GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-9-1143-2016Validation of the ALARO-0 model within the EURO-CORDEX frameworkGiotOlivierolivier.giot@meteo.beTermoniaPietDegrauweDaanDe TrochRozemienCaluwaertsStevenSmetGeertBerckmansJulieDeckmynAlexDe CruzLesleyhttps://orcid.org/0000-0003-4458-8953De MeutterPieterDuerinckxAnneliesGerardLucHamdiRafiqVan den BerghJorisVan GinderachterMichielVan SchaeybroeckBertRoyal Meteorological Institute, Brussels, BelgiumCentre of Excellence PLECO (Plant and Vegetation Ecology), Department of Biology, University of Antwerp, Wilrijk, BelgiumDepartment of Physics and Astronomy, Ghent University, Ghent, BelgiumOlivier Giot (olivier.giot@meteo.be)30March2016931143115229July20151October20153March20164March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/9/1143/2016/gmd-9-1143-2016.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/9/1143/2016/gmd-9-1143-2016.pdf
Using the regional climate model ALARO-0, the Royal Meteorological Institute
of Belgium and Ghent University have performed two simulations of the past
observed climate within the framework of the Coordinated Regional Climate
Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive
the model for the period 1979–2010 on the EURO-CORDEX domain with two
horizontal resolutions, 0.11 and 0.44∘. ALARO-0 is characterised by the new microphysics scheme 3MT, which
allows for a better representation of convective precipitation. In
several metrics assessing the performance in representing
seasonal mean near-surface air temperature and precipitation are defined and
the corresponding scores are calculated for an ensemble of models for
different regions and seasons for the period 1989–2008. Of special interest
within this ensemble is the ARPEGE model by the Centre National de Recherches
Météorologiques (CNRM), which shares a large amount of core code with
ALARO-0.
Results show that ALARO-0 is capable of representing the European climate in
an acceptable way as most of the ALARO-0 scores lie within the existing
ensemble. However, for near-surface air temperature, some large biases, which
are often also found in the ARPEGE results, persist. For precipitation, on
the other hand, the ALARO-0 model produces some of the best scores within the
ensemble and no clear resemblance to ARPEGE is found, which is attributed to
the inclusion of 3MT.
Additionally, a jackknife procedure is applied to the ALARO-0 results in
order to test whether the scores are robust, meaning independent of
the period used to calculate them. Periods of 20 years are sampled from the
32-year simulation and used to construct the 95 % confidence interval for
each score. For most scores, these intervals are very small compared to the
total ensemble spread, implying that model differences in the scores are
significant.
Introduction
The climate projections used in the Fifth Assessment Report (AR5) of the
Intergovernmental Panel on Climate Change are based on the
set of global climate model (GCM) simulations performed within the fifth
Coupled Model Intercomparison Project CMIP5;. The
horizontal resolution of the contributing GCMs is limited to typically 1–2∘
by computational constraints. For many local climate impact
studies, regional climate models RCMs; are needed to reveal
the fine-scale details of potential climate change . In
addition, specific downstream models which simulate processes such as
vegetation interactions, urban effects e.g. or extreme
hydrological events in river catchments often require high-resolution (both
in time and space) forcing data from atmospheric models.
The Coordinated Regional Climate Downscaling Experiment
CORDEX; aims to perform both empirical–statistical
downscaling and regional climate simulations on different areas across the
globe using an ensemble of RCMs. By prescribing several integration domains
and resolutions, a direct quantitative comparison between the participating
models' performances and projections is feasible. The domain of interest in
this study, is the EURO-CORDEX domain shown in Fig. (inner
orange box). Several RCM groups have performed simulations on this domain
with horizontal resolutions of both 0.11 and 0.44∘.
All RCMs have a history in Numerical Weather Prediction (NWP) and often
consist of a modified NWP code which is further developed separately from or
parallel to the NWP code, borrowing for example its dynamical core but using
different physics parameterisations or surface schemes .
In the present day, NWP limited area models (LAMs) are designed for resolutions down to
a few kilometres, with adapted physics parameterisation schemes. At even
higher resolutions, these models can (partly) resolve clouds and convective
systems. Since a correct treatment of the cloud feedback is of critical
importance for climate modelling e.g., some of
these NWP models have been used in climate mode: studies by
, , and
, where models with resolution at the kilometre scale are
used without convection parameterisation, show a better representation of the
intensity of extreme precipitation, the diurnal cycle, afternoon convection
onset and less drizzle. For instance, ALADIN-CLIMATE of the Centre National
de Recherches Météorologiques CNRM; is a climate
version of the ALADIN limited area model that has been developed in the
context of the international ALADIN consortium .
