• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Performance of Convective Parameterization Schemes in Asia Using RegCM: Simulations in Three Typical Regions for the Period 1998–2002

    2015-02-24 06:22:05ShaukatALIDANLiFUCongbinandYANGYang
    Advances in Atmospheric Sciences 2015年5期

    Shaukat ALIDAN LiFU Congbinand YANG Yang

    1START Temperate East Asia Regional Center and Key Laboratory of Regional Climate-Environment for Temperate East Asia,

    Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing100029

    2University of Chinese Academy of Sciences,Beijing100049

    3Global Change Impact Studies Centre,Ministry of Climate Change,Islamabad,Pakistan

    Performance of Convective Parameterization Schemes in Asia Using RegCM: Simulations in Three Typical Regions for the Period 1998–2002

    Shaukat ALI1,2,3,DAN Li?1,FU Congbin1,and YANG Yang2

    1START Temperate East Asia Regional Center and Key Laboratory of Regional Climate-Environment for Temperate East Asia,

    Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing100029

    2University of Chinese Academy of Sciences,Beijing100049

    3Global Change Impact Studies Centre,Ministry of Climate Change,Islamabad,Pakistan

    This study discusses the sensitivity of convective parameterization schemes(CPSs)in the Regional Climate Model(version 4.3)(RegCM4.3)over East/South Asia.The simulations using different CPSs in RegCM are compared to discover a suitable scheme for this region,as the performance of different schemes is greatly in fl uenced by region and seasonality.Over SoutheastChinaandtheBayofBengal,theGrellschemeexhibitsthelowestRMSEsofsummerprecipitationcomparedtoobserved data.Moreover,the Emanuel over land and Grell over ocean(ELGO)scheme enhances the simulation,in comparison with any single CPS(Grell/Emanuel)over Western Ghats,Sri Lanka,and Southeast India.Over the Huang–Huai–Hai Plain (3H)and Tibetan Plateau(TP)regions of China,the Tiedtke scheme simulates the more reasonable summer precipitation with high correlation coef fi cient and comparable amplitude.Especially,it reproduces a minimum convective precipitation bias of 8 mm d-1and the lowest RMSEs throughout the year over East/South Asia.Furthermore,for seasonal variation of precipitation,the Tiedtke scheme results are closer to the observed data over the 3H and TP regions.However,none of the CPSs is able to simulate the seasonal variation over North Pakistan(NP).In comparison with previous research,the results of this study support the Grell scheme over South Asia.However,the Tiedtke scheme shows superiority for the 3H,TP and NP regions.The thicker PBL,less surface latent heat fl ux,the unique ability of deep convection and the entrainment process in the Tiedtke scheme are responsible for reducing the wet bias.

    regional climate models(RCM),RegCM4,Tiedtke scheme,convective parameterization scheme(CPS),topography,seasonality

    1. Introduction

    Efforts have been made in recent decades to develop and improve regional climate models(RCMs),but many problems remain;for example,the lateral boundary conditions (Liang et al.,2001;Wu et al.,2005),horizontal resolution, size match of a region(Vannitsem and Chom′e,2005;Xue et al.,2007;Liu et al.,2010),and the parameterization schemes of unique physical processes(Cha et al.,2008;Yhang and Hong,2008).Moreover,the selection of an appropriate convective parameterization scheme(CPS)in RCMs is a major source of error and has signi fi cant impact on regional climate model predictions(Pal et al.,2007).The Regional Climate Model Intercomparison Project(RMIP)for Asia concluded that the simulations of RCMs are highly sensitive to CPSchoices(Fu et al.,2005).Generally,no single CPS can perform well universally for all atmospheric systems because, for instance,the processes of convection in the tropics can be signi fi cantly different from those in the midlatitudes(Wang and Seaman,1997;Singh et al.,2006;Chow and Chan, 2010).Manystudieshavebeenconductedindifferentregions to validate the sensitivity of CPSs,e.g.,America(Gochis et al.,2002;Liang et al.,2007),Europe(Wang and Seaman, 1997;Zanis et al.,2009),Africa(Davis et al.,2009;Segele et al.,2009;Tchotchou and Kamga,2010),the Caribbean region(Mart′?nez-Castro et al.,2013),the Maritime Continent (Gianotti et al.,2012),and South and East Asia(Dash et al., 2006;Chow and Chan,2010;Singh et al.,2011;Sinha et al., 2014).

    Wang and Seaman(1997)reported inconclusive results when they compared four CPSs,with no single scheme emerging as the best.The Regional Climate Model(RegCM) with a horizontal resolution of 55 km was used by Dash et al.(2006)for a four-year simulation(1993–96),and the results showed that the Grell scheme performed better for simulating the summer precipitation over India.Using the Anthes–KuoschemeinRegCMproducedcomparableresultsforareaaveraged precipitation over the Caribbean(Mart′?nez-Castro et al.,2013).Singh et al.(2006)statistically analyzed the climate over East Asia.Their results indicated that the Emanuel and Grell schemes minimize the biases and performs well over this region,especially Korea,but that issues of overestimation still remain.Liang et al.(2007)simulated the summer precipitation over the U.S.and Mexico using an ensemble of the Grell and Kain–Fritschl cumulus schemes.This approach produced far superior performance and considerable improvement was achieved compared to each individual scheme over the entire study domain.RegCM was customized with the Emanuel scheme for precipitation simulation by Davis et al.(2009).This model con fi guration predicted the rainfall over eastern Africa and the tropical Indian Ocean more realistically,but overestimation of precipitation also occurred.Segele et al.(2009)concluded that the Emanuel scheme performs better when selecting 1984 as a dry year and 1996 as a wet year.Octaviani and Manomaiphiboon(2011)demonstratedthattheEmanuelschemeperforms well,followed by the Anthes–Kuo scheme,when a doublenested 60 and 20 km resolution domain is used.According to Basit et al.(2012),the Grell scheme captures well the monsoon phenomenon,especially for the mountainous regions of North Pakistan,with the Arakawa–Schubert(AS) and Fritsch–Chappell(FC)closures both performing satisfactorily.Huang et al.(2013)simulated the diurnal variation of rainfall over Southeast China during 1998–2002,and reported better results when using the Grell scheme;whereas, the simulation of summer mean rainfall over East Asia was more realistically simulated using the Emanuel scheme.The CPSs play an important role in the simulation of summer precipitation in monsoon regions and the performances of CPSs have numerous uncertainties(Lee et al.,2008;Bao,2013).

    Table 1 provides a list of recent RegCM recent studies on CPSs over East,South,and Southeast Asia.Most of the studieswerebasedonmodelsimulationsof1–5years(Dashetal., 2006;Singh et al.,2006;Im et al.,2008a;Basit et al.,2012), but with some based on 10 years or more(Cao et al.,2007; Im et al.,2008b).Horizontal resolutions of 50 km or more have been used in several studies(Dash et al.,2006;Rahman et al.,2007a,2007b;Liu and Ding,2007;Chow and Chan, 2010;Huang et al.,2013;Bao,2013).However,few studies have also used a resolution of less than 50 km(Singh et al., 2006;Gianotti et al.,2012)in a nested domain setup(Im et al.,2008a;Octaviani and Manomaiphiboon,2011).Studies show that CPSs present good results with a smaller model domain over East Asia,but they are unable to model tropical cyclones and extreme events(Zhong,2006).Convective precipitation derived from different convection parameterizations is a major contributor to the performance of some models in the summer season(Im et al.,2008a).In simulating the Asian summermonsoon,the dualscheme approach enhances model performance(Chow and Chan,2010).Errors in models persist irrespective of the choice of CPS or land surface scheme and lateral boundary conditions(Gianotti et al.,2012).The performances of CPSs improve with a smaller horizontal resolution;for example,Sinha et al.(2013)reported results that were better when using 30 km rather than 90 km.Overall,the previous results summarized in Table 1 suggest that the Grell scheme is best for South Asia,the Emanuel scheme for East Asia,while both are suitable for Southeast Asia.

