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Evaluation of the analyzed large-scale features in a global data assimilation
system due to different convective parameterization scheme and their
impact on downscaled climatology using a RCM
Jung-Eun Kim and Song-You Hong
Department of Atmospheric Sciences, Yonsei University, Seoul, Korea, japril@yonsei.ac.kr
1. Introduction
Despite the successful application of the RCMs to
dynamical downscaling for climate change assessement and
seasonal climate predictions, the regional predictability and
the evaluation of added values to the GCM outputs are still
not clarified. As reviewed by Giorgi et al. (2001), Leung et
al. (2003), and Wang et al. (2004), the errors in the
downscaled regional climate within a nested RCM result
from the 1) uncertainties in the large-scale fields driven by
GCM and the related unphysical treatment of the lateral
boundary conditions, 2) inaccuracies in the physics and
dynamics in the RCM, and 3) inconsistency between the
regional and global models in dynamics and physics. The
treatment of the lateral boundary conditions, the physics and
dynamics in the RCM has significantly been improved by
the RCM community. However, the first and fourth issues
are still open to the questions.
The skill of an RCM in dynamical downscaling applications
is highly dependent upon the skill of the driving GCM.
However, substantial differences among several reanalysis
datasets, in particular, in the lower-atmospheric circulations
and water vapor flux, lead to another complexity in
improving the RCM (Annamalai et al. 1999). The
consistency between the GCM and RCM is even difficulty
issue to explore since the physics package between the two
models is usually not consistent. These two issues are rather
clear in future development of the RCM, but the uncertainty
due to the inconsistency in the internal forcing due to
physical parameterizations has to be explored to clarify the
RCM’s predictability. Our study aims to explore the impact
on the regional downscaling embedded within large-scale
climate information due to different convective
parameterization scheme
2. Experimental Design
The predicted large-scale features are obtained by the
perfect large-scale experiments runs (GDAS) that are forced
by an analyzed data. The National Centers for
Environmental Prediction (NCEP) regional spectral model
(RSM) is used in this study for downscaling. A detailed
model description is provided by Juang et al. (1997). To
discuss the uncertainty due to the inconsistency of different
cumulus convective parameterization scheme, two
sensitivity experiments are conducted; the simplified
Arakawa-Schubert (SAS; Hong and Pan 1998) and
community-climate model (CCM; Zhang and McPhalane
1995) schemes. The summer of 2004 was selected in this
study, which recorded a near-normal seasonal precipitation
in East Asia (Fig.1a).
3. Result
Figures 1b and 1c show the JJA precipitation from the CCM
and SAS experiments in GDAS, respectively. It is seen that
both runs reproduce the observed precipitation well. Over
land, the local maxima in central China, Korea, and southern
Japan, are commonly captured, irrespective of the
convection scheme in the GDAS. Oceanic precipitation
over the sub-tropics is fairly well simulated. Precipitation
in Mongolia and Siberia is excessive when the CCM
scheme is used in the GDAS run. The pattern correlation
of JJA precipitation is 0.52 and 0.70, for CCM and SAS
experiments, respectively.
(b) Gccm
(a) GPCP
(c) Gsas
Figure 1. (a) Observed JJA accumulated rainfall
from GPCP and the simulated precipitation (mm)
from (b) CCM and (c) SAS runs in GDAS.
(a) Gc_Rc
(c) Gs_Rc
(b) Gc_Rs
(d) Gs_Rs
Figure 2. Simulated 3-month (JJA) accumulated
precipitation (mm) from RCM experiments forced
by GDAS.
Figure 2 shows that the distribution pattern of downscaled
precipitation using the RCM depends more on the
convection scheme in the RCM, rather than that used in
the mother-domain experiments. An excessive
precipitation over the southern China and East China Sea
regions is commonly observed when the CCM scheme in