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Overview of the research project of Multi-model ensembles and downscaling
methods for assessment of climate change impact, supported by
MoE Japan
Izuru Takayabu
Meteorological Research Institute, 1-1 Nagamine Tsukuba Ibaraki 305-0052 JAPAN; takayabu@mri-jma.go.jp
1. Introduction
With a strong determination to fill in the gap between the
result of climate prediction by the whole atmospheric model
and the study of impact statement, we started the project S-
5-3 supported by Ministry of the Environment, Japan. Now
we are in the third year of it. Unfortunately we haven’t
made yet a specific formation of the big system that we had
in mind when we applied it for permission in figure 1.
However, each part of it has been making its clear
formation, thanks to the effort made by each participating
institution. The accomplishment report for the last two
years is outlined as follows.
We also have a two way nesting system. Inatsu and
Kimoto (2009) have succeeded to drive their two way
nesting model around the Japan Islands, and simulate the
effect of its method of nesting on the northern hemisphere
storm track activity.
By comparing these valuable methods of driving RCM,
we try to reduce the model uncertainty.
Fig.2: The requested area of calculation in this project.
Fig.1: Summary of the project
2. To Reduce the model uncertainty
To reduce the uncertainty of the RCMs result, we integrate
many 20km RCMs parallel over the field of Japan Islands
(Fig. 2). Three institutions and universities (Natiolnal
Institute for Earth science and Disaster prevention (NIED),
University of Tsukuba and Meteorological Research
Institute (MRI)) integrated their 20km resolutions regional
climate models for three years (2002 – 2004), by using JRA-
25 (reanalysis data) as the boundary forcing data. They are
NIED-RAMS (Dairaku et al., 2009), TERC-RAMS
(University of Tsukuba), MRI-NHM (Sasaki et al., 2007)
and MRI RCM (Murazaki et al., 2008). With the
cooperation of Tanaka (2008, personal communication), we
have made advances in our studies of bias specification and
model tuning, as shown in figure 3. Using the calculational
result from them, Tanaka (2008, personal communication)
has started to study a bias correction method of precipitation
and surface air temperature, as shown in figure 4.
We also apply pseudo global warming method (PGWM) and
do the integration of the WRF model. Hara et al. (2008)
succeeded to simulate the snow depth pattern around Japan
Islands caused by the Asian winter monsoon. Kawase et al.
(2009) succeeded to simulate the Meiyu-Baiu forntal
rainbands appearing at the Asian summer monsoon season
with the lateral boundary forcing by the ensemble results of
CMIP3 AO-GCMs.
Fig.3: Monthly mean surface temperature bias vs. monthly
accumulated precipitation bias. Red: NHM of MRI, blue:
RAMS of NIED, green: RAMS of Tsukuba University,
black: RCM of MRI. All but RCM of MRI are nonhydrostatic
model. These dots are drawn every month for
each prefecture in Japan. The observation data comes
from the AWS network of Japan Meteorological Agency
(AMeDAS). PDFs are drawn on the top and right hand
side of the figure.
3. To bridge the results to the impact research
In the area of the dynamic downscaling which is no less
important than the statistical downscaling, the study about
the city area was developed by using urban canopy
schemes (University of Tsukuba , MRI and DPRI). At
MRI (Aoyagi et al., 2009), the regional climate model was
put together with an urban canopy scheme and an