outside the RCM domain.
The effects of these climate modes are translated into the
RCM domain through the lateral boundary conditions.
Here we examine how successful the RCMs are at propa-
gating these boundary condition changes into precipitation
region climate
1 extreme dry desert
2 alpine temperate
3 tropical
4 desert with some tropical storm intrusions
5 semi-desert
6 desert with some sporadic storm intrusions
7 temperate wet
8 Mediterranean
9 alpine wet
10 coastal sub-tropical
11 coastal temperate
12 tropical
13 tropical
14 mountain sub-tropical
Fig. 1 Climate affinity spatial regions for temperature and precipitation following a two step method (with seasonality included) using the
AWAP data-set (see text for more details), and its comparison of derived regions and mean Australian climate characteristics
L. Fita et al.
1 3
and temperature changes over Australia. The results for
selected seasons and indices are shown here, the full range
of results are contained within the supplementary material.
Indices were obtained from various sources as shown in
Table 2.
5.1 Spatial distribution
Figure 2 shows the spring (SON) correlations (computed
for all the period of coincident data between index and sim-
ulations) between climate modes represented by the SOI
and the blocking indices, and the local precipitation, mini-
mum and maximum temperature. The SOI index shows the
highest correlation values over a large area during spring
(see top panels on Fig. 2). Niño 3.4 shows the second larg-
est correlations during spring (see supplementary material).
Blocking during spring has the third largest impact (based
on overall correlation mean) on the climate conditions (see
lower panels on Fig. 2).
For the standardized SOI index, precipitation is highly
correlated over most of Australia. It covers most of the
north-eastern half of Australia, except along the east coast
fringe known as the eastern seaboard. This clearly depicts
the different dynamics of precipitation in this area, includ-
ing the somewhat unique character of the eastern seaboard.
The RCM runs show a larger area with significant corre-
lations than observed (more overall colored values in the
maps), while NNRP shows weaker correlations particularly
in the east. It must be remembered that much of the centre-
west of the country has unreliable or missing observations.
For minimum temperature, all models are able to show the
dipole pattern of correlations between the south-west cor-
ner and the north-east. However, models overestimated the
area covered by positive correlations, along with a mixture
of magnitudes and extension of covered area at the anti-cor-
relation zone. For maximum temperature, correlations are
less significant and models generally fail to reproduce the
observed weak negative correlation over eastern Australia.
Rmean shows a better correlation map except for precipita-
tion with a clear stronger and wider positive correlation.
Positive correlation during SON between the blocking
index and precipitation is well represented by the models
with some differences between them (Fig. 2). Blocking is
mostly correlated with precipitation over the southern and
eastern portions of Australia. Results clearly show the
impact on the correlation with the increase of horizontal
resolution from NARCliM runs. WRF runs show a positive
correlation in the windward section of the mountain range
and a negative one on the lee side. For the minimum tem-
perature, NNRP re-analysis shows too much of the north
being positively correlated and not enough of the south
being anti-correlated. The RCMs also have difficulty rep-
licating the observed correlations with no areas of positive
correlation captured and only R3 reproducing the extent of
the negatively correlated region. The larger significant anti-
correlation pattern for the maximum temperature is well
captured by all the models. However, the RCM runs under-
estimate the strength of the correlation. For this index,
Rmean presents better correlation maps than the individual
models except for minimum temperatures.
While the RCM simulations generally do well at cap-
turing the spatial distribution of correlations with climate
mode indices there are examples when this is not the case.
These are: SON Niño 1+2 tasmax, JJA Niño 3.4 tasmax,
MAM SAM tasmin, SON SAM tasmax (see Fig. 3), JJA
SOI std tasmax, JJA DMI tasmax, MAM blocking tasmax
(see the rest of them in figure 50, ‘Poorly captured corre-
lations’ in supplementary material). It is noticeable that
NNRP re-analyses shows a good correlation structure in
these examples. These poor correlations might show some
of the limitations of the methodology and might be of con-
cern in climate projections. However these poorly captured
correlations are not in general high, nor the most important
in the area where the agreement is missed (see further text
and Fig. 6). However for MAM SAM tasmin (SW Aus-
tralia, suppl. material figure 55) and JJA SOI std tasmax
(North Australia, suppl. material figure 56) (both with cor-
relations lower than 40%) they are the most important ones
in some areas. This might introduce some doubts in the
projected climate changes in the affected areas.