    2. The convective parameterization schemes

    2.1.Grell scheme

    Grell(1993)represents cloud and environment as two steady-state circulations(an updraft and a downdraft that are undiluted,i.e.,no entrainment or detrainment occurs along the cloud edges such that the mass fl ux is constant with height)in the Grell scheme.The scheme can be used when a lifted parcel reaches moist convection level,and the mixing of cloud,air,and the environment occurs only at the top and bottom.This scheme is very simple in nature and focuses on the statistical equilibrium between large-scale processes and convection.The minimum and maximum levels of moist static energy indicate the initiating levels of the downdraft and updraft.The Grell scheme activates when a lifted parcel of air reaches the condensation and moist convection level in the updraft,and is calculated by a saturated parcel.Mddenotes the downdraft mass fl ux andMurepresent the updraft mass fl ux.Hence,the scheme can be shown in the form of an equation:

    ThenormalizedupdraftcondensationisdenotedbyI1,the normalized downdraft evaporation isI2,andβis the fraction of updraft condensation that re-evaporates during the downdraft and depends on the change in the wind direction and speed that typically changes between 0.3 and 0.5.Hence,wehave the equation

    wherePis precipitation.The detrainment and mass fl uxes at the top and bottom of the cloud regulate the moistening and heating in the Grell scheme with the cooling effect during the downdrafts.The Grell scheme is fl exible to apply with different assumptions[i.e.Arakawa-Schubert(AS)and Fritsch-Chappell(FC)].

    2.2.Emanuel scheme

    The Emanuel scheme assumes that mixing in clouds is highly inhomogeneous and episodic.Modeling of convective fl uxes is based on an idealized condition of sub-cloud-scale downdrafts and updrafts.The process of convection is initiated if the level of neutral buoyancy is greater than the cloud base level.Air is lifted between these two levels and a fraction of the condensed vapor forms precipitation,whereas the residual forms the cloud(Elguindi et al.,2013).It is assumed that the cloud is mixed with the air from the environment permitting to the range of mixtures that descend or ascend to particular levels of neutral buoyancy.The mixing detrainment and entrainment rates are the functions of the vertical gradients of buoyancy in clouds.The fraction of the total cloud base mass fl ux that combines with the environment at all levels is proportional to the rate of change of buoyancy with altitude.The upward mass fl ux at the cloud base decreases towards the sub-cloud layer quasi equilibrium.The Emanuel scheme is designed in such a way that it provides several advantages regarding convection options.It consists of a technique that converts the cloud water into precipitation in cumulus clouds.The ice processes are temperature-dependent, allowing the auto-conversion threshold water content to be used.The precipitation is included in hydrostatic,unsaturated,and a single downdraft that carries water and heat.The Emanuel scheme also considers the carrying of passive tracers.

    2.3.Tiedtke scheme

    The Tiedtke scheme was originally developed for application on the global scale.This scheme is dependent on the mass fl ux and moisture convergence.The triggering mechanism is based on convection,i.e.if the temperature of the parcel exceeds the temperature of the environment by a fi xed temperature threshold,it creates conditional instability.It has shallow and deep convection,determining the cloud base mass fl ux from the PBL equilibrium and mass fl ux closure from CAPE,respectively.In deep convection,the reduction of CAPE is the integrated effect of the convective heating, and the middle level exists if there is large-scale ascent with mass fl ux closure.In the Tiedtke parameterization,updraft is sensitive to entrained air from the free troposphere(Hourdin et al.,2006).Thus,the convection can be reduced by the dry, free troposphere.The convective cloud cover and cloud water content sources are described as functions of the detrainment of mass from the speci fi c content of cloud water in the updrafts,convective updrafts,and the density of cloudy air.An updraft air parcel is assumed to detrain into existing cloud as well as into cloud-free air,making sure the real limits of zero cloud cover and full cloud cover.Zero cloud cover is considered in which updraft air detrains only into clear air and full cloud cover is considered where all updraft air detrains into existing clouds.The mass for detrainment is acquired from the cumulus parameterization for the updraft mass fl ux (Tiedtke,1993).Cumulus clouds can occur if a deep layer of conditional instability and large-scale moisture convergence exist.Within the lower half of the troposphere,an increase in vertical mass fl ux is connected to the moisture convergence in the column of the atmosphere.Hence,deep convection occurs as the undiluted parcel of air rises adiabatically with the dry adiabatic lapse rate that positively regulates the buoyancy until it touches the LCL and becomes saturated.The precipitation will occur due to turbulent eddies and stronger large-scale moisture convergence(Tiedtke,1989).

    3. Methods

    3.1.Model

    The model used in this study is version 4.3 of RegCM. The fi rst version of RegCM was developed in the late 1980s (Giorgi and Bates,1989;Dickinson et al.,1989).Since then, it has been continuously upgraded to RegCM2(Giorgi et al.,1993),RegCM3(Pal et al.,2000,2007)and RegCM4 (Giorgi et al.,2012).Earth System Physics(ESP),Abdul Salam International Center for Theoretical Physics(ICTP), Italy,maintains the latest version,i.e.,RegCM4.The model’s latest versions are more user-friendly, fl exible,and portable, having important updates made to the source code.The dynamic core of RegCM4 is similar to RegCM3,which was based on the hydrostatic version of the National Center for Atmospheric Research(NCAR)/Penn State mesoscale model(MM5)(Grell et al.,1994).The PBL computations are parameterized by the scheme of Holtslag et al.(1990); the land surface model is the Biosphere–Atmosphere Transfer Scheme(BATS)(Dickinson et al.,1989);the radiation scheme is the modi fi ed NCAR Community Climate Model version 3(CCM3)(Kiehl et al.,1998);and the resolvablescale precipitation is signi fi ed by the system of subgrid explicit moisture scheme(SUBEX)(Pal et al.,2000).

    3.2.Experiments and data

    The model domain covers Southeast Asia over the area (4°–56°N,49°–138°E).The climatology of this region is mainly affected by the monsoon season.Over most parts of the Southeast Asian region,rainfall occurs during the months of June to September,also known as the summer monsoon, depending on the seasonal direction of the prevailing surface wind.High rainfall associated with regional orography occurs parallel to India,the west coast of the Indochina peninsula,South China,and the South China Sea.There is a wide zone of around 20°N extending towards the northeast from the north part of the Bay of Bengal(BoB)that receives substantial rainfall.Southeast Asian summer monsoon rainfallis linked with the variability of rainfall in this monsoon zone (Wang and Wu,1997).The rainfall over monsoon zone is associated with the synoptic-scale convective systems produced over the warm oceans surrounded by the subcontinent that transport moisture to the northeast.Partly overlapping disturbances are also generated over this zone due to active spells of the monsoon.The summer monsoon even in fl uences the global climate through energy exchange and hydrological processes,as well as the regional climate(Lau and Weng, 2001).

    Three sub-regions are selected from the model domain for detailed analysis(Fig.1):North Pakistan[NP:(34.5°–37°N,71°–78°E)];Tibetan Plateau[TP:(30°–37°N,80°–95°E)];and Huang-Huai-Hai Plain[3H:(31°–41°N,112°–121°E)].The selected sub-regions consist of two mountain areas(NP and TP)and one irrigated plain(3H)region(Dan et al.,2012)in China.3H is occupied by 425 million people and provides 50%of China’s grain,where the crop production is greatly dependent on irrigation from groundwater and surface runoff.NP and TP consist of the Himalaya,Hindu Kush,andKarakoram,withtheworld’sthird-largesticestore. The domain contains 336 grid points in the latitudinal direction and 280 grid points in the longitudinal direction,with a central point of(32°N,94°E)and 18 vertical sigma levels. This domain is quite large for the development of internal model mesoscale circulations and regional forcing(Jones et al.,1995;Singh et al.,2006).

    RCMs are usually run at high resolution when the output of RCMs is used for impact studies.In this study,RegCM4 is run at 20 km at its maximum fi ner resolution because the hydrostatic engine of RegCM does not allow a resolution fi ner than 20 km(Elguindi et al.,2013).Five simulations with fi ve different CPSs are carried out for the period 1997–2002.The fi rst year,1997,is used as model spin-up,and so the analysis is considered from 1998 to 2002.The lateral and initial boundary conditions are taken from ERA-interim data(Simmons et al.,2007;Uppala et al.,2008),available since 1979 to the present day with 1.5°horizontal resolution.The ERA-interim dataset is a realistic state-of-the-art and widely used model solution.It has good coverage of upper-air measurements over the whole globe and is regarded as relatively more reliable than other reanalysis datasets(Lin et al.,2014),especially over the Northern Hemisphere.It also assimilates more advanced and observational data(Dee et al.,2011).

