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where k represents the number of the category. S varies
between 0 indicating that no overlap exists among the
distributions, and 1, when both distributions are identical.
The S score as calculated from the different data is presented
in Figure 3. In winter season, the CGCM and the CRCM
display similar skill to simulate the frequency and intensity
of observed daily values by showing similar values of the S
score, independently of the region considered (with maybe
the exception of BC and ALTA regions).
In summer, models have more difficulties to reproduce the
observed daily distributions, presenting smaller values of S
than in wintertime. The CGCM shows a better agreement
with observed data than the CRCM and this improvement is
mainly coming from a better simulation of the frequency of
observed dry days (not shown).
DJF
JJA
Figure 3. S score calculated between the simulated and
observed distribution.
4. Heavier precipitation events
We use the 95 th percentile of the distribution to evaluate the
performance of models to reproduce the heavier
precipitation events (see Figure 4). Seasonal 95-percentiles
are estimated from approximately 90 values of precipitation
rate (20 years of daily data).
DJF
Differences of the 95 th percentile values across regions are
generally well simulated by both models in both seasons.
In summer, the CGCM produces generally a better
agreement with observed values than the CRCM. The
latter shows a consistent underestimation of the frequency
of occurrence of heavy precipitation events as compared
with observed frequencies.
5. Summary and discussion
Results of the comparison of daily values of precipitation
as simulated by a regional and a global model suggest that
there is no evidence of the existence of added value at the
scale studied. It is important to emphasise that the
approach employed in this study is based on the
assessment of spatial and temporal scales of precipitation
that are represented by the two models although in a much
coarser way by CGCM since these scales are near its
truncation limit.
Advantages of the RCM simulations due to its higher
spatial-temporal resolution have not yet been explicitly
explored but it will be part of our next step.
Although the failure of the assumption from which added
value should be expected (i.e., that the global model have
little skill at their smallest resolved scales) can account for
the absence of added value, it can not explain that the
CGCM performs better than the CRCM (see for example
the simulation of heavier events in Figure 4). Others
errors, probably specific to the model seem to play an
important role in the simulation of precipitation statistics.
In general, this work highlights the need to further
investigate issues related to added value in RCMs.
Certainly this work must be extended to studies that
explicitly include fine spatial and temporal scale features.
For more practical purposes, as when using RCMsimulated
scenarios in regional impact studies, a
characterization of added value in terms of the variable,
climate statistic, surface forcing, weather regime, etc.
would be of interest.
References
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Giorgi, F., and G.T. Bates: The climatological skill of a
regional model over complex terrain. Mon. Wea. Rev.,
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Laprise, R., Resolved scales and nonlinear interactions in
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779, 2003.
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Laprise, R., R. de Elía, D. Caya, S. Biner, Ph. Lucas-
Picher, E. P. Diaconescu, M. Leduc, A. Alexandru and
L. Separovic, Challenging some tenets of Regional
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2008.
Perkins, S.E., A.J. Pitman, N.J. Holbrook, and J.
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Figure 4. Observed and simulated 95-percentile.