Asia-Pacific J. Atmos. Sci., 49(4), 401-407, 2013 DOI:10.1007/s13143-013-0037-7

Torrential Rainfall Responses to Radiation and Ice Clouds over Jiang-Huai Valley, China in July 2007 Fengwen Xu1, Xiaofeng Xu2, Xiaopeng Cui3, and Guoping Zhang4 1

Public Meteorological Service Centre of China Meteorological Administration, Beijing, China China Meteorological Administration, Beijing, China 3 Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 4 Public Meteorological Service Centre of China Meteorological Administration, Beijing, China 2

(Manuscript received 13 September 2012; revised 20 November 2012; accepted 27 November 2012) © The Korean Meteorological Society and Springer 2013

Abstract: The effects of radiation and ice clouds on a torrential rainfall event of Jiang-Huai Valley over China are investigated through a series of two-dimensional sensitivity cloud-resolving model experiments. The model is integrated with an imposed large-scale vertical velocity and zonal wind constructed from the National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) from 2 to 9 July 2007, while the control experiment is compared with two sensitivity experiments that exclude radiation and ice clouds, respectively. The exclusion of ice clouds decreases model domain mean surface rain rate through the weakened mean net condensation and the mean hydrometeor change from loss to gain during the life span of the rainfall event. The sensitivity of the mean rain rate to radiation occurs only in the later period of the rainfall event and is less than that to ice clouds. The reduction in the mean rain rate caused by the elimination of radiation is associated with the decreases in the mean net condensation and latent heat, which corresponds to the strengthened mean local atmospheric cooling. Key words: Radiation, ice clouds, rain rate, cloud-resolving model

1. Introduction Torrential rainfall occurs over Jiang-Huai Valley of eastern China each year and often causes tremendous economic and life loss. For instance, the economic losses associated with flooding are 122 billion Chinese Yuan in 1991 and 200 billion Chinese Yuan in 1998. Thus, accurate forecast of torrential rainfall events is needed by government for decision making and associated studied has been paid much attention by Chinese meteorologists. Physical processes associated with the development of torrential rainfall over Jiang-Huai Valley have been studied at multi temporal and spatial scales (e.g., Zhou et al., 1984; Ding and Zhu, 1993; Tao et al., 1996; Zhai et al., 1997; Gao et al., 2002; Sun and Zhao, 2002). Radiation and ice clouds may play important roles in the development of torrential rainfall over Jiang-Huai Valley. Radiation directly changes air temperature. Solar radiative Corresponding Author: Fengwen Xu, National Meteorological Center of China, No. 46, Zhongguancun South Street, Haidian District, Beijing, China. E-mail: [email protected]

heating increases temperature and saturation mixing ratio during the daytime and leads to the decrease in condensation and precipitation. Infrared radiative cooling decreases temperature and satuation mixing ratio during the nighttime and increases condensation and precipitation (Fu et al., 1995; Xu and Randall, 1995; Tao et al., 1996; Sui et al., 1997, 1998). The exclusion of radiation enhances infrared radiative cooling. Correspondingly, latent heat is increased to produce more rainfall when large-scale forcing is absent (Gao and Li, 2008a). In the presence of large-scale forcing, the increase in infrared cooling caused by the removal of radiation may be largely offset or overcome by the decrease in heat divergence, which may slow down the increase in latent heat and rainfall or may lead to the decreases in latent heat and rainfall (Wang et al., 2010a; Shen et al., 2011a,b; Yue and Shao, 2011; Zhou, 2011). Ice clouds may have important impacts on precipitation through the change in net condensation (e.g., McCumber et al., 1991; Grabowski, 2001; Wu, 2002). The exclusion of ice clouds decreases net condensation and rainfall (Gao et al., 2006; Ping et al., 2007; Wang et al., 2010b; Wang et al., 2010c; Yue and Shao, 2011; Zhou, 2011). In this study, effects of radiation and ice clouds on a torrential rainfall of Jiang-Huai Valley, China during July 2007 are investigated through a series of two dimensional (2D) sensitivity cloud-resolving model simulations. The model, experiment designs, forcing data, and convective-stratiform rainfall separation scheme are discussed in the next section. The results will be presented in section 3. The summary is given in section 4.