    The SST data are acquired by optimum interpolation(OI) processing,i.e.,the weighted monthly mean of the given set of data on the basis of the observed Reynolds SST fi eld where the variance of the estimate is kept to the minimum. The OI SST analysis is produced weekly on a 1°grid.The analysis usesin situand satellite SSTs,plus SSTs simulated by sea-ice cover.Before the analysis is computed, the satellite data are adjusted for biases using the method of Reynolds(1988)and Reynolds and Marsico(1993).Topographic and vegetation cover datasets was taken from 30-arcsecond(GTOPO30),aggregated to 10 arc minutes and global land cover characterization(GLCC)respectively.To validate the model results,three observational datasets are used for precipitation and temperature including:monthly-mean and 3-hourly datasets available from 0.25°×0.25°products from the Tropical Rainfall Measurement Mission(TRMM 3B42, Huffman et al.,2007),over the range 50°N–50°S since January 1998 to the present day.TRMM 3A25 data with distinct convective and stratiform rainfall are used to validate the convective precipitation.The monthly mean temperature and precipitation data for the period 1901–2000 over the global land area at regular intervals of 0.5°are from the Climate Research Unit(CRU)TS2.1 dataset(Mitchell and Jones,2005).The gridded precipitation data for the period 1957–2007arefromtheAsianPrecipitationHighly-Resolved Observational Data Integration Towards Evaluation of water resources(APHRODITE)dataset(Yatagai et al.,2012),provided by the Meteorological Research Institute of the Japan Meteorological Agency(MRI/JMA)and the Research Institute for Humanity and Nature(RIHN)at 0.25°resolution.

    4. Results

    The effects of different CPSs on average temperature, precipitation,and winds at 850 hPa during a fi ve-year period (1998–2002)are presented in this section.First,we describe the simulation results over the whole of the East/South Asia domain for the four seasons de fi ned as:spring(March–May; MAM);summer(June–August;JJA);autumn(September–November;SON);and winter(December–February;DJF). And second,we analyze the statistical results,including correlation coef fi cient,normalized standard deviation,and RMSE values,over the three sub-regions(3H,NP,and TP).

    4.1.Atmospheric simulation using the fi ve CPSs

    The seasonal and annual means of precipitation and wind are examined to understand the moisture fl ow from one region to another(Fig.2).It can be seen that RegCM4.3 is able to properly reproduce the circulation for the East/South Asian monsoon.During MAM–JJA,atmospheric pressure decreases over the Asian landmass due to intense surface heating,and at the same time surface pressure remains relatively high over the cool sea to the south.Moist air from the equator moves northward under this seasonally developed pressure gradient carrying water vapor from the BoB and Arabian Sea.South/southwestward wind patterns develop due to the Coriolis force and prevail over South/Southeast Asia during the summer monsoon from May to September. During SON–DJF,north/northeast winds move from northern high latitudes affected by the Siberian high,carrying cold air from northern land to most parts of East/South Asia and causing winter precipitation.The annual means of precipitation and circulation are more affected by MAM–JJA,whereas SON–DJF has less impact.The annual mean and MAM–JJA circulation patterns are more similar in Fig.2,which clearly indicates that MAM–JJA is dominant in terms of the annual mean of circulation and precipitation.

    The results of RegCM4.3-simulated precipitation and winds are compared with TRMM and ERA-Interim data(Fig. 3)separately.It is clear that the Grell simulations produce more rainfall over the Arabian Sea and BoB throughout the year,whereas other schemes show a dry bias.In summer, the Emanuel and Tiedtke schemes underestimate the precipitation over the Indian Peninsula,whereas the Grell scheme overestimates precipitation by 6 mm d-1over south India due to the local cyclone for air convergence.Furthermore, over southwest China,the Indochina peninsula,and Northeast China,the Emanuel scheme produces more precipitation and the Tiedtke scheme produces less precipitation over South China,probably due to less surface latent heat fl ux (Fig.4)and entrained air from the troposphere.In winter, all the schemes produce less biased results of precipitation as compared to summer due to less convective activity.Table 2 lists the seasonal RMSEs of precipitation for the fi ve schemes over the whole domain.The Grell scheme shows a small RMSE value for precipitation of 3.41 mm d-1for the summer season(JJA),whereas the Emanuel scheme shows a high RMSE of 5.20 mm d-1,followed by GLEO(4.57 mm d-1).During MAM,the Tiedtke scheme shows the lowest RMSE values in comparison to the other CPSs.In winter, the Emanuel and Tiedtke schemes have low RMSE values for precipitation(~1.7 mm d-1).RMSE values are higher during summer precipitation than in other seasons.Table 3 lists the precipitation RMSE values on a seasonal basis overthe southeast China(SEC),BoB,the Western Ghats(WG), and Sri Lanka/southeast India(SLSEI).For SEC,the Grell scheme performs effectively compared to the other schemes during JJA,SON,and DJF,while the Tiedtke scheme performs better for MAM.Over the BoB,the Grell scheme shows effective results for JJA and SON,while GLEO and the Tiedtke scheme improve their outcomes for MAM and DJF,respectively.Over WG,the Grell scheme shows enhanced performance during MAM,SON,and DJF.However, the ELGO scheme produces considerable results over WG and SLSEI compared to each standalone scheme(i.e.Grell and Emanuel)during JJA,indicating that in regions that containbothlandandocean,thecombinationoftheGrellscheme over the ocean and the Emanuel scheme over land produces suitable results.

    The amount of convective precipitation is very sensitive and directly in fl uenced by the choice of convection scheme (Wang and Seaman,1997).Figure 5 illustrates the differences of seasonal mean convective precipitation between RegCM4.3 and TRMM 3A25 data.It is clear that in JJA the Tiedtke scheme produces the least biased convective precipitation.However,it overestimates rainfall by 4–6 mm d-1over southern China and adjoining areas,and underestimates precipitation over some parts of India and NP.Figure 5 also indicates that the Grell,Emanuel,GLEO and ELGO schemes overestimate precipitation by 4–12 mm d-1over the Indianpeninsula,SEC,and adjoining areas.The Emanuel scheme shows a precipitation bias of 4–6 mm d-1over India and the BoB,while over SEC and the Indochina peninsula it generates more convective precipitation and the bias exceeds 12 mm d-1.The ELGO scheme reduces the wet bias over India and the Indochina peninsula,and produces the secondsmallest wet bias over Southwest China.The GLEO scheme produces a small dry bias over the BoB,close to that of the Emanuel scheme,but over India and the Indochina peninsula it shows a large wet bias.In comparison with TRMM data, theTiedtkeschemereproduceswell-distributedseasonalvariability and intensity of convective precipitation,with the smaller bias probably due to less convection in the scheme. The Tiedtke scheme also produces minimum RMSE values for all the seasons with TRMM(Table 4).

    Table 3.Seasonal RMSE for precipitation over Southeast China, Bay of Bengal,Western Ghats near the Arabian Sea,and Sri Lanka/Southeast India(bold values indicate the lowest RMSE for each season).

    Table 4.Seasonal RMSE for convective precipitation for the entire East/South Asian domain of our study(bold values indicate the lowest RMSE for each season).

    The seasonal mean temperatures at the height of 2 m in the fi ve schemes along with CRU data are shown in Fig. 6.Overall,the simulated surface air temperatures with the fi ve CPSs agree closely with CRU data throughout the year. The results show that RegCM4.3 is able to capture the mean state and seasonal cycle of the surface air temperature over East/South Asia.