Over the past decade, within the context of the ALADIN consortium, a physics
parameterisation scheme called 3MT (Modular Multiscale Microphysics and
Transport) has been developed and used as the central feature of a new NWP
model, ALARO-0 . It is based
on a parameterisation of deep convection and optimally adapted to be used at
resolutions in the so-called grey zone. Several countries have used and
tested the model for operational weather forecasting and regional climate
studies. The main feature of 3MT is scale awareness, i.e. the
parameterisation itself works out which processes are unresolved at the
current resolution, in contrast to traditional parameterisations which are
switched on or off or have different tuned parameter values at different
resolutions. This allows 3MT to generate consistent results across scales, as
shown by in an extended downscaling experiment covering
the period from 1961 to 1990. In their study, for every day, short-term
simulations were performed at different horizontal resolutions between 40 and 4 km. Both
the initial and lateral boundary conditions were provided by either the
ERA-40 reanalysis or model simulations at a lower resolution in a double
nesting procedure. Given the large amount of required computing resources for
such a simulation, this type of validation is rather unusual for NWP models.
The results showed that extreme precipitation values are correctly and
consistently reproduced for all horizontal resolutions by a model version
including 3MT, whereas extreme precipitation was progressively overestimated
when increasing the resolution by a model version without 3MT.
Domain boundaries of the used integration grids. The CORDEX
community prescribes the rotated lat–long EURO-CORDEX domain (inner orange
box) which is completely encompassed by the E-OBS domain (outer orange box).
The outer green boxes show the RMIB-UGent-11 (dashed lines) and RMIB-UGent-44
(full lines) conformal Lambert domain boundaries. The inner green boxes
exclude the eight grid point Davies coupling zone. In black the different
European climatic regions as defined in are shown
(BI: the British Isles, IP: the Iberian Peninsula, FR: France, SC: Scandinavia, ME: mid-Europe, AL: the Alps, MD: the Mediterranean, EA: eastern
Europe).
In the present study the ALARO-0 model has been used to perform the
EURO-CORDEX validation simulations, i.e. the conditions of ERA-Interim reanalysis
is used as lateral boundary conditions allowing for a direct
comparison to observations. The model setup differs from the setup used in
, since in the current study simulations are initialised
on the 1 January 1979, after which they are only forced at the
boundaries by ERA-Interim. This allows the model and its surface fields in
particular to become independent of the initial state. Results are then
compared to an ensemble of 17 other EURO-CORDEX experiments which have been
evaluated in , which we will refer to as K14 from now on. In
K14, seasonal means of near-surface air temperature and precipitation amounts
are compared to observations using several metrics which quantify the
spatiotemporal performance of the ensemble. In their article, they evaluate the 20-year period 1989–2008, while for this study the
32-year period 1979–2010 was simulated.
The objective of the present work is (1) to quantify the performance of the
ALARO-0 model within the existing K14 ensemble and (2) to assess the
robustness of the calculated scores given the rather short 20-year period
used in K14.
This paper is organised as follows. In Sect. , the
existing K14 ensemble, details on the setup of ALARO-0 and the methods used
to attain the goals of this paper are discussed. In Sect. ,
results are presented for ALARO-0 and compared to the K14 ensemble, followed
by a discussion in Sect. . Finally, in
Sect. , we come back to the goals that were set,
formulate conclusions and present an outlook.
Data and methodsK14 ensemble
The CORDEX community prescribes two European integration grids which differ
only in resolution. The low-resolution EUR-44 domain's grid points are
0.44∘ apart on a rotated lat–long grid limited to Europe (see inner
orange box in Fig. , 106 × 103 grid boxes). For the
high-resolution EUR-11 experiment, each EUR-44 grid box is divided into
16 0.11∘-wide grid boxes. In K14, a total of 17 experiments were
analysed by 9 different research groups. Eight groups performed both the
EUR-11 and EUR-44 simulations, one group only EUR-11, and three groups used
the same model (WRF) but with different physics parameterisations. All models
are forced directly by ERA-Interim except for the experiment performed by
CNRM. This group set up the global model ARPEGE (version 5.1) to be strongly
nudged towards ERA-Interim outside of the CORDEX domain, but allowed the
model to evolve freely inside of it. Further details on all models can be
found in Table 1 of K14.