Knowing the long term characteristics (decadal variabil-
ity) of the IPO index, yearly mean correlations maps are
also provided (see Fig. 4) which would better capture the
influence of the index. Observations show a stronger cor-
relation using yearly mean averaged fields than seasonal
fields (see supplementary material). Yearly precipitation is
correlated over much of the Eastern part of the continent.
While not as uniform as in the observations, the RCMs
do show significant correlations scattered over the eastern
part of the continent, an improvement over the NNRP. All
models have difficulty reproducing the observed yearly
IPO correlations with temperature. Rmean improves the
relationship with precipitation by producing larger coher-
ent anti-correlated regions in the east, though it does not
improve the temperature correlations.
5.2 Regional index correlation
Correlation analysis is reproduced for the time-series of
the spatial mean for each climate region. Only the results
for the spring (SON) season are shown (Fig. 5) since it is
the season with the largest influences from these modes.
Results for other seasons can be found in the supplemen-
tary material. IPO has a cycle with yearly characteristics,
thus it is excluded from this analysis (seasonal based).
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
As noted previously, SOI, Niño 3.4 and blocking show
the highest correlations in spring (SON). To a certain
extent, all regions show significant correlations with some
of the analysed indices, although this is less consistent for
temperature variables. The RCMs are able to capture the
different regional correlations with each index. The RCMs
better reproduce the observed regional correlations with
precipitation when compared to reanalysis. However, they
Fig. 2 Temporal correlation
maps of the grid point seasonal
mean values with the time-
series of various indices. For the
SOI index (left column), and for
the blocking index (right col-
umn). Correlations for seasonal
accumulated precipitation (top
row), mean seasonal minimum
temperature (middle row)
and mean seasonal maximum
temperature (bottom row). Only
correlations with a significance
above 95% threshold are shown.
Number inside maps show
the mean significance corre-
lated value for the entire map
(used to order the influence of
the modes). Correlations are
computed for the full period
of coincidence between index
values and simulation runs
L. Fita et al.
1 3
Fig. 3 As in Fig. 2, but for the indices, seasons and variables where WRF runs do not well capture the significant driven effects. SON Niño 1+2
tasmax (top), JJA Niño 3.4 tasmax (bottom left), MAM sam tasmin (bottom right)
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
Fig. 4 As for Fig. 2, but for correlation for yearly averages with IPO index
L. Fita et al.
1 3
overestimate the correlation with minimum temperature
and underestimate the correlation with the maximum tem-
perature. The SAM index shows no significant correlations
with observed minimum temperature.
A composite map with the index with the largest sig-
nificant correlation at each grid point is also provided.
Again only for the SON season (Fig. 6, see the supple-
mentary material for the other seasons). For precipita-
tion, the pattern of indices are generally well represented
by the models. However, all models have areas that differ
from each other. These differences may be, at least partly,
due to the RCMs being chosen based on the independ-
ence of their model errors. However, an over depend-
ence on the DMI index in certain parts of the continent
is given, whereas in others there is a mixture between
SOI and Niño 3.4 indices. However, the relationship with
DMI in these areas is well known (Ummenhofer et al.
2009; Cai et al. 2012). The gray area in the maps cor-
responds to grid-points where the index with the highest
correlation was not significant at 95% level.
Models tend to overestimate the correlation with the
minimum temperature of the standardized SOI, IPO and
DMI indices (see middle panel in Fig. 6). R3 shows the
best representation of the spatial influence of the indi-
ces on minimum temperature and clearly differs from
R1 and R2. Most of the country does show significant
correlations between maximum temperature and block-
ing, while northern Australia has a mix of many influ-
ences. All models properly represent this with significant
correlations over a larger portion of the country with a
stronger DMI influence in R3. A better representation
of the dominant modes is obtained with Rmean except
in precipitation, where the blocking influence is clearly
underestimated.
Fig. 5 SON Regional (see definition of regions in Fig. 1) spatial
mean (x axis) time-series correlation with different climatological
indices (y axis). For precipitation (top), minimum temperature (bot-
tom left) and maximum temperature (bottom right). Only correlation
values above the 95% of significance are plotted in colors, non-signif-
icant are plotted in grey
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
6 Summary and conclusions
A series of regional climatological runs have been per-
formed within the NARCliM project in order to inform the
climate change impact and adaptation work of state govern-
ments. Results of the control period runs (1950–2009) of
three different WRF configurations (RCMs) are analysed
here.