    In order to study the inter-model differences in temperature at the height of 2 m,the differences between RegCM4.3 when using the fi ve CPSs and CRU data are shown in Fig. 7.Also shown are the differences between ERA-Interim and CRU data(Figs.7u–x).In general,surface air temperature bias patterns are similar in RegCM4.3 with the different CPSs.All the schemes result in a similar cold bias throughout the year over the west of the TP and NP.The possible reasons for this cold bias are:(1)the difference in the representation of elevation in the model and actual station values;(2)the rapid variation in the topography of mountainous regions,e.g.,the problem of accommodating and processing orographic lifting is very signi fi cant in the NP;and (3)the actual height of the Himalaya is more than prescribed in RegCM(Sinha et al.,2014).The Tiedtke scheme shows a relatively smaller cold bias among the fi ve CPSs in summer.For surface air temperature during summer,the Tiedtke scheme even shows a warm bias(more heating)because of more radiation in the surrounding regions and a high rate of evaporation and convection,which later transports moisture towards the northeast.Increased heating makes the atmosphere warmer over most parts of the domain when compared to other schemes that show cold bias.The Tiedtke scheme results show a thick PBL over India and most parts of China(not shown)with less precipitation.Since convection is primarily initiated within the PBL region that responds to surface turbulent heat fl uxes driven by incoming solar radiation after relatively short time scales,convective activity affects large-scale atmospheric dynamics by vertical transport of heat and moisture and adiabatic heating.It is also in fl uenced by the interaction of cumulus clouds with radiation.The resilient contact of cumulus clouds with longwave andshortwaveradiationcausesatmosphericmotion,moisture convergence and dispersion over the surface as well as in the atmosphere.This moisture is carried to the northeast,leaving most parts of India and China relatively drier.In winter,all the schemes produce a cold bias over SEC and the Indochina peninsula,with a center over Thailand,west of the TP,and Southwest China.The temperature bias patterns in RegCM are similar in different CPSs.The Grell,Emanuel,ELGO and GLEO schemes exhibit RMSE values between 2.09 and 2.47°C during summer(JJA)(Table 2),whereas the Tiedtke scheme shows the highest RMSE value of 3.11°C and produces worse results than the other schemes.In winter,the Tiedtke and Grell schemes show smaller RMSEs.It is also clear that the RMSE values for temperature are larger in winter than in other seasons.

    4.2.Simulation over the three sub-regions

    The results of seasonal variation of precipitation over the three sub-regions(3H,TP and NP)(Fig.1)are presented in Fig.8.TRMM and APHRODITE data show more precipitation in the region of 3H but less in TP and NP.The model resultsinFig.8forJJAalsoshowsimilarresults.Overthe3H region,precipitation reaches the maximum,i.e.,around 4mm d-1in July and the minimum in December.The Grell and GLEO schemes show that precipitation in summer exceeds 6 mm d-1,while in the Emanuel and ELGO scheme results its amplitude reaches double that of the APHRODITE data.The seasonal variation of the Tiedtke scheme produces simulation results closer to TRMM and APHRODITE data.The results over the TP are similar to the 3H region,with all CPSs showing more precipitation throughout the year except the Tiedtkescheme,which shows a dry bias in summer.Small variations in precipitation are shown according to APHRODITE and TRMM data for the NP.APHRODITE produces maximum precipitation in April,whereas TRMM produces maximum precipitation in July.The seasonal variation of simulated precipitation shows larger differences in winter than summerifwecomparewithTRMMandAPHRODITE.None of the CPSs generate results that re fl ect the observed amplitude variation(high and low)of rainfall in February and August.Overall,in the three sub-regions,the simulation results of the Grell(Emanuel)and GLEO(ELGO)schemes are quite similar,which share the same CPS over land,and are therefore in fl uenced directly by the CPS over land rather than over the ocean.

    Vertical pro fi les of summer temperature and water vapor mixing ratio are compared with ERA-interim data(Fig.9). Over the 3H region,the Tiedtke scheme shows warmer temperatures near the surface up to 500 hPa associated with the maximum temperature at the height of 2 m for the same region(Fig.7).Above 500 hPa,the temperature differences become very small.Over the TP and NP,the warmer temperature in the Tiedtke scheme reaches up to the height of 200 hPa,and the differences between RegCM4.3 and ERAInterim show warmer temperatures at this level compared to the lower troposphere.The warm difference(RegCM minus ERA-Interim)decreases as the altitude increases up to 600 hPa,and even turns to cold differences in the middle troposphere by the Grell and GLEO schemes above 600 hPa.This temperature decrease is due to frequent convective activities in the Grell scheme.Furthermore,the differences in temperature increase and attain maximum values of around 12°C at 150 hPa.The Grell and GLEO schemes show more unstable atmospheric conditions when the altitude increases up to 600 hPa.In the Tiedtke scheme the slow convective process decreases the ascending motion of the air before the condensation level and creates a more stable structure with less precipitation,possibly due to less surface latent heat fl ux and entrainment.This reduces the wet bias of convective precipitation in the Tiedtke scheme(Fig.5).The Tiedtke scheme retains more water vapor in the troposphere but shows a reduced precipitation difference(Fig.3b).The underestimation of precipitation in the Tiedtke scheme was also reported by Kaspar and Cubasch(2008),Wang et al.(2011),and Mart′?nez-Castro et al.(2013).The atmosphere is drier in the Tiedtke scheme at 400–700 hPa,as shown in Fig.9e,and at 850 hPa in Fig.9f.The Emanuel scheme is more active in transporting humid air from low levels to high levels,generating more precipitation.

    Figures 10a and b show the correlation coef fi cient and normalized standard deviation results for summer surface air temperature and precipitation,respectively,with the fi ve CPSs over the 3H,TP and NP sub-regions.For surface air temperature,the simulations of the different CPSs are closer to each other and the correlation ranges from 0.72 to 0.97 compared with CRU data.Over the 3H,the Emanuel and ELGO schemes show strong correlation and standard deviation of temperature which is approximately 1,while over the NP and TP,the Grell scheme shows strong correlation and a normalized standard deviation ratio of closer to 1.For precipitation,the correlation is weaker than for temperature over the three sub-regions,and the correlation and standard deviation show large differences in different schemes.Over the 3H region,the correlation of the Grell and Tiedtke schemes with TRMM data is about 0.6,which is double that for the Emanuel scheme(0.3).Over the TP,the Tiedtke scheme produces strong positive correlation(0.8)and standard deviation closer to 1 in simulating summer precipitation.Over the NP, all the CPSs show very weak correlation,i.e.,less than 0.3, and low standard deviation for summer precipitation.

    Table 5.Seasonal RMSE for precipitation and temperature over the 3H region,North Pakistan and Tibetan Plateau(bold values indicate the lowest RMSE for each season).

    RMSEvaluesarecalculatedforallschemesoverthethree sub-regions for precipitation and temperature(Table 5).The Tiedtke scheme performs better in simulating precipitation over the 3H region all year round,especially in MAM and JJA.For temperature in the 3H region,ELGO produces better results for JJA,SON,and DJF.Over the NP,the Tiedtke scheme produces good approximations of precipitation except for JJA.The ELGO scheme produces better results for JJA compared to the Tiedtke scheme. Over the TP,the Tiedtke scheme produces appropriate results throughout the year for precipitation and temperature.

    5. Conclusion

    RegCM4.3 successfully simulates the characteristics of East/South Asia summer monsoon circulation at 850 hPa. The performance of all CPSs is strongly dependent on spatial and seasonal variation of a region.Over SEC and the BoB, the Grell scheme performs more effectively than the other schemes.However,all the schemes tend to show a dry bias over the Arabian Sea and BoB throughout the year,except the Grell scheme which produces surplus rainfall.Over WG,the Grell scheme shows better performance throughout the year except in JJA,when the ELGO scheme produces reasonable results.The Tiedtke scheme improves the simulation of convective precipitation in both spatial distribution and magnitude for all the seasons with minimum RMSE values.In the case of surface air temperature,all the CPSs produce almost the same cold bias(0–10°C)over the west of the TP and NP throughout the year.

    Generally,the Tiedtke scheme shows close agreement withTRMMandAPHRODITEdataforprecipitationoverthe three sub-regions.The annual cycle of precipitation shows that the model simulations with the different schemes areclose to the observed data over the 3H and TP regions,except over NP,where no scheme produces the seasonal variation because the actual height of the Himalaya is more than prescribed in RegCM.The simulations are processed with 20 km horizontal resolution,and hence the problem of a leeward sudden change in orography exists.The model is unable to process the issues of orography,and thus some balancing parameters in the model are needed to overcome the simulation problems in this region.In the vertical pro fi les of temperature and water vapor mixing ratio,the atmosphere in the Tiedtke scheme is warmer and drier over the TP and NP compared to the other CPSs,which generates a precipitation difference. The Tiedtke scheme produces the dry conditions over the 3H region,and hence less precipitation due to less surface latent heat fl ux.This result is supported by the Tiedtke scheme’s mechanism of moisture transportation and precipitation towards the northeast.For precipitation,the CPSs differ distinctly from one another.Among all the schemes,the Tiedtke scheme shows a strong correlation and standard deviation of precipitation and temperature.However,the correlation and standard deviation is very weak in NP in the results of all the schemes.