The main conclusions of K14 were that the higher resolution simulations did
not perform significantly better and the models in the ensemble generally had
a cold and wet bias, except for summers in southern Europe which are commonly
warm and dry biased.
Setup of the ALARO-0 model
The ALARO-0 model used for this study is the identical configuration of the
ALADIN system described in detail and validated by
. Essentially, ALARO-0 uses the dynamical core of ALADIN,
but with different physics routines (e.g. for radiation, microphysics and
convection, cloudiness, turbulence), which are designed to tackle the issues
that arise when using resolutions of 1–15 km, which is known as the
grey zone for convection. Here, we only describe the EURO-CORDEX specific
setup of the model, which is the coupling to the boundary conditions and the
definition of the integration grids.
Similar to all other models in K14 (except for the global CNRM model),
ALARO-0 is coupled to ERA-Interim by the classical Davies procedure
. The relaxation zone consists of eight grid points irrespective of
resolution, and new boundary conditions are provided every 6 hours. No
further nudging or relaxation towards the boundary conditions was done inside
of the domain. Some fields in ALARO-0 are constant during runtime, most
notably sea surface temperatures (SSTs). Simulations are, however, interrupted
and restarted monthly to allow for SSTs to be updated. Other fields that have
monthly updates, but are constant during any given month are surface
roughness length, surface emissivity, surface albedo and vegetation
parameters. All other variables were computed continuously from
1 January 1979 to 31 December 2010 and thus, in contrast to
, no daily restarts were done.
It would be preferable to use the exact rotated lat–long grids defined by the
CORDEX community for the simulations. However, ALARO-0 does not support this
projection but instead uses a conformal Lambert projection. Following the
CORDEX guidelines, two new grids with a 12.5 and 50 km resolution were
defined for the ALARO-0 simulations. Figure shows the
bounding boxes of the low-resolution (full green lines) and high-resolution
(dashed green lines) ALARO-0 Lambert domains. The outer boxes show the
complete domain, while the inner boxes exclude the relaxation zone. The grids
were chosen such that the common EURO-CORDEX analysis domain (inner orange
box in Fig. ) is completely included in the non-coupling
zone. The low-resolution Lambert domain consists of 139-by-139 grid points,
while the high-resolution domain consists of 501-by-501 grid points (both
including eight coupling grid points at every boundary). In both simulations, the
number of vertical levels was 46. Following K14, we will refer to the results
with the acronym of the institute performing the simulations, yielding
RMIB-UGent-11 and RMIB-UGent-44, for the high- and low-resolution
simulations, respectively. These model data will be uploaded to the Earth System Grid
Federation (ESGF, website: http://esgf.llnl.gov/) data nodes.
Data
As an observational reference set, the E-OBS data set version 7 was used
. The E-OBS data set has a 0.22∘ rotated lat–long version
(outer orange box in Fig. ) which encompasses the complete
EURO-CORDEX domain. In the overlapping area, each E-OBS grid box contains
four grid boxes of the EUR-11 domain and by consequence each EUR-44 box
contains four E-OBS boxes.
In order to effectively compare model and observations, both need to share a
common grid. The same approach as in K14 was taken to interpolate all data to
a common grid. For the high-resolution simulations, first the values of the
closest grid point were taken to go from the native Lambert ALARO-0 grid to
the EUR-11 grid for both precipitation and temperature. For the latter, an
additional height difference correction between the ALARO-0 and closest
EUR-11 grid point was performed using the standard climatological lapse rate
of 0.0064 K m-1. Second, on this grid, for both precipitation and
temperature, two-by-two grid box averages were calculated to obtain an
identical grid to the E-OBS data set.
For the low-resolution simulations, again a closest grid point mapping from
the native grid to the EUR-44 grid and temperature-height correction was
performed. Then, the E-OBS data set was averaged over two-by-two grid boxes
that are in every EUR-44 grid box and used as reference.
Analysis methods
In K14, model performance is quantified for several metrics in different
regions and seasons based on seasonal mean values of near-surface air
temperature (or simply temperature from now on) and precipitation. All
considered regions and their acronyms are shown in Fig. and
details regarding the definition of the different metrics can be found in K14, more
specifically in Appendix A. Here, we only consider mean bias (BIAS), 95th
percentile of the absolute grid point differences (95 %-P), ratio of
spatial variability (RSV), pattern correlation (PACO), ratio of interannual
variability (RIAV) and temporal correlation of interannual variability
(TCOIAV). The climatological rank correlation (CRCO) and ratio of yearly
amplitudes (ROYA) were not considered here, since these metrics showed very
similar performance for all other models. Reanalysis forced simulations are
by construction correlated with the observed weather at the seasonal timescale.
