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47<br />
Evaluation of dynamical downscaling of the East Asian regional climate<br />
using the HadGEM3-RA: Summer monsoon of 1997 and 1998<br />
Johan Lee 1 , Suhee Park 1 , Wilfran Moufouma-Okia 2 , David Hassell 2 , Richard Jones 2 , Hyun-Suk Kang 1 ,<br />
Young-Hwa Byun 1 , and Won-Tae Kwon 1<br />
1 National Institute of Meteorological Research/Korea Meteorological Administration, Seoul, Republic of Korea,<br />
Johan.lee@kma.go.kr, 2 Met Office Hadley Centre, Exeter, UK<br />
1. Introduction<br />
The HadGEM3-RA is a regional atmospheric model based<br />
on the global atmospheric model of the Met Office Hadley<br />
Centre (MOHC). Korea Meteorological Administration<br />
(KMA) has a plan to adopt this model for dynamical<br />
downscaling from seasonal to decadal scales over the East<br />
Asian region according to the collaboration agreement<br />
between MOHC and KMA.<br />
As the first stage of this effort, the performance of the<br />
HadGEM3-RA for the East Asian summer monsoon is<br />
investigated. The East Asian summer monsoon is the most<br />
prominent feature in the East Asian region climate and a<br />
distinct component of the Asian summer monsoon system.<br />
Therefore, it is important to examine the model’s capability<br />
to simulate the East Asian summer monsoon before utilizing<br />
the model for downscaling for longer time scales over the<br />
East Asian region.<br />
In this paper, we will show a preliminary evaluation of its<br />
performance for the East Asian summer monsoon.<br />
2. Experiment design<br />
The model domain is the East Asian monsoon region<br />
including Korean Peninsula, East China, Mongolia, and<br />
Japan. Number of grid points are 190 (in west-east) by 158<br />
(in north-south) and horizontal resolution is selected to be<br />
0.22 degree, about 25 km. The buffer zone for lateral<br />
boundary conditions is 8 grids for each direction.<br />
Configuration of HadGEM3-RA for the East Asian region is<br />
almost the same as that of the HadGEM3-A, except that the<br />
dynamic settings are taken from the operational limited area<br />
model. Dynamics core and physical packages are described<br />
in Davies et al. (2005) and Martin et al. (2006), respectively.<br />
Atmospheric initial conditions were obtained from the<br />
European Centre for Medium-Range Weather Forecasts<br />
(ECMWF) 40-year reanalysis (ERA40) data. Lateral<br />
boundary conditions to drive HadGEM3-RA were derived<br />
from the 6-hourly data. Sea surface temperature and sea ice<br />
were prescribed by Atmospheric Model Inter-comparison<br />
Project (AMIP) II data.<br />
We selected two summer seasons to examine the model’s<br />
capability to simulate the East Asian summer monsoon and<br />
its interannual variation. The model integration periods are 3<br />
months from 0000 UTC 1 June 1997 and 1998, respectively.<br />
Typical characteristics of the East Asian summer are heavy<br />
rainfall from June to July and heat waves from late July to<br />
August. The two selected summers represented extreme<br />
climatic events which were opposite extremes in terms of<br />
precipitation. In summer of 1997, there were drought and<br />
heat waves in North China. In the other hand, there were<br />
abnormal heavy rains in summer of 1998 which had caused<br />
severe flooding in the Yangtze River valley and northeast<br />
China and record-breaking daily precipitations over the<br />
Korean Peninsula. Therefore, the model’s capability to<br />
capture interannual variability of the summer monsoon can<br />
be evaluated by comparing the simulations of 1998 and<br />
1997.<br />
3. Results and discussions<br />
Figure 1 shows observed and simulated precipitations for<br />
summer of 1998. As mentioned above, East Asia<br />
experienced severe heavy rainfall in summer of 1998. In<br />
Fig. 1a, there are intense rainbands over central China<br />
around the Yangtze River basin, the Korean Peninsula,<br />
and Japan. On the contrary the major rainband is weaker<br />
and shifted southward (not shown) in 1997. Therefore, in<br />
the difference between 1998 and 1997, positive anomalies<br />
occur in central China, Korea, and the northwest Pacific<br />
Ocean, and a negative anomaly in south China and Taiwan<br />
(Fig.1b). HadGEM3-RA reproduces well the observed<br />
features of both summers in terms of the distribution of<br />
seasonal precipitation. The pattern correlation coefficients<br />
are 0.53 and 0.61 for 1998 and 1997, respectively, and the<br />
model reproduces well the anomaly from 1998 to 1997<br />
(Table 1). In Fig. 2b, the rainfall deficit over south China<br />
and its surplus across central China and the Korean<br />
Peninsula are distinct as in the observations. However, the<br />
HadGEM3-RA tends to overestimate the intensity of<br />
precipitations. Simulated precipitations of 1998 and 1997<br />
are greater than observations and wet biases are 66.5% and<br />
47.9%, respectively (Table 1).<br />
Simulations of surface air temperature are better than<br />
those of precipitation. The pattern correlations are 0.96 for<br />
both summers (Table 1) with the distribution and<br />
magnitude of surface air temperature very close to<br />
observations (not shown). In addition, the features of 1998<br />
and 1997 anomalies are well captured, e.g. the cold<br />
anomaly in north China and warm anomalies in south<br />
China and the north-eastern region of Asia. The simulated<br />
surface air temperature is generally warmer than<br />
observations with area averaged biases of 0.5 °C and 0.9<br />
°C respectively (Table 1).<br />
Table 1. Comparison of statistical values for<br />
precipitation and surface air temperature of<br />
observation and simulations. In case of biases of<br />
precipitation, the ratio (%) of bias to observation is<br />
presented in parentheses.<br />
Observed<br />
mean<br />
Simulated<br />
mean<br />
Bias<br />
Precipitation Temperature<br />
1998 1997 1998 1997<br />
5.27 4.35 21.0 21.4<br />
8.78 6.44 21.5 22.3<br />
3.51<br />
(66.5)<br />
2.09<br />
(47.9)<br />
0.5 0.9<br />
RMSE 4.32 2.66 1.0 1.3<br />
Pattern<br />
Correlation<br />
0.53 0.63 0.96 0.96