    Overall,the Tiedtke scheme yields better results for precipitation and temperature as compared to observed data, with the lowest RMSE values over the three sub-regions.Previous studies have recommended the Grell scheme for South Asia,the Emanuel scheme for East Asia,and both schemes (GrellandEmanuel)withequalweightingforSoutheastAsia. This study agrees with previous results that the Grell scheme is best for South Asia;however,over most parts of China and the three sub-regions,our results are quite different from previous studies and suggest the Tiedtke scheme to be superior. We believe that the reason for this fi nding is the unique characteristics of deep convection and entrainment in the Tiedtke scheme,which reduces the wet bias of precipitation.

    Acknowledgements.We thank Graziano GIULIANI and Erika COPPOLA,ICTP,for providing support and data for this research.This study was fi nancially supported by the Knowledge Innovation Program of the Chinese Academy of Sciences(KZCX2-EW-QN208),a project of the National Natural Science Foundation of China(Grant No.41275082),the National Basic Research Program of China(Grant Nos.2010CB428502 and 2011CB952003), and the R&D Special Fund for Public Welfare Industry(meteorology)of the Ministry of Finance and the Ministry of Science and Technology(GYHY201006014-04).

    REFERENCES

    Bao,Y.,2013:Simulations of summer monsoon climate over East Asia with a Regional Climate Model(RegCM)using Tiedtke convective parameterization scheme(CPS).Atmos.Res.,134, 35–44.

    Bao,Y.,2013:Simulations of summer monsoon climate over East Asia with a Regional Climate Model(RegCM)using Tiedtke convective parameterization scheme(CPS).Atmos.Res.,134, 35–44.

    Basit,A.,S.R.Shoaib,N.Irfan,and R.Avila,2012:Simulation of monsoon precipitation over South-Asia using RegCM3.ISRN Meteor.,12,754902,doi:10.5402/2012/754902.

    Cao,J.,X.N.Zhang,Y.L.You,and R.W.Yang,2007:Applicability of cumulus convective parameter schemes in RegCM3 to the rainfall over the Longitudinal Range-Gorge Region.Chinese Science Bulletin,52(2),115–121.

    Cha,D.H.,D.K.Lee,and S.Y.Hong,2008:Impact of boundary layer processes on seasonal simulation of the East Asian summer monsoon using a regional climate model.Meteorology and.Atmospheric Physics,100(1–4),53–72.

    Chow,K.C.,and J.C.Chan,2010:A dual-scheme approach of cumulus parameterization for simulating the Asian summer monsoon.Meteorological Applications,17(3),287–297.

    Dan,L.,J.Ji,Z.Xie,F.Chen,G.Wen,and J.E.Richey,2012:Hydrological projections of climate change scenarios over the 3H region of China:A VIC model assessment.J.Geophys. Res,117(D11).doi:10.1029/2011JD017131.

    Dash,S.K.,M.S.Shekhar,and G.P.Singh,2006:Simulation of Indian summer monsoon circulation and rainfall using RegCM3.Theor.Appl.Climatol.,86(1–4),161–172.

    Davis,N.,J.Bowden,F.Semazzi,L.Xie,and B.¨Onol,2009: Customization of RegCM3 regional climate model for eastern Africa and a tropical Indian Ocean domain.J.Climate, 22,3595–3616.

    Dee,D.P.,and Coauthors,2011:The ERA-Interim reanalysis: Con fi guration and performance of the data assimilation system.Quart.J.Roy.Meteor.Soc.,137,553–597.

    Dickinson,R.E.,R.M.Errico,F.Giorgi,and G.T.Bates,1989: A regional climate model for the western United States.Climatic Change,15(3),383–422.

    Elguindi,N.,X.Bi,F.Giorgi,B.Nagarajan,J.Pal,F.Solmon, and G.Giuliani,2013:Regional Climate Model RegCM User Manual Version 4.4.The Abdus Salam International Centre for Theoretical Physics,Strada Costiera,Trieste,Italy October 21,2013,54 pp.

    Emanuel,K.A.,and M.ˇZivkovic-Rothman,1999:Development andevaluationofaconvectionschemeforuseinclimatemodels.J.Atmos.Sci.,56(11),1766–1782.

    Fu,C.,S.Wang,Z.Xiong,W.J.Gutowski,D.K.Lee,J.L.Mc-Gregor,and M.S.Suh,2005:Regional climate model intercomparison project for Asia.Bull.Amer.Meteor.Soc.,86(2), 257–266.

    Gianotti,R.L.,D.Zhang,and E.A.Eltahir,2012:Assessment of the regional climate model version 3 over the maritime continent using different cumulus parameterization and land surface schemes.J.Climate,25(2),638–656.

    Giorgi,F.,and G.T.Bates,1989:The climatological skill of a regional model over complex terrain.Mon.Wea.Rev.,117(11), 2325–2347.

    Giorgi,F.,M.R.Marinucci,and G.T.Bates,1993:Development of a second generation regional climate model(RegCM2).I. Boundary layer and radiative transfer processes.Mon.Wea. Rev.,121,2794–2813.

    Giorgi,F.,and Coauthors,2012:RegCM4:Model description and preliminary tests over multiple CORDEX domains.Climate Res.,52,7–29.

    Gochis,D.J.,W.J.Shuttleworth,and Z.-L.Yang,2002:Sensitivity of the modeled North American monsoon regional climate to convective parameterization.Mon.Wea.Rev.,130,1282–1298.

    Grell,G.A.,1993:Prognostic evaluation of assumptions usedby cumulus parameterizations.Mon.Wea.Rev.,121(3),764–787.

    Grell,G.A.,J.Dudhia,and D.R.Stauffer,1994:A description of the fi fth-generation Penn State/NCAR mesoscale model (MM5).NCAR Technical Note NCAR/TN-398+STR,doi: 10.5065/D60Z716B.

    Holtslag,A.A.M.,E.I.F.De Bruijn,and H.L.Pan,1990:A high resolution air mass transformation model for short-range weather forecasting.Mon.Wea.Rev.,118(8),1561–1575.

    Hourdin,F.,and Coauthors,2006:The LMDZ4 general circulation model:Climate performance and sensitivity to parameterized physics with emphasis on tropical convection.Climate Dyn., 27(7–8),787–813.

    Huang,W.R.,J.C.L.Chan,and A.Y.M.Au-Yeung,2013:Regional climate simulations of summer diurnal rainfall variations over East Asia and Southeast China.Climate Dyn., 40(7–8),1625–1642.

    Huffman,G.J.,D.T.Bolvin,E.J.Nelkin,D.B.Wolff,R.F. Adler,G.Gu,and E.F.Stocker,2007:The TRMM Multisatellite Precipitation Analysis(TMPA):Quasi-global,multiyear,combined-sensor precipitation estimates at fi ne scales.Journal of Hydrometeorology,8(1),38–55.

    Im,E.S.,J.B.Ahn,A.R.Remedio,and W.T.Kwon,2008a:Sensitivity of the regional climate of East/Southeast Asia to convective parameterizations in the RegCM3 modelling system. Part 1:Focus on the Korean peninsula.International Journal of Climatology,28(14),1861–1877.

    Im,E.S.,W.J.Gutowski,and F.Giorgi,2008b:Consistent changes in twenty- fi rst century daily precipitation from regional climate simulations for Korea using two convection parameterizations.Geophys.Res.Lett.,35,doi:10.1029/2008 GL034126.

    Jones,R.G.,J.M.Murphy,and M.Noguer,1995:Simulation of climate change over Europe using a nested regional climate model.Part I:Assessment of control climate,including sensitivity to location of lateral boundaries.Quart.J.Roy.Meteor. Soc.,121,1413–1449.

    Kaspar,F.,and U.Cubasch,2008:Simulation of East African precipitation patterns with the regional climate model CLM.Meteorologische Zeitschrift,17(4),511–517.

    Kiehl,J.T.,J.J.Hack,G.B.Bonan,B.A.Boville,D.L. Williamson,and P.J.Rasch,1998:The national center for atmospheric research community climate model:CCM3.J. Climate,11(6),1131–1149.

    Lau,K.M.,and H.Weng,2001:Coherent modes of global SST and summer rainfall over China:An assessment of the regional impacts of the 1997–98 El Nino.J.Climate,14(6), 1294–1308.