For this reason, low correlation in time, even for short time periods,
can be interpreted as an RCM deficiency for these simulations. This is not true
for GCM-driven simulations, where only the correct number of occurrences in a
certain time period (typically 30 years) are supposed to be represented and
correlations at the shorter-than-decadal scale are meaningless due to strong
interannual variability. Therefore, we expect TCOIAV to be positive for the
simulations in this study, i.e. relatively cold/warm seasons in the
simulations should coincide with relatively cold/warm observed seasons, while
for GCM-driven simulations TCOIAV is expected to be zero. By contrast, all
other scores should be similar for reanalysis-driven and GCM-driven RCM
simulations if the GCM boundary conditions sufficiently represent the
observed climatology. Due to realistic boundary conditions from reanalyses,
the typical 30-year verification period for GCM-driven simulations can be shortened
to 20 years, as in K14 where all scores are calculated based on the period
1989–2008. However, as the authors of K14 state, this implies that the
“short evaluation period, leading to a sample size of only 20
seasonal/annual means, also hampers a sound analysis of statistical
robustness”. The 32-year long integration period of ALARO-0 allows us to
quantify how the scores change for different 20-year analysis periods and as
such to test their robustness.
A jackknife procedure was applied for this purpose; let
I=1979, …, 2010 be
the set of 32 years for which the ALARO-0 simulations were performed and I
a random subset of length 20 of I. We write the score for the
metric s for a certain subregion j and season k based on the set of
years I as
sjk(I)
with j∈BI, IP, FR, ME, SC, AL, MD,
EA, k∈DJF: winter, MAM: spring, JJA: summer, SON: autumn, YEAR: year. For example, in K14, values for sjk are calculated
based on IK14=1989, …,
2008. To study the robustness of sjk we study the
distribution of sjk(I) for all possible I. The number of possible
20-year subsets from 32 years without repetition and ordering is given by the
binomial coefficient: 32!/(20!(32-20)!)= 225 792 840. It is,
however, not feasible to perform the calculations for all possible combinations and
therefore only 1000 random sequences were chosen. The width of the 95 %
confidence interval, limited by the 25th and 975th value of the ordered
series of sjk, then quantifies the robustness of the score.
Spatial BIAS of near-surface air temperature (K) over the sample
IK14 for DJF (left) and JJA (right) for RMIB-UGent-11. Compare to
Fig. 2 of .
ResultsTemperature
Figure shows the spatial distribution of the daily mean
temperature RMIB-UGent-11 BIAS in winter (DJF, left) and summer (JJA, right)
for the years in IK14. Compared to Fig. 2 from K14, the spatial
bias of RMIB-UGent-11 in winter looks very similar to CNRM-11. Both models
show a general cold bias in southern Europe, a warm bias in north-eastern
Europe and a large east–west bias gradient linked to orography in
Scandinavia. Compared to CNRM-11, the cold biases in mountainous regions are
smaller for RMIB-UGent-11. In summer, again CNRM-11 and RMIB-UGent-11 share
some biases although the difference is larger than in winter, and again the
orographic forcing of the bias of CNRM-11 is more pronounced. Generally we
find a cold bias, except in southern Europe where a warm bias is present.
Figure shows all metrics in separate columns for all
different domains and seasons for seasonal and yearly mean temperature. The
scale is shown at the bottom of each column, the full grey line shows the
“optimal” score of the metric (0 K for BIAS and 95 %-P, 1 for all
others). The grey circles show the scores for the high-resolution K14
ensemble (nine models). For each season and region, two transparent red bands are
superimposed, which show the jackknife 95 % confidence interval for the
high-resolution (top band) and low-resolution (bottom band) simulations with
ALARO-0. The vertical red dashes show the value of sjk(IK14),
again for the high-resolution (top) and low-resolution (bottom) simulation.
When the background colour is white, the RMIB-UGent-11 value of
sjk(IK14) lies within the K14 high-resolution ensemble spread.
