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Sensitivity of a regional climate model to physics parameterizations:
Simulation of summer precipitation over East Asia using MM5
Shi Song 1 and Jian-ping Tang 1,2
1. School of Atmospheric Sciences, Nanjing University, Nanjing, China, 210093
2. Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, China, 210093
1. Introduction
A number of studies address that the uncertainty due to
model parameterizations is one of the major sources of
errors in dynamical downscaling. The concept of ensemble
climate prediction has been raised to alleviate prediction
errors arising from model physical parameterizations
(Krishnamurti et al., 1991). The sensitivity of a regional
climate model (RCM) in modeling summer precipitation
over East Asia to cumulative parameterization schemes
(CUPAs) is significant because the skill of CUPAs has great
effect on the performance of the RCM on the simulation of
the summer precipitation. It has recently been demonstrated
that spectral nudging can effectively incorporate large-scale
regulations inside the RCM domain and thus improve model
performance (von Storch et al. 2000; Feser et al., 2006;
Castro et al., 2005). Therefore, this study is to investigate
(1)how does the downscaling method of spectral nudging
perform over the region of East Asia, especially in the
simulations of summer precipitation over East Asia and
(2)how sensitive is the RCM solution to the choice of
physical parameterization scheme before and after
incorporating spectral nudging method.
2. Model, Experiments and Data
The case examined here is the rainy season (JJA) in East
Asia in the years 1998. Two groups of numerical
experiments are conducted: the control runs (CTLs) which
refer to traditional MM5 simulations without any form of
nudging, and the spectral nudging runs (SNs), which are the
runs with spectral nudging method adopted in MM5. Each
group includes three experiments with different CUPA
schemes: the KF2 scheme, the GR scheme, and the BM
scheme. It is designed to study the sensitivity of the model
to CUPAs. The RCM simulations are evaluated against the
NCEP/NCAR Reanalysis data and NCEP/CPC precipitation
analysis data.
In each group of experiments with different CUPAs, we
calculate the PC between each two members of the three and
then obtain the group average, which indicates the similarity
among three simulation fields with different CUPAs. We
speculate that the spectral nudging can diminish model’s
sensitivity to physical parameterization schemes, and
therefore we expect in all cases the average values of PCs of
SNs are generally larger than those of CTLs.
3. Results
Compared to the experiments without interior nudging,
experiments using interior nudging can reproduce more
realistic large-scale circulations in the upper levels of the
atmosphere as well as near the surface. So we further
investigate the model performance of simulating
precipitations.
Monthly precipitation fields
Figure 1 shows the observed and simulated monthly mean
precipitation fields of August of 1998. There are two
features: (1) the simulation ability are largely improved by
using interior nudging because the results of SN runs are in
more consistence with the observation; (2) the large
discrepancy among CTL runs under different CUPAs are
largely reduced in the SN runs, which is confirmed by the
skill socres in Table 1. Similar results can be seen in the
simulations of other months.
Individual precipitation fields
igure 2 shows the time series of regional-averaged daily
precipitation rates in JJA of 1998 in North China and the
Yangtze River basin. Overall, most results of SNs
markedly outperform those of CTLs: RMSEs of CTLs are
generally reduced by averaging 20~30% in SNs; temporal
correlation coefficients of CTLs are significantly increased
in SNs (skill scores not shown). Furthermore, the
discrepancy between individual runs of SN group is far
less than that of the CTL group.
4. Conclusions
The sensitivity of a regional climate model (RCM) in
modeling summer precipitation over East Asia to
cumulative parameterization schemes are tested using a
regional climate model PSU–NCAR MM5. The effect of
interior (spectral) nudging is also assessed. In conclusion,
this study demonstrates that compared to the experiments
without interior nudging, the results indicate three major
improvements by using interior nudging: (1) the RCM can
reproduce more realistic large-scale circulations in the
upper levels of the atmosphere as well as near the surface;
(2) the precipitation fields in both monthly mean and intraseasonal
(daily) variability are improved; and (3) the
sensitivities of the RCM simulations to the choice of
cumulative parameterization schemes are reduced.
Therefore we argue that the discrepancies of model
simulations using different cumulative parameterization
schemes could be reduced by spectral nudging method, the
multi-CUPA ensemble simulation can demonstrate an
improved skill to some extent, especially in the simulation
of summer precipitation over East Asia.
References
Krishnamurti TN, and Coauthors. 1999: Improved weather
and seasonal climate forecasts from multi-model
superensemble. Science 285:1548–1550
Castro CL, Pielke RA Sr., Leoncini G. 2005: Dynamical
downscaling: Assessment of value retained and added
using the Regional Atmospheric Modeling System
(RAMS), J. Geophys. Res., 110, D05,108
von Storch, H, Feser F. 2000: A spectral nudging
technique for dynamical downscaling purposes. Mon.
Wea. Rev.,128, 3664–3673.
Feser F. 2006: Enhanced Detectability of Added Value in
Limited-Area Model Results Separated into Different
Spatial Scales. Mon. Wea. Rev., 134, 2180–2190.