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Assessment of precipitation as simulated by a RCM and its driving data
Alejandro Di Luca 1,3,4 , Ramón de Elía 2,3,4 and René Laprise 1,3,4
1 Université du Québec à Montréal (UQÀM), Montréal (Québec), Canada, diluca@sca.uqam.ca; 2 Consortium Ouranos,
Montréal (Québec), Canada; 3 Canadian Network for Regional Climate Modelling and Diagnostics (CRCMD), Montréal ,
Canada; 4 Centre pour l’Étude et la Simulation du Climat à l’Échelle Régionale (ESCER), Montréal (Québec), Canada
1. Introduction
The primary and most comprehensive tools to study future
climate are the Atmosphere-Ocean General Circulation
Models (AOGCMs). Present horizontal grid intervals of the
atmospheric component of AOGCMs are insufficient to
capture the fine-scale structure of climatic. In this context,
an alternative to obtain future regional climate projections is
the use of high-resolution Regional Climate Models
(RCMs), nested at their lateral boundaries with lowresolution
AOGCMs (Giorgi and Bates, 1989; Laprise et al.,
2008). Because differences between AOGCMs and RCMs
are mainly their resolution, the small scales represent the
main potential added value of the high resolution RCM over
the AOGCM. We say “potential added value” because the
RCM simulation should satisfy several conditions before
this potentiality becomes effective. Among them: the one
way nesting technique must be reliable in the sense that it
should allow a good development of the fine scale with no
amplification of driving fields errors; overall regional
models errors should be smaller or equal than those from the
global model; and, the considered variable must contain
information of fine scales.
In this work we focus on the models specific errors, and in
order to do so we have studied daily precipitation as
simulated by the Canadian RCM (CRCM) and the Canadian
GCM (CGCM).
2. Methodology and data
The methodology used to investigate the presence of added
value in CRCM simulations is based on the assessment of
the RCM performance when compared to its driving model
and observed data. We have evaluated some statistics of
daily precipitation as simulated by both models in several
regions across Canada (see Figure 1) during the period 1971
- 1990.
CGCM cumulative precipitation data was archived at 24-
hour intervals forces to carry the comparison at this time
scale, thus discarding shorter time interval information.
As a result, the considered variable does not explicitly
contain spatio-temporal fine scale information produced
by the CRCM. The hypothesis of the existence of added
value then lies not in the presence of fine scale
information but in the assumption that the global model is
expected to have little skill near its truncation limit
(Laprise, 2003; Feser, 2006).
The global model used in this study is the third generation
of the Canadian Centre for Climate Modelling and
Analysis Coupled Global Climate Model (CGCM3). The
gridded output of precipitation occurs on a 96 by 48
Gaussian grid (output data has a grid spacing of 3.75° in
latitude and longitude).
The CRCM simulations were performed at the Ouranos
Consortium with horizontal grid spacing of 45 km (true at
60° N) over a North American domain with a total of 201
by 193 grid points. Two CRCM simulations were
considered in the present investigation differing only in
the lateral boundary conditions used as nesting data. One
simulation is driven by the CGCM and will be designated
as CRCM (CGCM). The other simulation is nested by the
National Centers for Environmental Prediction (NCEP) -
National Center for Atmospheric Research (NCAR)
reanalyses and will be designated as CRCM (NCEP).
3. Intensity frequency distributions
Intensity frequency distributions are constructed with bin
sizes that vary logarithmically in order to account for the
reduction on the number of events with increasing
intensity. The frequency of each category is calculated
using the following thresholds: 1, 2, 4, 8, 16, 32, 64, 128,
256 and 512 mm/day. As an example, Figure 2 shows the
intensity frequency distributions as observed and
simulated in the BC region for the winter season.
QC
BC
ALTA SAS MAN
Figure 1. Specification of areas of interest. Blue boxes
indicate regions including one CGCM grid point.
A direct comparison between a RCM, a GCM and observed
values can be properly done when quantities are equivalent
for the three sources of data. In this work, the evaluation is
carried out at scales that are greater or equal than that of the
coarser resolution data. In our case, the CGCM defines the
minimum area (i.e., one CGCM grid box) at which perform
the comparison and this forces as to transforms high
resolution data into lower resolution. Upscaling RCM and
observed results to the GCM level is simply done by
computing the spatial-average of all grid-points and stations
data within each GCM grid box. Similarly, the fact that the
Figure 2. Intensity frequency distributions of precipitation
rate in BC for wintertime.
A simple score S (Perkins et al., 2007) that measures the
overlap between simulated (f r mod ) and observed (f r obs )
intensity frequency distributions is defined for each region
r with the aim of obtaining an objective comparison,
S r mod =
9
∑
k=0
( (), f obs r () k )
min f r mod k