6.1 Summary
This analysis has focused on seasonal and longer time
scales. A high resolution gridded observational data-
set has been taken as the reference. Analysis is done in
multiple ways for precipitation, minimum and maximum
temperature.
Fig. 6 Map of the indices with the maximum correlation (1950–
2009) during SON at each grid point. For precipitation (top), mini-
mum (bottom left) and maximum (bottom right) temperatures. Each
index is given by a different colour. Only correlated indices with
more than 95% significance are shown
L. Fita et al.
1 3
A general comparison of the model outcomes with the
observed gridded data-set in terms of biases, q–q plots
and trend analysis has been done (see more detail in sup-
plementary material). All WRF runs present a positive pre-
cipitation bias along the Great Dividing Range (mountain
range along the East coast). R1 and R3, which were run
with the same cumulus scheme (Kain-Fristch) also present
a positive bias in the north which is dominated by tropical
precipitation during the monsoon season. R1 and R2 (shar-
ing MYJ/ETa pbl and Dudhia/RRTM radiative schemes)
present a negative bias in minimum temperature whereas
R3 presents a positive one. All WRF runs show a nega-
tive bias in maximum temperature. In general, WRF biases
differ from those shown by the forcing re-analysis NNRP.
Seasonal precipitation biases are lower in winter (JJA), but
temperatures biases are larger. Model runs present a quan-
tile dependency of precipitation error only for the extreme
percentiles, being worst in tropical regions. Distribution
of regional temperatures show similar errors for all quan-
tiles. WRF is capable of reproducing the transient evolution
of precipitation and temperatures in all regions. Climate
trends in precipitation from WRF are different than those
given by NNRP suggesting strong local effects. WRF runs
overestimate minimum temperature trends and underesti-
mate those for maximum temperature.
Following a standard statistical analysis, correlation
with different indices representing climatological modes is
also performed. In general patterns of the most significant
indices (in order, standardized SOI, Niño 3.4 and blocking)
on the most impacted season (SON, spring), are well cap-
tured by the RCMs. In most cases, the correlation between
the climate mode indices and local climate variable is
improved in comparison with the driving re-analysis. How-
ever, there are some examples where the WRF simulations
do not represent the correlation with particular indices well.
Due to the long-term variability of the IPO index (dec-
ades), a complementary analysis based on yearly means is
also presented. In this case the RCMs are better able to cap-
ture the observed correlation with precipitation, compared
to the driving reanalysis, but not with temperature.
At the regional scale, seasonal simulated correlations
match the observed correlations well, with larger discrepan-
cies in the temperature fields. Maps of the most correlated
index at each grid point and season, are less well repre-
sented with the worst results for the minimum temperature.
Evaluating the mean of the ensemble of WRF models
presents a mix of results. The ensemble mean generally
improves the trends compared to any individual model. In
some cases it produces a better correlation map with vari-
ous large scale modes. However, any such improvement is
case specific and it remains unclear whether the ensemble
mean is able to provide a general improvement in terms of
the correlation with large scale climate modes.
It has been found that climate tendencies (see supple-
mentary material) in Tasmania and Victoria (regions 7 and
9, with few grid-points in NNRP) differ between NNRP
forcing and WRF runs. These differences in simulated
trends provide a note of caution when examining future
trends in these regions.
6.2 Conclusions
Overall, results show that dynamical downscaling of the
NNRP re-analysis using these RCMs improves the simu-
lated climate in a number of ways, though not all aspects
are improved. Due to the length of simulations performed
(60 years, 1950–2009), it is possible to examine the influ-
ence of large scale climate modes on local precipitation and
temperature. In general the RCMs improve the representa-
tion of these teleconnections compared to the driving rea-
nalysis due to their higher resolution and better represen-
tation of regional atmospheric dynamics. Though there are
seasons and climate modes where the reanalysis performs
better. Thus while many aspects of the simulations demon-
strate the added value provided by the RCMs, the improve-
ments are not found for all indices, variables and seasons.
It is worth noting that the RCMs presented in this study
differ in key parametrizations, specifically chosen as they
produced independent errors measured against a select
group of events (Evans et al. 2012, 2014; Ji et al. 2014).