    Lee,D.Y.,C.Y.Tam,and C.K.Park,2008:Effects of multicumulus convective ensemble on East Asian summer monsoon rainfall simulation.J.Geophys.Res.,113(D24),doi:10.1029/ 2008JD009847.

    Liang,X.Z.,K.E.Kunkel,and A.N.Samel,2001:Development of a regional climate model for US Midwest applications.Part I:Sensitivity to buffer zone treatment.J.Climate, 14(23),4363–4378.

    Liang,X.Z.,M.Xu,K.E.Kunkel,G.A.Grell,and J.S.Kain, 2007:Regional climate model simulation of U.S.-Mexico summer precipitation using the optimal ensemble of two cumulus parameterizations.J.Climate,20(20),5201–5207.

    Lin,R.,T.Zhou,and Y.Qian,2014:Evaluation of global monsoon precipitation changes based on fi ve reanalysis datasets.J.Climate,27(3),1271–1289.

    Liu,Y.,and Y.Ding,2007:Sensitivity study of the South China Sea summer monsoon in 1998 to different cumulus parameterization schemes.Adv.Atmos.Sci.,24,360–376,doi: 10.1007/s00376-007-0360-y.

    Liu,Y.,and Y.Ding,2007:Sensitivity study of the South China Sea summer monsoon in 1998 to different cumulus parameterization schemes.Adv.Atmos.Sci.,24,360–376,doi: 10.1007/s00376-007-0360-y.

    Liu,H.,D.L.Zhang,and B.Wang,2010:Impact of horizontal resolution on the regional climate simulations of the summer 1998 extreme rainfall along the Yangtze River Basin.J.Geophys.Res.,115(D12),doi:10.1029/2009JD012746.

    Mamgain,A.,L.Mariotti,E.Coppola,F.Giorgi,and S.K.Dash, 2013:Sensitivity of RegCM4.3 two convection schemes on Indian summer monsoon for the South Asia CORDEX domain.EGU General Assembly Conference Abstracts,Vol.15, 4812.

    Mart′?nez-Castro,D.,A.Vichot-Llano,A.Bezanilla-Morlot,and A.Centella-Artola,2013:Sensitivity study on convection schemes in regCM4.3,in the simulation of air temperature and precipitation fi elds in Central America and the Caribbean.InternationalConferenceonRegionalClimateCORDEX,4–7 November 2013,Brussels,Belgium.

    Mitchell,T.D.,and P.D.Jones,,2005:An improved method of constructing a database of monthly climate observations and associated high-resolution grids.International Journal of Climatology,25(6),693–712.

    Octaviani,M.,and K.Manomaiphiboon,2011:Performance of regional climate model RegCM3 over Thailand.Climate Research,47(3),171–186.

    Pal,J.S.,E.E.Small,and E.A.Eltahir,2000:Simulation of regional-scale water and energy budgets:Representation of subgrid cloud and precipitation processes within RegCM.J. Geophys.Res..,105(D24),29 579–29 594.

    Pal,J.S.,F.Giorgi,X.Bi,N.Elguindi,F.Solmon,S.A.Rauscher, and A.L.Steiner,2007:Regional climate modeling for the developing world:The ICTP RegCM3 and RegCNET.Bull. Amer.Meteor.Soc.,88(9),1395–1409.

    Rahman,M.M.,M.N.Islam,A.U.Ahmed,and R.Afroz,2007a: Comparison of RegCM3 simulated meteorological parameters in Bangladesh:Part I—Preliminary result for rainfall.Sri Lankan Journal of Physics,8,1–9.

    Rahman,M.M.,M.N.Islam,A.U.Ahmed,and R.Afroz,2007b: Comparison of RegCM3 simulated meteorological parameters in Bangladesh:Part II—Preliminary result for temperature.Sri Lankan Journal of Physics,8,11–19.

    Reynolds,R.W.,1988:A real-time global sea surface temperature analysis.J.Climate,1,75–86.

    Reynolds,R.W.,and D.C.Marsico,1993:An improved real-time global sea surface temperature analysis.J.Climate,6,114–119.

    Segele,Z.T.,L.M.Leslie,and P.J.Lamb,2009:Evaluation and adaptation of a regional climate model for the Horn of Africa: Rainfallclimatologyandinterannualvariability.International Journal of Climatology,29(1),47–65.

    Simmons,A.,C.Uppala,D.Dee,and S.Kobayashi,2007:ERAInterim:New ECMWF reanalysis products from 1989 onwards.ECMWF Newsletter,No.110,25–35.

    Singh,A.P.,R.P.Singh,P.V.S.Raju,and R.Bhatla,2011:The impact of three different cumulus parameterization schemes on the Indian summer monsoon circulation.The InternationalJournal of Ocean and Climate Systems,2,27–44.

    Singh,G.P.,J.H.Oh,J.Y.Kim,and O.Y.Kim,2006:Sensitivity of summer monsoon precipitation over East Asia to convective parameterization schemes in RegCM3.Sola,2,29–32.

    Sinha,P.,U.C.Mohanty,S.C.Kar,S.K.Dash,and S.Kumari,2013:Sensitivity of the GCM driven summer monsoon simulations to cumulus parameterization schemes in nested RegCM3.Theor.Appl.Climatol.,112(1–2),285–306.

    Sinha,P.,U.C.Mohanty,S.C.Kar,and S.Kumari,2014:Role of the Himalayan orography in simulation of the Indian summer monsoon using RegCM3.Pure and Applied Geophysics,171, 1385–1407,doi:10.1007/s00024-013-0675-9.

    Tiedtke,M.,1993:Representation of clouds in large-scale models.Mon.Wea.Rev.,121,3040–3061.

    Tiedtke,M.,1989:A comprehensive mass fl ux scheme for cumulus parameterization in large-scale models.Mon.Wea.Rev, 117(8),1779–1800.

    Tchotchou,L.A.D.,and M.Kamga,2010:Sensitivity of the simulated African monsoon of summers 1993 and 1999 to convective parameterization schemes in RegCM3.Theor.Appl. Climatol.,100,207–220.

    Uppala,S.,D.Dee,S.Kobayashi,P.Berrisford,and A.Simmons, 2008:Towards a climate data assimilation system:Status update of ERA-Interim.European Centre for Medium-Range Weather Forecasts(ECMWF)Newsletter,No.115,12–18.

    Vannitsem,S.,and F.Chom′e,2005:One-way nested regional climate simulations and domain size.J.Climate,18(1),229–233.

    Wang,W.,and N.L.Seaman,1997:A comparison study of convectiveparameterizationschemesinamesoscalemodel.Mon. Wea.Rev.,125(2),252–278.

    Wang,B.,and R.Wu,1997:Peculiar temporal structure of the South China Sea summer monsoon.Adv.Atmos.Sci.,14(2), 177–194,doi:10.1007/s00376-997-0018-9.

    Wang,X.,Q.Bao,K.Liu,G.Wu,and Y.Liu,2011:Features of rainfall and latent heating structure simulated by two convectiveparameterizationschemes.ScienceChinaEarthSciences, 54(11),1779–1788.

    Wu,W.,A.H.Lynch,and A.Rivers,2005:Estimating the uncertainty in a regional climate model related to initial and lateral boundary conditions.J.Climate,18(7),917–933.

    Xue,Y.,R.Vasic,Z.Janjic,F.Mesinger,and K.E.Mitchell,2007: Assessment of dynamic downscaling of the continental US regional climate using the Eta/SSiB regional climate model.J.Climate,20(16),4172–4193.

    Yatagai,A.,K.Kamiguchi,O.Arakawa,A.Hamada,N.Yasutomi,and A.Kitoh,2012:APHRODITE:Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges.Bull.Amer.Meteor.Soc., 93(9),1401–1415.

    Yhang,Y.B.,and S.Y.Hong,2008:Improved physical processes in a regional climate model and their impact on the simulated summer monsoon circulations over East Asia.J.Climate,21(5),963–979.

    Zanis,P.,C.Douvis,I.Kapsomenakis,I.Kioutsioukis,D.Melas, and J.S.Pal,2009:A sensitivity study of the Regional ClimateModel(RegCM3)totheconvectiveschemewithemphasis in central eastern and southeastern Europe.Theor.Appl. Climatol.,97(3–4),327–337.