If the background colour is yellow, this value lies outside and is “worse”
than the other members of the K14 ensemble. “Worse” means that the
absolute distance from the RMIB-UGent-11 value based on IK14 (top red
dash) to the optimal value (grey line) is larger than that of any other K14
ensemble member. For example, the bias for the Iberian Peninsula in winter
(in short written as BIAS-IP-DJF) is more negative than any other model,
and it is in absolute value the furthest from the optimal 0 K. If instead
the background colour is green, this indicates again the value is outside of
the K14 ensemble but not the furthest from the optimal value. This implies
that either RMIB-UGent-11 outperforms all other models (e.g. RSV-AL-DJF) or
is not the worst model as defined above (e.g. RSV-EA-DJF is outside of the
K14 ensemble, but not as bad as models at the other end of the ensemble).
Overall, Fig. shows that (i) RMIB-UGent-11 mostly falls
within the K14 ensemble (white background colour), (ii) the jackknife
confidence intervals are always much smaller than the total spread of the
K14 ensemble, except for RIAV and TCOIAV where the intervals often cover half
of the ensemble spread, (iii) the difference between the RMIB-UGent-11 (top
red dash) and RMIB-UGent-44 (bottom red dash) scores is very small
considering the total range covered by the ensemble and the calculated
jackknife confidence intervals.
Scores for near-surface air temperature for all domains (first
column), seasons (second column) and metrics.
A more detailed analysis shows that for BIAS, RMIB-UGent is almost always on
the “cold side” of the K14 ensemble and even outside of its range on a
fairly large amount of occasions. Especially for IP-DJF and SC-MAM, the cold
bias is considerable. Also, RMIB-UGent-44 is slightly (∼ 0.2 K) colder
than RMIB-UGent-11, which may be due to regridding and the resolution
difference. For 95 %-P, RMIB-UGent-11 is the worst model on four occasions
among which most notably again are IP-DJF and SC-MAM.
Spatial BIAS of precipitation (%) over the sample IK14
for DJF (left) and JJA (right) for RMIB-UGent-11. Compare to Fig. 3 of
.
For spatial correlation (PACO) and variability (RSV) RMIB-UGent-11 performs
better. Although in K14 these two metrics are plotted on a Taylor diagram, we
choose to show them here separately in one figure for clarity and
conciseness. RSV for RMIB-UGent is almost always larger than 1, even where
other models show less variability (e.g. ME). In the Alpine region (AL),
RMIB-UGent seems to be able to grasp RSV well, but not at the right
locations, as shown by the low PACO, especially in DJF. The jackknife
confidence intervals are very small here, which indicates that both RSV and
PACO produce very robust scores.
For RIAV and TCOIAV, RMIB-UGent again shows acceptable scores, some being outside
of the K14 ensemble in a limited amount of cases. More notably, the jackknife
confidence intervals are relatively large for these scores and this questions
the robustness of these metrics. For example, for FR-MAM the TCOIAV based on
IK14 is 0.6, but the jackknife confidence interval extends from 0.6
to 0.8, covering all but two other models. For RIAV a similar situation for
AL-JJA can be seen.
Precipitation
Figure shows the spatial distribution of the relative
seasonal precipitation BIAS (in %, (model - observed)/observed) for
the winter and summer season for the years in IK14. Comparison to Fig. 3
of K14 shows that in winter, like all other models, RMIB-UGent-11 generally
overestimates precipitation amounts, except in northern Africa. In contrast
to temperature, RMIB-UGent-11 clearly differs from CNRM-11, with the latter
showing large dry biases. In summer, RMIB-UGent-11 overestimates
precipitation amounts, especially in the Mediterranean. Again, no clear
resemblance to CNRM-11 is found.
Scores for precipitation for all domains (first column), seasons
(second column) and metrics.
Figure is constructed in the same way as
Fig. and shows all precipitation scores for all different
metrics, regions and seasons. Similar to the temperature scores, the results
for precipitation reveal that the majority of scores lie within the K14
ensemble, no difference between RMIB-UGent-11 and RMIB-UGent-44 is found and
the jackknife confidence intervals are much smaller than the total ensemble
range except for RIAV and TCOIAV. However, there is a clear absence of yellow
scores and an increased presence of green scores, indicating that RMIB-UGent
precipitation scores are generally better than the temperature scores.
RMIB-UGent has a wet BIAS for almost all regions and seasons. Remarkably, the
best BIAS scores are obtained for SC-MAM and AL-DJF, where large temperature
biases were found. Additionally, the corresponding 95 %-P scores are also
on the low side which shows that the good performance is not due to
compensating biases.