This independence at short time scales has manifested
itself as independence of the errors at climatological time
scales, as seen in the long-term biases, q-q plots and tel-
econnections with climate modes. This behaviour vali-
dates the selection process in terms of producing a small
RCM ensemble where each model contributes independent
information rather than simply being near copies of each
other. This also conforms with previous findings that multi-
physics ensembles can produce ensemble spreads similar
to multi-model ensembles (Vautard et al. 2013; Kotlarski
et al. 2014).
Overall these RCMs are able to capture the spatial struc-
ture of the correlations of Australian climate with indices
of climate modes including the spatial variability, inter-
annual variability and trends, and teleconnections with
large scale climate modes quite well. In many cases the
RCMs improve on the climate produced by the driving rea-
nalysis. The results also identify reasons for caution when
using these WRF models to produce climate projections
including the over-estimation of precipitation in the tropical
north of Australia by RCMs R1 and R3 and the reversal of
rainfall trends for two regions in the south-east. WRF mod-
els are able to show improvement in the climate representa-
tion through better resolving regional rainfall drivers such
as topography, coastal effects and blocking events which
are particularly important in the eastern seaboard and
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
Tasmania as shown previously (Evans and McCabe 2010,
2013). The Eastern seaboard is also impacted by storm sys-
tems known as East Coast Lows which are better captured
at these resolutions (Luca et al. 2015, 2016b). We also
note that these WRF models have been extensively tested
for added value compared to the driving global model and
found to provide improvements across most aspects of the
model tested however improvements in precipitation were
generally smaller than those found for temperature (Luca
et al. 2016a). These simulations are being performed as
part of the NARCliM project in order to produce regional
scale future climate projections that can be used in climate
change impacts and adaptation research.
Acknowledgements This research was undertaken with the assis-
tance of resources provided at the ‘NCI National Facility’ through the
National Computational Merit Allocation Scheme supported by the
Australian Government. NARCliM is funded through a consortium
of project partners including NSW Office of Environment and Herit-
age (OEH), ACT Environment and Sustainable Development Direc-
torate, Sydney Water, Sydney Catchment Authority, Hunter Water,
NSW Department of Transport, NSW Department of Primary Indus-
try, NSW Office of Water. The work was supported by the Australian
Research Council as part of the Future Fellowship FT110100576 and
Linkage Project LP120200777. Authors are also gratefully to all the
different institutions which compute and provide the global climate
indices. Authors also thanks, P. Maher from the ARCCSS-CCRC for
the computations of the blocking index.
References
Argüeso D, Hidalgo-Muñoz JM, Gámiz-Fortis SR, Esteban-Parra MJ,
Castro-Díez Y, Dudhia J (2011) Evaluation of wrf parameteriza-
tions for climate studies over southern spain using a multi-step
regionalization. J Clim 24:5633–5651
Bishop CH, Abramowitz G (2013) Climate model dependence and the
replicate earth paradigm. Clim Dyn 41:885–900. doi:10.1007/
s00382-012-1610-y
Boulanger JP, Schlindwein S, Gentile E (2011) Claris lpb wp1: meta-
morphosis of the CLARIS LPB European project: from a mecha-
nistic to a systemic approach. CLIVAR Exch 16:7–10
Cai W, van Rensch P, Cowan T, Hendon HH (2011) Teleconnection
pathways of ENSO and the IOD and the mechanisms for impacts
on Australian rainfall. J Clim 24(15):3910–3923. doi:10.1175/20
11JCLI4129.1
Cai W, van Rensch P, Cowan T, Hendon HH (2012) An asymmetry
in the IOD and ENSO teleconnection pathway and its impact
on Australian climate. J Clim 25(18):6318–6329. doi:10.1175/
JCLI-D-11-00501.1
Christensen JH, Carter TR, Rummukainen M, Amanatidis G (2007)
Evaluating the performance and utility of regional climate mod-
els: the PRUDENCE project. Clim Change 81:1–6. doi:10.1007/
s10584-006-9211-6
Collins WD, Rasch PJ, Boville BA, Hack JJ, McCaa JR, William-
son DL, Kiehl JT, Briegleb B, Bitz C, Lin SJ, Zhang M, Dai Y
(2004) Description of the NCAR community atmosphere model
(CAM 3.0). NCAR Technical Note NCAR/TN-464+STR:226
Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin
X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Brönni-
mann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY,
Jones PD, Kruk M, Kruger AC, Marshall GJ, Maugeri M, Mok
HY, Nordli O, Ross TF, Trigo RM, Wang XL, Woodruff SD,
Worley SJ (2011) The twentieth century reanalysis project. Q J R
Meteorol Soc 137:1–28. doi:10.1002/qj.776
Corney S, Grose M, Bennett JC, White C, Katzfey J, McGregor J,
Holz G, Bindoff NL (2013) Performance of downscaled regional
climate simulations using a variable-resolution regional climate
model: Tasmania as a test case. J Geophys Res 118(21):11936–
11950. doi:10.1002/2013JD020087
Domínguez M, Romera R, Sánchez E, Fita L, Fernández J, Jiménez-
Guerrero P, Montávez JP, Cabos WD, Liguori G, Gaertner MA
(2013) Present climate precipitation and temperature extremes
over Spain from a set of high resolution RCMs. Climate research
58:149–164. doi:10.3354/cr01186
Dudhia J (1989) Numerical study of convection observed dur-
ing the winter monsoon experiment using a mesoscale
two-dimensional model. J Atmos Sci 46:3077–3107.
doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
Evans JP, McCabe MF (2010) Regional climate simulation over Aus-
tralia’s Murray-Darling basin: a multitemporal assessment. J
Geophys Res 115:D14114. doi:10.1029/2010JD013816
Evans JP, McCabe MF (2013) Effect of model resolution impact on
regional climate and climate change using WRF over south-east
Australia. Clim Res 56:131–145. doi:10.3354/cr01151
Evans JP, Ekström M, Ji F (2012) Evaluating the performance of a
WRF physics ensemble over south-east Australia. Clim Dyn
39:1241–1258. doi:10.1007/s00382-011-1244-5
Evans JP, Ji F, Abramowitz G, Ekström M (2013) Optimally choosing
small ensemble members to produce robust climate simulations.
Environ Res Lett 8:044050. doi:10.1088/1748-9326/8/4/044050
Evans JP, Ji F, Lee C, Smith P, Argüeso D, Fita L (2014) A regional
climate modelling projection ensemble experiment–NARCliM.
Geosci Model Dev 7(2):621–629. doi:10.5194/gmd-7-621-2014
Fernández J, Montávez JP, Sáenz J, González-Rouco JF, Zorita E
(2007) Sensitivity of MM5 mesoscale model to physical param-
eterizations for regional climate studies: annual cycle. JGR
112(D04):101. doi:10.1029/2005JD00664
Feser F, Rockel B, von Storch H, Winterfeldt J, Zahn M (2011)
Regional climate models add value to global model data: A
review and selected examples. Bull Am Meteor Soc 92:1181–
1192. doi:10.1175/2011BAMS3061.1
Fu C, Wang S, Xiong Z, Gutowski WJ, Lee DK, McGregor JL,
Sato Y, Kato H, Kim JW, Suh MS (2005) Regional climate
model intercomparison project for Asia. Bull Am Meteor Soc
86(2):257–266
García-Díez M, Fernández J, Fita L, Yagüe C (2012) Seasonal
dependence of WRF model biases and sensitivity to pbl schemes
over Europe. Q J R Met Soc 139:501–514
Giorgi F, Mearns LO (1991) Approaches to the simulation of
regional climate change: a review. Rev Geophy 29(2):191–216.
doi:10.1029/90RG02636
Giorgi F, Jones C, Asrar G (2009) Addressing climate information
needs at the regional level: the CORDEX framework. WMO
Bull 58:175–183
Hendon HH, Thompson DWJ, Wheeler MC (2007) Australian rain-
fall and surface temperature variations associated with the
Southern hemisphere Annul Mode. J Clim 20(11):2452–2467.