2. Model and experimental design The cloud-resolving model was originally developed by Soong and Ogura (1980), Soong and Tao (1980), and Tao and Simpson (1993) and its 2D version was modified by Sui et al. (1994, 1998) and Li et al. (1999), which is used in this study. Due to a small model domain in cloud-resolving model, the large-scale forcing is imposed in the model. The model with cyclic lateral boundaries (Gao and Li, 2008a) contains prognostic equations for potential temperature, specific humidity, per-

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Fig. 1. Time-pressure cross sections of (a) vertical velocity (cm s−1) and (b) zonal wind (m s−1) from 2000 LST 2 July - 1400 LST 9 July 2007. The data are averaged in a rectangular box of 112-120oE, 33-34oN.

turbation zonal and vertical momentum, as well as the mixing ratios of five cloud species. The model uses the cloud microphysical parameterization schemes from Lin et al. (1983), Rutledge and Hobbs (1983, 1984), Tao et al. (1989), and Krueger et al. (1995) and radiation parameterization schemes from Chou et al. (1991, 1998) and Chou and Suarez (1994). The model parameters include a horizontal domain of 768 km, a horizontal grid resolution of 1.5 km, 33 vertical levels, and a time step of 12 s. The 2D cloud-resolving model with this model setup has successfully simulated various rainfall events including the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE) (Li et al., 1999), the South China Sea Monsoon Experiment (SCSMEX) (Wang et al., 2007), and torrential rainfall events over China (e.g., Xu et al., 2007; Wang et al., 2009; Yue et al., 2009; Wang et al., 2010c; Zhou, 2011). In the control experiment (C), the model is integrated from 2000 LST 2 July to 1400 LST 9 July 2007 with forcing data averaged over a rectangular box of 112-120oE, 33-34oN, where maximum zonally-oriented rainfall band occurred. The forcing data including vertical velocity, zonal wind, and horizontal temperature and vapor advection are constructed from NCEP/ GDAS data. The large-scale vertical velocity shows maximum upward motions on 3, 5, 7, and 8 July (Fig. 1a). The largescale zonal wind shows that the westerly wind increases as the height increases on 3-4 and 7-9 July (Fig. 1b), whereas the westerly wind reaches its maximum in the mid troposphere on 5-6 July. Simulated rain rate basically captures observed rain rate (Fig. 2). The major differences are that observed rainfall peak on 6 July is missing in the simulation because of weak imposed large-scale upward motions and that the simulated rainfall is much stronger than the observed rainfall on 8 July

Fig. 2. Time series of domain-mean simulated (solid) rain rate in C and observed (dashed) rain rate. Unit is mm h−1.

because of stronger imposed large-scale upward motions in the upper troposphere. Since radiation and ice clouds may impact torrential rainfall, two sensitivity experiments that exclude radiation (CNCR) and ice clouds (CNIM) are conducted and compared with the control experiment in this study. The mixing ratios of five cloud species are set to zero in the calculation of radiation in CNCR. The mixing ratios of three ice species and associated microphysical rates are set to zero in CNIM. Rainfall evolution in Fig. 2 shows three basic phases: phase 1 (0000-1900 LST 3 July), phase 2 (0800 LST 4 July -1300 LST 7 July), and phase 3 (0500 LST 8 July - 1000 LST 9 July). Thus, three phases are analyzed in the following discussions. Surface rainfall is separated into convective and stratiform components through the rainfall partitioning scheme (Tao et

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al., 1993; Sui et al., 1994), in which a model grid point is identified as convective if a rain rate at this grid point is twice larger than the average taken over the surrounding four grid points (two grid points on either side of this grid point in 2D framework) or if a rain rate at this grid point is higher than 20 mm h−1. Additional information about cloud hydrometeor mixing ratio and vertical velocity is also used to further identify convective grid points in raining stratiform regions. All nonconvective cloudy grid points are considered to be stratiform.