For RSV, RMIB-UGent performs relatively well and for PACO it excels, with 10
out of 80 region–season combinations performing better than the complete K14
ensemble. Only for AL-MAM is its performance not satisfactory, but remark
that the actual score is an extreme outlier considering the jackknife
confidence interval.
For RIAV, RMIB-UGent again performs consistently well, especially compared to
the K14 ensemble which sometimes shows a large overestimation of interannual
variability, i.e. very large values of RIAV. On the other hand, TCOIAV is
mostly on the low side of the K14 ensemble, which shows that although
RMIB-UGent gets the variability right, the actual temporal correlation is not
well grasped. As for temperature, the large jackknife confidence intervals
question the robustness of the scores.
Discussion
This is the first time ALARO-0 was used for a climate experiment.
Nevertheless, the performance of ALARO-0 on seasonal and yearly scales for
both near-surface air temperature and precipitation is satisfactory.
Generally ALARO-0 performs well, which is quantified by the large number of
white boxes in Figs. and indicating that
the ALARO-0 score lies within the existing K14 ensemble. For precipitation,
ALARO-0 even outperforms all other models on numerous occasions. These
results are encouraging, given that ALARO-0 does not yet have the experience
in climate modelling that some of the other models of the K14 ensemble had,
but was directly ported from its NWP setup. Although the 12.5 km resolution
was also a novelty for the K14 models, their performance undoubtedly
benefited from previous optimisations for climate experiments, albeit at a
lower resolution of 50 km.
Some issues still remain. Most notably, this study has revealed some large
temperature biases in Scandinavia and eastern Europe. The spatial pattern of
the BIAS resembles CNRM's ARPEGE model (shown in Fig. 2 of K14). In winter,
the common east–west bias gradient can possibly be attributed to the shared
dynamical core and the strong synoptic scale forcing in winter. In NWP
applications of the ALADIN system similar symptoms have been diagnosed and
have been shown to be related to stable boundary layer issues. The dampened
bias patterns for RMIB-UGent-11 compared to CNRM-11 in the Alps and other
mountainous regions is probably due to the different surface and snow cover
scheme that is used by both. In summer, RMIB-UGent-11 is generally cold
biased, except in southern Europe where it suffers from the common summer
warm bias, probably due to soil moisture feedbacks. Also, the RMIB-UGent-11
and CNRM-11 bias patterns are less alike than in winter, possibly due to the
increased number of local processes that influence and feed back into the
mean fields. Both spatial and temporal variability are very well reproduced
by ALARO-0, while correlations are on the low side compared to other models.
The latter could partly be explained by the comparatively larger domain of
ALARO-0 which could imply a weaker control of the boundary forcing.
For precipitation, ALARO-0 performs very well. Aside from some large wet
biases in summer for the Iberian Peninsula (IP) and the Mediterranean (MD),
biases are almost always below 50 %. Contrary to temperature, the
precipitation bias pattern shows no resemblance to ARPEGE (shown in Fig. 3 of
K14). This can be attributed to the different microphysics and convection
parameterisation schemes that are used by both models. A similar result was
found for the three WRF experiments that were analysed in K14. These only differed in the parameterisation schemes used, but often
covered the complete ensemble spread. Remarkably, in Scandinavia all
precipitation scores are very good, although temperature scores are sometimes
very bad. It is very possible that the two are linked and some compensating
effects or feedbacks exist, which is an additional incentive for a more
thorough study. The good scores for spatial variability (RSV) and correlation
(PACO) show that ALARO-0 is capable of producing not only the right amount of
precipitation but also at the right locations. The common model
overestimation of spatial variability is also present in the RMIB-UGent simulations,
but as stated in K14, this could be due to a smoothing of the reference E-OBS
data set. Temporal variability is very well reproduced, but correlations are
again rather low.
Similarly to the conclusions in K14, no consistent difference between the
low- and high-resolution simulations in the scores is shown. However, based
on preliminary results, we expect that at the sub-daily scale the timing of
precipitation is better represented by the high-resolution simulation.
Finally, it is clear that the period IK14 (1989–2010) used in K14
is sufficient to produce robust scores for BIAS, 95 %-P, RSV, PACO and
partly RIAV. This is quantified by the fact that the jackknife intervals for
these metrics are very small compared to the total ensemble spread and they
therefore do not depend strongly on the period used to compute them. For
example, temperature biases calculated for IK14 are mostly within
0.1 K of the jackknife mean. This does not hold for some RIAV and most of
the TCOIAV scores due to the fact that these exactly assess interannual
variability. For model intercomparison a larger period should be considered
for these scores.