    Zhong,Z.,2006:A possible cause of a regional climate model’s failure in simulating the East Asian summer monsoon.Geophys.Res.Lett.,33,L24707,doi:10.1029/2006GL027654.

    :Ali,S.,L.Dan,C.B.Fu,and Y.Yang,2015:Performance of convective parameterization schemes in Asia using RegCM:Simulations in three typical regions for the period 1998–2002.Adv.Atmos.Sci.,32(5),715–730,

    10.1007/s00376-014-4158-4.

    (Received 11 July 2014;revised 20 September 2014;accepted 26 September 2014)

    ?Corresponding author:DAN Li

    Email:danli@tea.ac.cn

    高清午夜精品一区二区三区| 极品教师在线视频| 神马国产精品三级电影在线观看| 夜夜看夜夜爽夜夜摸| 99视频精品全部免费 在线| 欧美高清成人免费视频www| 亚洲av在线观看美女高潮| 女人十人毛片免费观看3o分钟| 亚洲国产av新网站| 一级毛片黄色毛片免费观看视频| 又爽又黄无遮挡网站| 亚洲精品影视一区二区三区av| 在线观看美女被高潮喷水网站| 国产视频首页在线观看| 亚洲成人精品中文字幕电影| 久久精品国产亚洲av涩爱| 搡老妇女老女人老熟妇| 亚洲欧美成人综合另类久久久| 国内精品美女久久久久久| 国产又色又爽无遮挡免| 干丝袜人妻中文字幕| 欧美一区二区亚洲| 久久久久久久久大av| 亚洲真实伦在线观看| 国内精品美女久久久久久| 在线a可以看的网站| 国产伦在线观看视频一区| 久久精品熟女亚洲av麻豆精品 | 亚洲欧美日韩无卡精品| 三级男女做爰猛烈吃奶摸视频| 国产亚洲精品av在线| 午夜精品国产一区二区电影 | 色网站视频免费| 国产乱人偷精品视频| 蜜桃久久精品国产亚洲av| 99热全是精品| 亚洲精品国产av成人精品| 好男人视频免费观看在线| 午夜福利视频精品| 我要看日韩黄色一级片| 国产真实伦视频高清在线观看| 亚洲精品aⅴ在线观看| 成人亚洲欧美一区二区av| 欧美人与善性xxx| 国产探花在线观看一区二区| 成人亚洲精品av一区二区| 国产精品熟女久久久久浪| 日日干狠狠操夜夜爽| 日韩成人av中文字幕在线观看| 最近视频中文字幕2019在线8| 一级片'在线观看视频| 又爽又黄a免费视频| 国精品久久久久久国模美| 男女边吃奶边做爰视频| 久久久久精品久久久久真实原创| 91精品一卡2卡3卡4卡| 在线免费观看的www视频| 亚洲精品日韩av片在线观看| 男人舔奶头视频| 永久免费av网站大全| 身体一侧抽搐| 亚洲国产高清在线一区二区三| 日韩国内少妇激情av| 女人久久www免费人成看片| 亚洲最大成人中文| 搞女人的毛片| 成人高潮视频无遮挡免费网站| 国产精品.久久久| 欧美xxⅹ黑人| 欧美日韩在线观看h| 亚洲精品乱久久久久久| 青春草亚洲视频在线观看| 一二三四中文在线观看免费高清| 久99久视频精品免费| 欧美极品一区二区三区四区| 国产成人精品一,二区| 欧美变态另类bdsm刘玥| 欧美区成人在线视频| 街头女战士在线观看网站| 草草在线视频免费看| 久久久久久久午夜电影| 日本爱情动作片www.在线观看| 超碰av人人做人人爽久久| 日韩欧美精品免费久久| 午夜激情欧美在线| 十八禁国产超污无遮挡网站| 国产综合懂色| 80岁老熟妇乱子伦牲交| 寂寞人妻少妇视频99o| 永久免费av网站大全| 男女啪啪激烈高潮av片| 男人舔女人下体高潮全视频| 午夜激情久久久久久久| 麻豆国产97在线/欧美| 午夜激情久久久久久久| 777米奇影视久久| 亚洲一级一片aⅴ在线观看| 搡女人真爽免费视频火全软件| 少妇人妻一区二区三区视频| 免费少妇av软件| 国产综合懂色| 内地一区二区视频在线| 日韩亚洲欧美综合| 国产高清不卡午夜福利| 夫妻性生交免费视频一级片| 国产熟女欧美一区二区| 成人亚洲欧美一区二区av| 精品少妇黑人巨大在线播放| 岛国毛片在线播放| 免费无遮挡裸体视频| 午夜福利在线观看免费完整高清在| av在线蜜桃| 日韩av不卡免费在线播放| 自拍偷自拍亚洲精品老妇| 国产精品福利在线免费观看| 国产伦精品一区二区三区视频9| 国产 亚洲一区二区三区 | 青春草视频在线免费观看| 国产成人精品一,二区| 国产综合懂色| 成人一区二区视频在线观看| 91狼人影院| 三级经典国产精品| 高清午夜精品一区二区三区| 久久热精品热| 欧美最新免费一区二区三区| 全区人妻精品视频| 三级经典国产精品| 亚洲精品,欧美精品| 国产真实伦视频高清在线观看| 国产精品无大码| 男人舔奶头视频| 中文欧美无线码| 成人国产麻豆网| 国产精品福利在线免费观看| 婷婷色综合大香蕉| 国产伦精品一区二区三区视频9| 在线免费观看的www视频| 日韩不卡一区二区三区视频在线| 国产精品日韩av在线免费观看| 青青草视频在线视频观看| 听说在线观看完整版免费高清| 色吧在线观看| 国产精品一区二区在线观看99 | 亚洲国产最新在线播放| 久久韩国三级中文字幕| 91在线精品国自产拍蜜月| 69人妻影院| 美女内射精品一级片tv| 能在线免费观看的黄片| 中文字幕免费在线视频6| 欧美高清成人免费视频www| 波多野结衣巨乳人妻| 亚洲色图av天堂| 国产精品1区2区在线观看.| 午夜福利在线观看免费完整高清在| 身体一侧抽搐| 又大又黄又爽视频免费| 最近最新中文字幕免费大全7| 九色成人免费人妻av| 国产视频首页在线观看| 亚洲av电影在线观看一区二区三区 | 欧美成人一区二区免费高清观看| 亚洲精品日本国产第一区| 精品久久久精品久久久| 久久精品综合一区二区三区| 美女cb高潮喷水在线观看| 水蜜桃什么品种好| 成人二区视频| av.在线天堂| 免费看a级黄色片| 久久精品久久久久久噜噜老黄| 精华霜和精华液先用哪个| 激情五月婷婷亚洲| 卡戴珊不雅视频在线播放| 亚洲经典国产精华液单| 国产精品1区2区在线观看.