doi:10.1175/JCLI4134.1
Hendon HH, Lim EP, Nguyen H (2014) Seasonal variations
of subtropical precipitation associated with the South-
ern Annular Mode. J Clim 27(9):3446–3460. doi:10.1175/
JCLI-D-13-00550.1
Herrera S, Fita L, Fernández J, Gutiérrez JM (2010) Evaluation of
the mean and extreme precipitation regimes from the ensembles
regional climate multimodel simulations over Spain. J Geophys
Res 115(D21):117
L. Fita et al.
1 3
Hewitt CD (2005) The ENSEMBLES project: providing ensemble-
based predictions of climate changes and their impacts. EGGS
Newsl 13:22–25
Hong SY, Kanamitsu M (2014) Dynamical downscaling: fundamental
issues from an NWP point of view and recommendations. Asia
Pac J Atmos Sci 50(1):83–104. doi:10.1007/s13143-014-0029-2
Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice-
microphysical processes for the bulk parameterization of cloud
and precipitation. Mon Weather Rev 132:103–120
Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package
with an explicit treatment of entrainment processes. Mon Wea
Rev 134:2318–2341
Janjić ZI (1994) The step-mountain eta coordinate model: further
developments of the convection. Mon Weather Rev 122:927–945
Janjić ZI (1996) The surface layer in the NCEP eta model. Elev-
enth Conference on Numerical Weather Prediction, Norfolk,
VA, 1923 August. American Meteor Society, Boston, MA, pp
354–355
Janjić ZI (2000) Comments on development and evaluation of a con-
vection scheme for use in climate models. J Atmos Sci 57:3686
Jerez S, Montávez JP, Gómez-Navarro JJ, Lorente-Plazas R, Garcia-
Valero JA, Jiménez-Guerrero P (2013) A multi-physics ensemble
of regional climate change projections over the Iberian peninsula.
Clim Dyn 41(7):1749–1768. doi:10.1007/s00382-012-1551-5
Ji F, Ekström M, Evans JP, Teng J (2014) Evaluating rainfall patterns
using physics scheme ensembles from a regional atmospheric
model. Theor Appl Climatol 115(1–2):297–304. doi:10.1007/
s00704-013-0904-2
Ji F, Evans JP, Teng J, Scorgie Y, Argüeso D, Luca AD, Olson R
(2016) Evaluation of long-term precipitation and temperature
WRF simulations for southeast Australia. Clim Res 67:99–115
Jiménez-Guerrero P, Montávez JP, Domínguez M, Romera R, Fita L,
Fernández J, Cabos WD, Liguori G, Gaertner MA (2013) Mean
fields and interannual variability in RCM simulations over Spain:
the ESCENA project. Clim Res 57:201–220
Jones DA, Trewin BC (2000) On the relationships between the
el Niño-southern oscillation and Australian land surface
temperature. Int J Clim 20(7):697–719. doi:10.1002/1097-
0088(20000615)20:7<697::AID-JOC499>3.0.CO;2-A
Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate
data-sets for Australia. Aust Meteorol Mag 58:233248
Kain JS (2004) The Kain-Fritsch convective parameterization: an
update. J Appl Meteorol 43:170–181
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin
L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A,
Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J,
Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The
NCEP/NCAR 40-year reanalysis project B. Am Meteorol Soc
77:437–471. doi:10.1175/1520-0477(1996)077<0437:TNYRP>
2.0.CO;2
King AD, Alexander LV, Donat MG (2013) Asymmetry in the
response of eastern Australia extreme rainfall to low-frequency
pacific variability. Geophys Res Lett 40(10):2271–2277.
doi:10.1002/grl.50427
Kotlarski S, Keuler K, Christensen OB, Colette A, Déqué M, Gob-
iet A, Goergen K, Jacob D, Lüthi D, van Meijgaard E, Niku-
lin G, Schär C, Teichmann C, Vautard R, Warrach-Sagi K,
Wulfmeyer V (2014) Regional climate modeling on European
scales: a joint standard evaluation of the Euro-CORDEX RCM
ensemble. Geosci Model Dev Discuss 7:217–293. doi:10.5194/
gmdd-7-217-2014
Lee JW, Hong SY (2014) Potential for added value to downscaled cli-
mate extremes over Korea by increased resolution of a regional
climate model. Theor Appl Climatol 117:667–677. doi:10.1007/
s00704-013-1034-6
Lee JW, Hong SY, Chang EC, Suh MS, Kang HS (2014) Assess-
ment of future climate change over east Asia due to the RCP
scenarios downscaled by GRIMS-RMP. Clim Dyn 42:733–747.
doi:10.1007/s00382-013-1841-6
Lim EP, Hendon HH (2015) Understanding the contrast of Aus-
tralian springtime rainfall of 1997 and 2002 in the frame of
two flavors of el Niño. J Clim 28(7):2804–2822. doi:10.1175/
JCLI-D-14-00582.1
Lim KSS, Hong SY (2010) Development of an effective double-
moment cloud microphysics scheme with prognostic cloud con-
densation nuclei (ccn) for weather and climate models. Mon
Weather Rev 138:1587–1612
Luca AD, de Elía R, Laprise R (2012) Potential for added value in
precipitation simulated by high-resolution nested regional cli-
mate models and observations. Clim Dyn 38(5–6):1229–1247.