derived by Gao et al. (2005), and Cui and Li (2006), can be symbolically expressed by Ps = QWVT + QWVF + QWVE + QCM

(1)

where ∂ [ qv ] QWVT = –----------∂t

(1a)

o

3. Results

∂ ( u′qv ′ ) w′∂q o ∂q o ∂q o ∂qv ′ o ∂qv ′ QWVF = – u -------v- – w -------v- – ------------------ – u --------- – w --------- – -------------v∂x ∂z ∂z ∂x ∂x ∂z

(1b)

a. Model domain mean analysis The largest changes in model domain mean surface rain rate in all three phases are the decrease in the mean rain rate caused by the exclusion of ice clouds (−26.9% in phase 1, −10.4% in phase 2, and −26.6% in phase 3) (Fig. 3). The decrease in the mean rain rate resulting from the elimination of radiation comes to the second (−14.7%). The surface rainfall budget in the mass-integrated form,

QWVE = Es

(1c)

∂ [ q5 ] ∂ ( uq5 ) - – --------------QCM = –----------∂t ∂x

(1d)

Here, qv is specific humidity; u and w are zonal and vertical wind components, respectively; Es is surface evaporation rate; q5 = qc + qr + qi + qs + qg, qc, qr, qi, qs, qg, are the mixing ratios of cloud water, raindrops, cloud ice, snow, and graupel, respectively; Overbar denotes a domain-mean; Prime is a perturbation from domain mean; [ ] is a mass integration; and superscript o is an imposed NCEP/GDAS value. Surface rainfall budget (1) states that surface rain rate is determined by local vapor change (QWVT), water vapor convergence (QWVF), surface evaporation (QWVE) and hydrometeor change/convergence (QCM). Note that surface rainfall budget is derived from water vapor budget QWVT + QWVF + QWVE = QCOND

(2)

and cloud budget Ps = QCOND + QCM

(3)

where QCOND = [PCND] + [PDEP] + [PSDEP] + [PGDEP] − [PREVP] − [PMLTG] − [PMLTS]

Fig. 3. Model domain means of surface rain rate (Ps), local water vapor change (QWVT), water vapor convergence (QWVF), surface evaporation (QWVE), hydrometeor change/hydrometeor convergence (QCM), and net condensation (QCOND) in the control experiment (C) in (a), and their differences for (b) CNCR-C and (c) CNIM-C averaged during the periods of 0000-1900 LST 3 July (phase 1; open bar), (b) 0800 LST 4 July - 1300 LST 7 July (phase 2; black bar), and (c) 0500 LST 8 July 2007 - 1000 LST 9 July (phase 3; grey bar). Unit is mm h−1.

Here QCOND is a net condensation (e.g., Gao and Li 2008b). ([PCND] + [PDEP] + [PSDEP] + [PGDEP]) represents water vapor sink term and cloud source term that consist of vapor condensation rate ([PCND]), vapor deposition rates for the growth of cloud ice ([PDEP]), snow ([PSDEP]) and graupel ([PGDEP]). (−[PREVP] − [PMLTG] − [PMLTS] denotes water vapor source term and cloud sink term that include growth of vapor by evaporation of raindrop ([PREVP]), evaporation of liquid from graupel surface ([PMLTG]), and evaporation of melting snow ([PMLTS]). The decreases in the mean rain rate caused by the exclusion of ice clouds are larger in phases 1 and 3 than in phase 2. The decrease in the mean rain rate can be directly seen from the mean cloud budget. Thus, the decrease in the mean rain rate could come from the decrease in the mean net condensation

