Annual Report 2012 - City University of Hong Kong

C4. Regional climate simulations of summer diurnal rainfall variations over East Asia and
Southeast China
(PI: Johnny CHAN)
In this study, the evaluation focuses on the sensitivity of the choice of
cumulus parameterizations and model domain. With the right setup, the
spatial and temporal evolution of diurnal rainfall over Southeast China, which
has not been well simulated by past studies, can be accurately simulated
by RegCM3. Results show that the Emanuel cumulus scheme has a more
realistic simulation of summer mean rainfall in East Asia, while the GFC (Grell
scheme with the Frisch-Chappell convective closure assumption) scheme
is better in simulating the diurnal variations of rainfall over Southeast China
(Table 2). The better performance of these two schemes [relative to the other
two schemes in RegCM3: the Kuo scheme and the GAS (Grell scheme with
the Arakawa–Schubert closure assumption) scheme] can be attributed to
the reasonable reproduction of the major formation mechanism of rainfall —
the moisture flux convergence — over Southeast China. Furthermore, when
the simulation domain covers the entire Tibetan Plateau, the diurnal variations of rainfall over Southeast China are
found to exhibit a noticeable improvement without changes in the physics schemes.
Table 2. Selected statistical variables for measuring the ability of model in simulating different components of
precipitation formation over the domain of SEC.
Comp. Field
Obs EMU1 GFC1 GAS1 AK1 EMU2 GFC2 GAS2 AK2
P Value (unit: mmh-1) –
0.39 0.37 0.31 0.22 0.48 0.43 0.19 0.31 0.30
Scorr 0.52 0.44 0.35 0.46 0.41 0.28 0.32 0.38
RMSE (unit: mmh -1) 0.09 0.11 0.15 0.14 0.11 0.22 0.18 0.16
ΔP Tcorr 0.82 0.90 0.15 0.43 0.61 0.76 0.11 0.38
Scorr at 09 UTC 0.54 0.91 0.12 -0.07 0.47 0.62 0.05 -0.03
RMSE at 09 UTC (unit: mmh -1) 0.28 0.14 0.47 0.45 0.46 0.41 0.60 0.59
S1 Var[S1(P)]/Var[ΔP] (unit: %) 64.8 75.1 69.7 44.9 73.5 72.4 57.7 61.6 72.7
Ampitude (unit: mmh -1) 0.19 0.16 0.20 0.02 0.08 0.22 0.17 0.03 0.09
Tcorr 0.87 0.93 -0.54 -0.10 0.66 0.78 -0.15 -0.34
Scorr at 09 UTC 0.49 0.93 -0.61 -0.14 0.33 0.86 -0.14 -0.02
RMSE at 09UTC (unit: mmh -1) 0.24 0.12 0.41 0.43 0.35 0.32 0.42 0.45
S2 Var[S2(P)]/Var[ΔP] (unit: %) 31.1 18.9 26.2 48.1 21.4 24.3 30.3 28.5 21.2
Ampitude (unit: mmh -1) 0.13 0.07 0.12 0.01 0.05 0.08 0.11 0.01 0.04
Tcorr 0.94 0.90 0.82 0.91 0.68 0.73 0.62 0.69
Scorr at 09 UTC 0.63 0.84 0.65 0.64 0.54 0.65 0.51 0.52
RMSE at 09 UTC (unit: mmh -1) 0.03 0.02 0.04 0.03 0.11 0.09 0.18 0.14
Note: The smallest value of RMSE and the Scorr, as well as Tcorr (temporal correlation), exceeding the 95% confidence
–
level is in bold. The spatial pattern of P, S1(P) at 09 UTC and S2(P) at 09 UTC is referred to Fig. 8, Fig. 9 and Fig. 10 in
Huang et al. (2012) respectively.
Obs vs. Simulations
This study evaluates the
capability of RegCM3 in
simulating the summer
precipitation over East
Asia, with focus on diurnal
variations of precipitation
over Southeast China during
the 1998-2002 summer
seasons.
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