Conclusions
The ALARO-0 model has its origins in the general circulation model ARPEGE and
mainly its limited area model ALADIN. The new microphysics and convection
scheme 3MT was implemented in ALADIN to form ALARO-0, which is used
operationally for daily weather forecasts at the Royal Meteorological
Institute of Belgium (RMIB). In this study, for the first time ever, the
ALARO-0 model was used to perform continuous climate simulations on a
European scale for a 32-year period. Within the framework of the CORDEX
project, one low- and one high-resolution simulation were done on the
EURO-CORDEX domain for the period 1979–2010, using the ERA-Interim
reanalysis as boundary conditions. The results are compared to an existing
ensemble of 19 similar simulations using different models that were analysed
in , referred to as K14 in this text. One of the models used in
K14 is the ARPEGE model by the Centre National de Recherches
Météorologiques (CNRM), which, due to its relation to ALARO-0, serves as a
first reference for the performed simulations.
The main conclusions are that (1) ALARO-0 is able to represent both seasonal mean
near-surface air temperature and accumulated precipitation amounts well and
(2) all scores computed in K14 are robust, except for RIAV and TCOIAV.
The first conclusion is founded by the fact that most of the ALARO-0 scores
lie within the K14 ensemble, thus not performing worse or better than other
models. This is qualified in Figs. and by
a white background. For temperature, some clear cold biases remain, which
will be the subject of a follow-up study. Also, for temperature ALARO-0 seems
to share some large biases with ARPEGE, while for precipitation this is not
the case due to the inclusion of the 3MT scheme in ALARO-0. For
precipitation, ALARO-0 performs very consistently for all scores,
regions and seasons and better on several instances than all other models
in the K14 ensemble.
In the second conclusion, robust means “independent of the
time period used to compute the scores”. The RMIB-UGent simulations span the
32-year period 1979–2010, which is longer than the 20-year period 1989–2008
used in K14. By taking 1000 random 20-year samples from the 32-year pool, we
computed 95 % confidence intervals for all scores.
Figures and show that the confidence
intervals (red transparent bands) are generally much smaller than the total
ensemble spread. Assuming this also holds for other models, this shows that
model differences are significant. For RIAV this does not always hold and a
longer period should be taken into account to compute the scores. For TCOIAV
the situation is even more problematic and scores or model ranking should not
be interpreted too strictly.
The outcomes of this study confirm the potential of ALARO-0 as a climate
model on European scales. Future work will focus on pinpointing the causes of
some of the remaining biases and performing simulations in which ALARO-0 is
driven by a GCM, rather than ERA-Interim.
Acknowledgements
The computational resources and services used in this work were provided by
the VSC (Flemish Supercomputer Center), funded by the Hercules Foundation and
the Flemish Government – department EWI. This work was financially supported
by the Belgian Science Policy (BELSPO) within the ECORISK (SD/RI/06A) and the
CORDEX.be (BR/143/A2) projects. We would also like to thank Sven Kotlarski
and Klaus Keuler for providing the necessary data and the two anonymous
reviewers for their comments and useful suggestions that have improved the
manuscript. Edited by: A. Colette
References
ALADIN international team: The ALADIN project: Mesoscale modelling seen as
a
basic tool for weather forecasting and atmospheric research, WMO Bull., 46,
317–324, 1997.Chan, S. C., Kendon, E. J., Fowler, H. J., Blenkinsop, S., Roberts, N. M.,
and
Ferro, C. A. T.: The Value of High-Resolution Met Office Regional Climate
Models in the Simulation of Multihourly Precipitation Extremes, J.
Climate, 27, 6155–6174, 10.1175/JCLI-D-13-00723.1, 2014.Christensen, J. and Christensen, O.: A summary of the PRUDENCE model
projections of changes in European climate by the end of this century,
Climatic Change, 81, 7–30, 10.1007/s10584-006-9210-7, 2007.Davies, H. C.: A lateral boundary formulation for multi-level prediction
models, Q. J. Roy. Meteor. Soc., 102, 405–418,
10.1002/qj.49710243210, 1976.De Meutter, P., Gerard, L., Smet, G., Hamid, K., Hamdi, R., Degrauwe, D.,
and Termonia, P.: Predicting Small-Scale, Short-Lived Downbursts: Case Study
with the NWP Limited-Area ALARO Model for the Pukkelpop Thunderstorm, Mon.
