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In order to simplify the display, the identity of each
simulation will not be indicated neither the model version,
nor its role as either control or perturbed run.
Figure 2a depicts the projected climate change for winter
temperature. The first thing to note is that for all
experiments realized temperature increases range between 4
and 6°C. CRCM internal variability seems to be a minor
source of uncertainty at regional scale, while the nesting
interval also seems to have a small impact. As expected,
changes in the driving GCM has a larger impact, but
curiously not as large as the change in GCM driving
member (we believe that a sampling problem is responsible
for this: if more driving data were available for different
models, changes in GCMs should have a larger impact than
a change in GCM members).
The spread caused by sensitivity to the driving member is
the largest of all. It is important to point out that this spread
could be considered as the minimum amount of uncertainty
associated to a climate change projection. This is because
even a perfect RCM driven by a perfect GCM will show
dependence on the driving member. Winter temperature
shows a weak sensitivity to the CRCM version used and the
same can be said of domain size.
Figure 2b depicts the climate change projection for summer
precipitation (in percentage). Overall results suggest that an
increase of precipitation between 0% and 10% should be
expected for this region. As for the case of winter
temperature, variation in the driving GCM has the greatest
impact through either a change in member or change in
model.
Internal variability has a minor role at the regional scale,
although it is non negligible at the grid point scale (not
shown). The impact of the nesting interval, although
considerably larger than that of internal variability, is small
compared to other sensitivities.
It can also be seen that a change in the CRCM version has
an impact comparable to that of variations in the driving
GCM. This result agrees with previous work that finds
summer precipitation to be particularly dependent on the
regional model used to perform the dynamical downscaling.
Domain size is shown to be a non-negligible source of
uncertainty, as was also found in previous studies.
5. Conclusions
In previous experiments (de Elia et al. 2008) we have
studied the sensitivity of RCM-simulated climate to
parameter perturbation. In the present work, we have
extended the research to include the sensitivity of the
climate change projection to parameter perturbation. These
studies account for more than 30 40-year long simulations.
Despite this effort, there still remains a lot of work to do, as
many more sensitivity experiments are needed.
Based on experiments to date we can say that issues related
to the driving data are the most important, with the
exception of summer precipitation where RCM perturbation
plays an important role.
This conclusion could be interpreted in two different ways:
From one point of view it is a positive result, since it tells us
that RCMs are trustworthy tools that do not add too much
noise to the driving large scale information. (too much
sensitivity to parameter perturbation will be a cause of
concern). But it is important to remember that for the case of
sensitivity to domain size, this is not the product of chance:
effort and resources were invested in the development of
spectral nudging in order to alleviate this sensitivity.
From another point of view, it could be argued –-and it is a
common statement especially in the GCM community--that
the dominance of the driving GCM as a source of
uncertainty is an indication that RCMs play a minor role in
climate downscaling (except for variables dominated by
small-scales processes such as summer precipitation).
These two opposing points of view indicate the intricate
relation in climate downscaling between questions of
uncertainty and those of RCMs potential added value.
Both these issues are fundamental in the development of
RCMs and deserve unrelenting attention.
Figure 2. Climate change projections for the seasonal
average of the region presented in Fig. 1. Type of
sensitivity experiment is defined in the abscissa. Panel a
depicts winter temperature (in °C), and panel b summer
precipitation (in %).
References
a) Winter temperature (°C)
Initial
conditions
Nesting
interval
Driving
GCM
GCM
member
CRCM
version
Domain
size
b) Summer precipitation (%)
Initial
conditions
Nesting
interval
Driving
GCM
GCM
member
CRCM
version
Domain
size
De Elia, R. and co-authors, Evaluation of uncertainties in
the CRCM-simulated North American climate,
Climate Dynamics, 30, pp. 113-132, 2008.
Music B, and D Caya, Evaluation of the Hydrological
Cycle over the Mississippi River Basin as Simulated
by the Canadian Regional Climate Model (CRCM). J.
Hydrometeorology, 8, pp. 969-988, 2007.