Low (web) Quality - BALTEX

23
relevant information, considering that RCMs are aimed at
providing climate change projections and their associated
uncertainty (meaning that they should be driven by different
members and various GCMs). It is clear that the sensitivity
of basin annual runoff to driving data frequency on AMNO
is much smaller than the effect of the GCM member,
asserting that sensitivity is within the CRCM’s noise level.
Figure 4 presents sensitivity results over the smaller QC
domain, in the same form as in Figure 3. Here, the blue stars
(from three pairs of 30-year “twin” runs) represent CRCM’s
internal variability at basin scale. It is clear that sensitivity of
basin annual runoff to driving data frequency (black
diamonds; from two pairs) is important. It is not only more
important than the model’s internal variability but it shows a
large systematic difference in the 30-year climate mean at
basin scale. This implies that changing the driving data
frequency on the smaller QC domain alters the simulated
climate. However, the effect of driving data frequency on
the RMSD of annual runoff is not as important as on the
climate means.
signal for basin annual runoff (2041-2070 VS 1961-1990)
(not shown). With the AMNO domain simulations,
climate change becomes more sensitive than would be
expected from a simple extrapolation of its effect on the
climate. On the other hand, over the QC domain, some
compensation effects seem to take place, since the climate
change signal becomes less sensitive than would be
expected from results depicted in Figures 3 and 4.
4. Discussion
An analysis of the effect of the driving data frequency
(12-hourly VS 6-hourly) at the basin scale has provided
interesting information. Over the investigated basins,
sensitivity to driving data frequency varies according to
domain dimension. We know that smaller domains are
more constrained by their driving data and we find that the
smaller QC domain is very sensitive to a change in driving
data frequency, probably because the basins of interest are
located much closer to the western inflow lateral
boundary.
In answer to our original question, this first analysis
indicates that there is potential use for the CRCM4
simulations on the AMNO domain driven by the five
CGCM3 members, even though all members are not
available at a 6-hourly interval. This does not seem
possible with the QC domain because of an important
change in the simulated climate. We must mention that all
simulations were performed with spectral nudging, which
may influence the sensitivity results, particularly over the
smaller QC domain.
Although the question treated here was mostly driven by
our specific operational needs, the general problem of the
impact of a low updating frequency remains open,
especially for simulations with increasing resolution.
References
Figure 3. Annual runoff over the 21 basins of interest
from CRCM’s AMNO domain runs: influence of
driving data frequency (black diamonds) compared to
the effect of CRCM’s internal variability (red stars)
and to the effect of driving GCM’s internal variability
on CRCM run (green squares).
Caya, D. and R. Laprise, A Semi-Implicit Semi-
Lagrangian Regional Climate Model: The Canadian
RCM. Monthly Weather Review, Vol. 127, No. 3,
pp. 341-362, 1999
Denis, B., R. Laprise, and D. Caya, Sensitivity of a
regional climate model to the resolution of the lateral
boundary conditions. Climate Dynamics, Vol. 20,
pp. 107-126, 2003
Frigon, A., M. Slivitzky, B. Music, and D. Caya, Internal
variability of the Canadian RCM’s hydrologic
variables at the basin scale. EGU Gen. Ass., Vienna
(Austria), Geophysical Research Abstracts, Vol. 10,
EGU2008-A-04093, 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, Vol. 8, No. 5, pp. 969-988, 2007
Figure 4. As Figure 3 but from the smaller QC
domain runs. CRCM’s internal variability at basin
scale is represented by the blue stars.
Riette, S., and D. Caya, Sensitivity of short simulations to
the various parameters in the new CRCM spectral
nudging. Research activities in Atmospheric and
Oceanic Modeling, edited by H. Ritchie, WMO/TD -
No. 1105, Report No. 32: 7.39-7.40, 2002
Scinocca, J. F., N. A. McFarlane, M. Lazare, J. Li, and D.
Plummer, The CCCma third generation AGCM and its
extension into the middle atmosphere. Atmos. Chem.
and Phys. Discuss., Vol. 8, pp. 7883-7930, 2008
Finally, we have examined the effect of driving data
frequency (12-hourly VS 6-hourly) on the climate change