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Present climate simulations (1982-2003) of precipitation and surface
temperature using the RegCM3, RSM, and WRF
Yoo-Bin Yhang, Kyo-Sun Lim, E-Hyung Park and Song-You Hong, ybyhang@yonsei.ac.kr
1. Introduction
Interannual variations of precipitation and surface air
temperature are integral parts of the climate system, where
remote controls by planetary circulation and global surface
anomalies act together with local influences by regional
mesoscale surface characteristics. These two broad factors
(remote and local) can be distinguished by using a regional
climate model (RCM), in which planetary signals are
integrated through lateral boundary conditions while
mesoscale impacts are internally resolved (e.g., Giorgi et al.,
1993; Liang et al., 2001). Given the inadequacies of general
circulation models (GCMs) to simulate regional climate
variability, the RCM downscaling has become a powerful
alterative and is widely applied in studies of seasonalinterannual
climate prediction and future climate change
projection.
It is imperative that any RCM must be rigorously validated
in reproducing historical observations, including both mean
climate and temporal variability, before credible application
for climate change projection. In the published literature,
numerous studies have demonstrated the RCM skill
enhancement for downscaling regional characteristics,
especially of precipitation and surface air temperature,
focusing on mean climate (long-term averaged) biases such
as in the annual cycles (Pan et al., 2001; Roads et al., 2003).
In this study, three different RCMs, the Weather Research
and Forecasting model (WRF), National Centers for the
Environmental Prediction (NCEP) Regional Spectral Model
(Juang et al., 1997), and Regional Climate Model version 3
(RegCM3, Pal et al., 2007), are chosen to examine
downscaling skill. The downscaling sckills of RCMs are
compared with the driving reanalysis, against observation of
interannual variations of precipitation and surface
temperature during 1982-2003 over East Asia. This is
facilitated by using empirical orthogonal function (EOF) and
correlation analyses.
2. Model and experimental design
Three regional climate models used in this study are the
RSM, WRF, and RegCM3. The RSM is a primitive equation
model using the sigma-vertical coordinate. The model
includes parameterizations of surface, boundary layer (BL),
and moist processes that account for the physical exchanges
between the land surface, the boundary layer, and the free
atmosphere. The RegCM3 is a primitive equation,
compressible, sigma-vertical coordinate, and limited area
model of which the dynamical core is based on the
hydrostatic version of the fifth-generation Penn State
University-National Center for Atmospheric Research (PSU-
NCAR) Mesoscale Model (MM5; Grell et al. 1994). The
Advanced Research WRF (ARW; Skamarock et al. 2005) is
a community model suitable for both research and
forecasting.
Three-month-long simulations for the summer season (June-
July-August; JJA) were performed for 22 years from 1982 to
2003. The model grids consist of 109 (west-east) by 86
(north-south) grid lines at 60 km horizontal separation with
the polar stereographic map projection. The model
configurations used for each model are summarized in Table
1. The simulations are performed with reanalyses-derived
boundary forcing.
RegCM3 RSM WRF
vertical σ-23 layers σ-28 layers σ-28 layers
levels
dynamics hydrostatic hydrostatic nonhydrostatic
numerics finite
difference
spectral
computation
finite
difference
CPS
Grell
SAS 2005 Kain-Fritsch
(Byun and Hong, (Kain and Fritsch,
(Grell, 1993)
2007)
1993)
PBL
LSM
RAD
Holtslag
(Holtslag ,1990)
BATS
(Dickinson et al.,
1986)
CCM3
(Kiehl, 1998)
YSUPBL
(Hong et al., 2006)
OSU
(Mahrt and Pan,
1984)
GSFC
(Chou and Suarez,
1999)
YSUPBL
(Hong et al., 2006)
Noah
(Chen and Dudhia,
2001)
simple cloudinteractive
(Dudhia, 1989),
RRTM
(Mlawer et al.,
1997)
3. Dominant interannual variation patterns
Figure 1. The first EOF mode and corresponding
principal component of surface temperature.
Figure 1 shows the first EOF mode of summer
temperature and the corresponding principal component
(PC) reanalysis data and simulated by the RegCM3, RSM,
and WRF, where summer is defined as the average of JJA.
The first dominant mode of the reanalysis explains 36% of
the total variance in the model domain, which is
characterized by positive values over the whole domain.