Weather Rev., 143, 742–756, 10.1175/MWR-D-14-00290.1,
2015.De Troch, R., Hamdi, R., Van de Vyver, H., Geleyn, J.-F., and Termonia, P.:
Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer
Precipitation Climatology in Belgium, J. Climate, 26, 8895–8915,
10.1175/JCLI-D-12-00844.1, 2013.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy,
S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette,
J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut,
J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, 10.1002/qj.828, 2011.Dudhia, J.: A history of mesoscale model development, Asia-Pac. J.
Atmos. Sci., 50, 121–131, 10.1007/s13143-014-0031-8, 2014.
Gerard, L.: An integrated package for subgrid convection, clouds and
precipitation compatible with the meso-gamma scales, Q. J. Roy. Meteor.
Soc., 133, 711–730, 2007.
Gerard, L. and Geleyn, J.-F.: Evolution of a subgrid deep convection
parameterization in a limited area model with increasing resolution, Q.
J. Roy. Meteor. Soc., 131, 2293–2312, 2005.
Gerard, L., Piriou, J.-M., Brožková, R., Geleyn, J.-F., and Banciu,
D.:
Cloud and precipitation parameterization in a meso-gamma-scale operational
weather prediction model, Mon. Weather Rev., 137, 3960–3977, 2009.Giorgi, F. and Mearns, L. O.: Introduction to special section: Regional
Climate
Modeling Revisited, J. Geophys. Res.-Atmos., 104,
6335–6352, 10.1029/98JD02072, 1999.Giorgi, F., Jones, C., and Asrar, G. R.: Addressing climate information needs
at the regional level: the CORDEX framework, WMO Bulletin, 58, 175–183,
2009.
Hamdi, R., Giot, O., Troch, R. D., Deckmyn, A., and Termonia, P.: Future
climate of Brussels and Paris for the 2050s under the {A1B} scenario, Urban
Climate, 12, 160–182, 10.1016/j.uclim.2015.03.003, 2015.Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D.,
and New, M.: A European daily high-resolution gridded data set of surface
temperature and precipitation for 1950–2006, J. Geophys.
Res.-Atmos., 113, D20119, 10.1029/2008JD010201, 2008.Hohenegger, C., Brockhaus, P., and Schär, C.: Towards climate simulations
at
cloud-resolving scales, Meteorol. Z., 17, 383–394,
10.1127/0941-2948/2008/0303, 2008.IPCC: Summary for Policymakers, book section SPM, 1–30, Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA,
10.1017/CBO9781107415324.004, 2013.Kendon, E. J., Roberts, N. M., Senior, C. A., and Roberts, M. J.: Realism of
Rainfall in a Very High-Resolution Regional Climate Model, J.
Climate, 25, 5791–5806, 10.1175/JCLI-D-11-00562.1, 2012.Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué,
M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E.,
Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and
Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard
evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7,
1297–1333, 10.5194/gmd-7-1297-2014, 2014.Lin, J.-L., Qian, T., and Shinoda, T.: Stratocumulus Clouds in Southeastern
Pacific Simulated by Eight CMIP5-CFMIP Global Climate Models, J.
Climate, 27, 3000–3022, 10.1175/JCLI-D-13-00376.1, 2014.
Spiridonov, V., Déqué, M., and Somot, S.: ALADIN-CLIMATE: from the
origins to present date, ALADIN Newsletter, 29, 89–92, 2005.Sun, D.-Z., Yu, Y., and Zhang, T.: Tropical Water Vapor and Cloud Feedbacks
in Climate Models: A Further Assessment Using Coupled Simulations, J.
Climate, 22, 1287–1304, 10.1175/2008JCLI2267.1, 2009.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
10.1175/BAMS-D-11-00094.1, 2011.Teutschbein, C. and Seibert, J.: Regional Climate Models for Hydrological
Impact Studies at the Catchment Scale: A Review of Recent Modeling
Strategies, Geography Compass, 4, 834–860,
10.1111/j.1749-8198.2010.00357.x, 2010.Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V.
D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A.,
Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E.,
Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J.,
Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher,
M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L.,
Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette,
J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth,
K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40
re-analysis, Q. J. Roy. Meteor. Soc., 131,
2961–3012, 10.1256/qj.04.176, 2005.