doi:10.1007/s00382-011-1068-3
Luca AD, Evans JP, Pepler A, Alexander L, Argüeso D (2015) Res-
olution sensitivity of cyclone climatology over eastern Aus-
tralia using six reanalysis products. J Clim 28:9530–9549.
doi:10.1175/JCLI-D-14-00645.1
Luca AD, Argüeso D, Evans JP, de Elía R, Laprise R (2016a) Quan-
tifying the overall added value of dynamical downscaling and
the contribution from different spatial scales. J Geophys Res
121(4):1575–1590. doi:10.1002/2015JD024009
Luca AD, Evans JP, Pepler AS, Alexander L, Argüeso D (2016b)
Evaluating the representation of Australian east coast lows in
a regional climate model ensemble. J South Hemisphere Earth
Syst Sci 66:108–124
Mearns L, Gutowski WJ, Jones R, Leung R, McGinnis S, Nunes A,
Qian Y (2009) A regional climate change assessment program
for north America. EOS Trans AGU 90:311–312. doi:10.1029/
2009EO360002
Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997)
Radiative transfer for inhomogeneous atmosphere: RRTM, a
validated correlated-k model for the long wave. J Geophys Res
102(D14):16663–16682
Olson R, Evans JP, Luca AD, Argüeso D (2016) The NARCliM pro-
ject: Model agreement and significance of climate projections.
Clim Res 69:209–227
Pepler A, Timbal B, Rakich C, Coutts-Smith A (2014) Indian
Ocean Dipole overrides ensos influence on cool season rainfall
across the eastern seaboard of Australia. J Clim 27:3816–3826.
doi:10.1175/JCLI-D-13-00554.1
Pook MJ, Gibson T (1999) Atmospheric blocking and storm tracks
during sop-1 of the frost project. Aust Meteor Mag 1:51–60
Power S, Tseitkin F, Torok S, Lavery B, Dahni R, McAvaney B
(1998) Australian temperature, Australian rainfall and the south-
ern oscillation, 1910–1992: coherent variability and recent
changes. Aust Meteorol Mag 47:85–101
Risbey JS, Pook MJ, McIntosh PC, Wheeler MC, Hendon HH (2009)
On the remote drivers of rainfall variability in Australia. Mon
Wea Rev 137(10):3233–3253. doi:10.1175/2009MWR2861.1
Skamarock WC, Klemp JB, Dudhia J, Gill DO, Duda DMBMG,
Huang XY, Wang W, Powers JG (2008) A description of the
advanced research WRF version 3. NCAR Technical Note
475:NCAR/TN475+STR
Solman SA (2013) Regional climate modeling over south America: a
review. Adv Meteorol 504357:13. doi:10.1155/2013/504357
Stern H, de Hoedt G, Ernst J (2000) Objective classification of Aus-
tralian climates. Aust Met Mag 49:87–96
von Storch H, Langenberg H, Feser F (2000) A spectral nudging tech-
nique for dynamical downscaling purposes. Mon Weather Rev
128(10):3664–3673
Ummenhofer CC, Gupta AS, Taschetto AS, England MH (2009)
Modulation of Australian precipitation by meridional
Evaluation of the regional climate response in Australia to large-scale climate modes in the…
1 3
gradients in East Indian Ocean sea surface temperature. J Clim
22(21):5597–5610. doi:10.1175/2009JCLI3021.1
Ummenhofer CC, Gupta AS, Briggs PR, England MH, McIntosh PC,
Meyers GA, Pook MJ, Raupach MR, Risbey JS (2011) Indian
and Pacific Ocean Influences on Southeast Australian Drought
and Soil Moisture. J Clim 24(5):1313–1336. doi:10.1175/2010J
CLI3475.1
Vautard R, Gobiet A, Jacob D, Belda M, Colette A, Déqué M,
Fernández J, García-Díez M, Goergen K, Güttler I, Halenka T,
Karacostas T, Katragkou E, Keuler K, Kotlarski S, Mayer S, van
Meijgaard E, Nikulin G, Patarčić M, Scinocca J, Sobolowski S,
Suklitsch M, Teichmann C, Warrach-Sagi K, Wulfmeyer V, Yiou
P (2013) The simulation of European heat waves from an ensem-
ble of regional climate models within the Euro-CORDEX pro-
ject. Clim Dyn 41:2555–2575. doi:10.1007/s00382-013-1714-z