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and the mean hydrometeor change from loss (QCM > 0) to gain (QCM < 0) (Fig. 3), which are similar to what found in Wang et al. (2010b) and Zhou (2011). Wang et al. (2010c) and Yue and Shao (2011) showed that the exclusion of ice clouds decreases the mean rain rate through the mean hydrometeor change from loss to gain during the onset phase of pre-summer heavy rainfall event and during the landfall of Typhoon Krosa on 6 October 2007, whereas it increases the mean rain rate through the increase in the mean net condensation during the decay phase of pre-summer heavy rainfall event. The decrease in the mean net condensation and the mean hydrometeor change from loss in C to gain in CNIM are larger in phases 1 and 3 than in phase 2. The removal of ice clouds decreases the mean water vapor convergence (QWVF > 0) in phases 1 and 2 and changes the mean local atmosphere from drying (QWVT > 0) to moistening (QWVT < 0) in phase 1 and increases the mean local atmospheric moistening in phase 2. In phase 3, the exclusion of ice clouds increases the mean water vapor convergence, whereas it decreases the mean local atmospheric drying. The model domain and mass-weighted mean heat budget can be expressed as SHT + SHF + SHS + SLH + SRAD = 0

(4)

where ∂ SHT = –----------------∂t

(4a)

o

o ∂T o ( u′T′-)〉 – 〈 uo ------∂T′-〉 SHF = – 〈 u -------〉 – 〈 πw ∂θ ------〉 – 〈 ∂---------------∂x ∂z ∂x ∂x o – 〈 πw ∂θ′ -------〉 – 〈 πw′∂θ ------〉 ∂z ∂z

(4b)

SHS = HS

(4c)

1- 〈 Q 〉 SLH = --cn cp

(4d)

1- 〈 Q 〉 SRAD = --R cp

(4e)

Here, T and θ are air temperature and potential temperature, respectively, cp is the specific heat of dry air at constant pressure, Hs is surface sensible heat flux, Qcn is the net latent heat release through phase changes among different cloud species, QR is the radiative heating rate due to the convergence of net flux of solar and infrared radiative fluxes, and <0> = z z ∫ z ρ( )dz ⁄ ∫ z ρdz . Heat budget (4) states that the local heat change (SHT) is determined by heat divergence (SHF), surface sensible heat flux (SHS), latent heat release (SLH) and radiation (SRAD) (Gao and Li 2008b). Model domain mean heat budgets reveal that the decreases in the mean net condensation caused by the exclusion of ice clouds lead to the decreases in the mean latent heat in all three phases, which slow down the mean heat divergence (SHF < 0) in three phases and enhance the mean local atmospheric cooling t

t

b

b

Fig. 4. Model domain means of radiative heating (SRAD), local heat change (SHT), heat convergence (SHF), surface sensible flux (SHS), and latent heat (SLH) in the control experiment (C) in (a), and their differences for (b) CNCR-C and (c) CNIM-C averaged during the periods of 0000-1900 LST 3 July (phase 1; open bar), (b) 0800 LST 4 July - 1300 LST 7 July (phase 2; black bar), and (c) 0500 LST 8 July 2007 - 1000 LST 9 July (phase 3; grey bar). Unit is oC h−1.

(SHT > 0) in phases 1 and 3 and suppress the local atmospheric cooling in phase 2 (Fig. 4). As shown in Fig. 4, the removal of radiation enhances the mean radiative cooling in three phases. The increase in the mean latent heat corresponds to the increase in the mean radiative cooling only in phase 1 because the mean heat divergence is barely changed. The increase in the mean latent heat has been found in Yue and Shao (2011) and Zhou (2011). The increase in the mean radiative cooling also leads to the increase in the mean local atmospheric cooling in phase 1. The mean latent heat is decreased in phases 2 and 3, which is associated with the weakened mean heat divergence and the enhanced mean local atmospheric cooling. Similar decreases in the mean latent heat have also been observed during the landfall of severe tropical storm Bilis on 16 July 2006 (Wang et al., 2010a) and during the mature phase of pre-summer heavy rainfall event (Shen et al., 2011). The increase in the mean net condensation associated with the increase in the mean latent heat leads to the small increase in the mean rain rate in phase 1 (Fig. 3). The small decrease in the mean latent heat barely changes the mean net condensation and the mean rain rate in phase 2. In phase 3, the decrease in the mean rain

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Fig. 5. Contributions of convective regions to model domain mean urface rain rate (PS), local water vapor change (QWVT), water vapor convergence (QWVF), surface evaporation (QWVE), local hydrometeor change/hydrometeor convergence (QCM) averaged during the periods of 0000-1900 LST 3 July (phase 1; open bar), (b) 0800 LST 4 July 1300 LST 7 July (phase 2; black bar), and (c) 0500 LST 8 July 2007 1000 LST 9 July (phase 3; grey bar). Unit is mm h−1.

rate is related to the decrease in the mean net condensation and the slowdown in the mean hydrometeor loss.

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Fig. 6. As in Fig. 5 except for those of raining stratiform regions.

vapor convergence. The insensitivity of the mean rainfall to radiation in phase 2 results from the offset between the decreased stratiform rainfall and the increased convective rainfall. The exclusion of radiation decreases stratiform rainfall through the decrease in water vapor convergence and the increase in local atmospheric moistening, whereas it increases convective rainfall through the enhanced local atmospheric drying and increased water vapor convergence.

b. Partitioning analysis

4. Summary The decreases in the mean rain rate caused by the exclusion of ice clouds result primarily from the weakened stratiform rain rate in phases 1 and 2 and the reduced convective rain rate in phase 3 (Figs. 5 and 6). The decreases in stratiform rainfall in phase 1 are associated with the weakened water vapor convergence and hydrometeor change from loss in C to gain in CNIM in phase 1 and the weakened water vapor convergence in phase 2. The decrease in convective rainfall in phase 3 is related to the decreases in water vapor convergence and local atmospheric drying. The decrease in the mean rain rate caused by the removal of radiation in phase 3 comes from the decreases in convective and stratiform rainfall. The decreased convective rainfall corresponds to the weakened local atmospheric drying and the enhanced transport of hydrometeor concentration from convective regions to raining stratiform regions, whereas the decrease stratiform rainfall is related to the suppressed water

The effects of radiation and ice clouds on a torrential rainfall event of Jiang-Huai Valley, China during 2-9 July 2007 is examined through a series of two-dimensional sensitivity cloudresolving model experiments forced by large-scale NCEP/ GDAS data. Two sensitivity experiments artificially suppress radiation and ice clouds compared to the control experiment. The results show that model domain mean surface rain rate is generally more sensitive to ice clouds than to radiation. The exclusion of ice clouds decreases the mean rain rate through the decrease in the mean net condensation and the mean hydrometeor change from loss in the control experiment with ice clouds to gain in the experiment without ice clouds. The decreases in the mean rain rate caused by the removal of ice clouds result from the reduced stratiform rainfall in the early period of the rainfall event and the suppressed convective rainfall in the later period of the rainfall event.

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The exclusion of radiation enhances the mean radiative cooling and correspondingly increases the mean latent heat in the early period of the rainfall event because it barely changes the mean heat divergence. As a result, the mean rain rate is slightly increased. In the later period of the rainfall event, the mean local atmospheric cooling increases and becomes more important than the increased mean radiative cooling associated with the removal of radiation, which leads to the decreases in the latent heat and associated mean net condensation and thus to the decrease in the mean rain rate. The decrease in the mean rain rate results from the decreases in both convective and stratiform rainfall. Caution should be exercised in the applications of the results of this study since the analysis is conducted using a 2D model with imposed large-scale forcing. Thus, it is necessary to conduct three-dimensional interactive cloud resolving model experiments to examine effects of radiation and ice clouds on torrential rainfall of Jiang-Huai Valley over China. Acknowledgements. The authors thank Dr. W.-K. Tao at NASA/GSFC for his 2D cloud-resolving model and two anonymous reviewers for their constructive comments. This work is supported by the Key Project of Natural Science Foundation of China under the grant No. 40930951 and CMAGJ2013M72.

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