| 波野结衣二区三区在线| 欧美日本视频| 18+在线观看网站| 成年版毛片免费区| 三级国产精品欧美在线观看| 亚洲av中文av极速乱| 午夜福利在线在线| 亚洲内射少妇av| 青青草视频在线视频观看| 久久午夜福利片| 精品久久久久久久末码| 欧美成人a在线观看| 婷婷色综合www| 亚洲精品成人av观看孕妇| 日本黄色片子视频| 女的被弄到高潮叫床怎么办| 国产综合懂色| 色5月婷婷丁香| 看免费成人av毛片| 日韩欧美 国产精品| 国产精品福利在线免费观看| 欧美日韩精品成人综合77777| 亚洲伊人久久精品综合| 国产黄频视频在线观看| 22中文网久久字幕| 中文字幕av成人在线电影| 日产精品乱码卡一卡2卡三| 1000部很黄的大片| 国产精品一区二区性色av| 美女脱内裤让男人舔精品视频| 日韩欧美 国产精品| 亚洲真实伦在线观看| 精品人妻视频免费看| 少妇丰满av| 国产成人精品婷婷| 久久精品综合一区二区三区| av在线播放精品| 亚洲最大成人手机在线| 麻豆精品久久久久久蜜桃| 亚洲精品456在线播放app| 日日啪夜夜爽| 欧美日本视频| 国产探花极品一区二区| 欧美日韩综合久久久久久| 日韩av不卡免费在线播放| 国内揄拍国产精品人妻在线| 2018国产大陆天天弄谢| 午夜亚洲福利在线播放| 午夜老司机福利剧场| 国产精品嫩草影院av在线观看| 国产精品久久久久久久久免| av线在线观看网站| 国产成人a区在线观看| 日韩,欧美,国产一区二区三区| 男女边摸边吃奶| 黄片wwwwww| 九色成人免费人妻av| 午夜福利视频精品| 三级毛片av免费| 亚洲图色成人| 直男gayav资源| 中文在线观看免费www的网站| 久99久视频精品免费| 日韩欧美国产在线观看| 青春草视频在线免费观看| 亚洲最大成人中文| 高清日韩中文字幕在线| 有码 亚洲区| 国产成年人精品一区二区| 欧美日韩在线观看h| 久久久精品欧美日韩精品| 毛片女人毛片| 51国产日韩欧美| 91精品国产九色| 精品久久久久久久久久久久久| 一个人看视频在线观看www免费| 天天躁夜夜躁狠狠久久av| 热99在线观看视频| 又粗又硬又长又爽又黄的视频| 国产 亚洲一区二区三区 | 亚洲自拍偷在线| 久久精品国产自在天天线| 久久久久久伊人网av| 有码 亚洲区| av卡一久久| 亚洲精品成人av观看孕妇| 亚洲图色成人| 久久久精品免费免费高清| 网址你懂的国产日韩在线| 亚洲经典国产精华液单| 永久网站在线| av在线蜜桃| 久久久久久久久大av| 欧美日韩视频高清一区二区三区二| 亚洲欧美清纯卡通| 熟妇人妻不卡中文字幕| av专区在线播放| 国产精品一区二区在线观看99 | 亚洲国产精品专区欧美| 一级毛片电影观看| 国产亚洲av嫩草精品影院| 亚洲精品乱码久久久v下载方式| 国产探花在线观看一区二区| 人人妻人人澡人人爽人人夜夜 | .国产精品久久| 国产精品福利在线免费观看| 久久久久久久久久黄片| 国产黄色小视频在线观看| 老司机影院成人| 女的被弄到高潮叫床怎么办| kizo精华| 成人亚洲欧美一区二区av| 深夜a级毛片| 狂野欧美激情性xxxx在线观看| 亚洲精品日本国产第一区| 国产精品蜜桃在线观看| 成人亚洲精品av一区二区| 国产伦理片在线播放av一区| 一个人看的www免费观看视频| 国产免费一级a男人的天堂| 91aial.com中文字幕在线观看| 高清日韩中文字幕在线| 亚洲欧洲日产国产| 亚洲欧洲日产国产| 国产精品三级大全| 午夜福利视频1000在线观看| 成人二区视频| 小蜜桃在线观看免费完整版高清| 欧美激情久久久久久爽电影| 国产黄色免费在线视频| 国产欧美另类精品又又久久亚洲欧美| av在线天堂中文字幕| 国产老妇伦熟女老妇高清| 中文字幕久久专区| 亚洲成人av在线免费| 91aial.com中文字幕在线观看| av一本久久久久| 亚洲欧美中文字幕日韩二区| av在线老鸭窝| 亚洲电影在线观看av| 最新中文字幕久久久久| 青青草视频在线视频观看| 亚洲欧美中文字幕日韩二区| 国产日韩欧美在线精品| 国产精品一区二区三区四区免费观看| 亚洲电影在线观看av| 免费播放大片免费观看视频在线观看| 国产精品女同一区二区软件| 成年人午夜在线观看视频 | 老司机影院成人| 熟妇人妻不卡中文字幕| av在线亚洲专区| 一区二区三区高清视频在线| 亚州av有码| 亚洲aⅴ乱码一区二区在线播放| 欧美xxxx性猛交bbbb| 国产成人精品一,二区| 大陆偷拍与自拍| 深夜a级毛片| 天美传媒精品一区二区| 51国产日韩欧美| 成人美女网站在线观看视频| 免费无遮挡裸体视频| 97人妻精品一区二区三区麻豆| 黄片无遮挡物在线观看| av又黄又爽大尺度在线免费看| 日韩欧美精品免费久久| 午夜久久久久精精品| 成人亚洲精品一区在线观看 | 中文字幕制服av| 18+在线观看网站| 日本-黄色视频高清免费观看| 亚洲欧洲国产日韩| 亚洲欧洲日产国产| 好男人视频免费观看在线| 晚上一个人看的免费电影| 国产伦精品一区二区三区四那| 成年女人看的毛片在线观看| 最近最新中文字幕免费大全7| 美女内射精品一级片tv| 久久精品国产亚洲av天美| 国产精品1区2区在线观看.| 亚洲欧洲国产日韩| 嫩草影院入口| 精品久久久久久成人av| 欧美高清成人免费视频www| 国产永久视频网站| 色综合站精品国产| 97精品久久久久久久久久精品| 一级毛片aaaaaa免费看小| 国产高潮美女av| 97超视频在线观看视频| 一区二区三区乱码不卡18| 天堂影院成人在线观看| 免费无遮挡裸体视频| 成人国产麻豆网| 卡戴珊不雅视频在线播放| 久久人人爽人人片av| 99热这里只有是精品50| 人人妻人人澡人人爽人人夜夜 | 免费看a级黄色片| 久久久久久久午夜电影| 97精品久久久久久久久久精品| 免费看不卡的av| 免费高清在线观看视频在线观看| av一本久久久久| 成年免费大片在线观看| 亚洲,欧美,日韩| 精品酒店卫生间| 亚洲精品视频女| 国产成人精品婷婷| 最后的刺客免费高清国语| 日韩欧美国产在线观看| 色综合色国产| 自拍偷自拍亚洲精品老妇| 听说在线观看完整版免费高清| 麻豆av噜噜一区二区三区| 亚洲在线自拍视频| h日本视频在线播放| 精品一区二区免费观看| 亚洲高清免费不卡视频| 伊人久久国产一区二区| 一个人看的www免费观看视频| 日韩精品有码人妻一区| 天天躁日日操中文字幕| 亚洲成人一二三区av| 日韩欧美一区视频在线观看 | 狂野欧美白嫩少妇大欣赏| kizo精华| 不卡视频在线观看欧美| 精品不卡国产一区二区三区| 日韩伦理黄色片| a级毛片免费高清观看在线播放| 国产黄色视频一区二区在线观看| 99久久精品国产国产毛片| 国产永久视频网站| 国产精品久久久久久av不卡| 欧美最新免费一区二区三区| 欧美成人精品欧美一级黄| 少妇丰满av| 国产精品精品国产色婷婷| 99热这里只有是精品在线观看| 成人欧美大片| 国产精品国产三级专区第一集| 日日摸夜夜添夜夜爱| 亚洲精品成人av观看孕妇| 又黄又爽又刺激的免费视频.| 国产精品久久久久久久电影| 亚洲av.av天堂| 亚洲欧美一区二区三区国产| 可以在线观看毛片的网站| 看免费成人av毛片| 亚洲av免费在线观看| 韩国av在线不卡| 人妻制服诱惑在线中文字幕| 精品久久久久久电影网| 中国国产av一级| 高清在线视频一区二区三区| 免费看光身美女| 国产精品久久久久久精品电影| 国产黄片视频在线免费观看| 国产久久久一区二区三区| 国产精品一区二区三区四区免费观看| 久久久久精品久久久久真实原创| 能在线免费看毛片的网站| 国产色爽女视频免费观看| 中文天堂在线官网| 色哟哟·www| 毛片女人毛片| 别揉我奶头 嗯啊视频| 在线a可以看的网站| 日韩制服骚丝袜av| 免费观看无遮挡的男女| 亚洲精品国产av蜜桃| 亚洲经典国产精华液单| 亚洲真实伦在线观看| 夫妻午夜视频| 国产中年淑女户外野战色| 80岁老熟妇乱子伦牲交| 久久久久久久久久黄片| 一二三四中文在线观看免费高清| 亚洲国产色片| 亚洲精品国产成人久久av| 色哟哟·www| 大片免费播放器 马上看| 国产69精品久久久久777片| 啦啦啦韩国在线观看视频| 精品少妇黑人巨大在线播放| 男女边摸边吃奶| 国产成人精品婷婷| 国产精品人妻久久久影院| 亚洲综合精品二区| 成年av动漫网址| 欧美不卡视频在线免费观看| 国产高潮美女av| 亚洲精品乱久久久久久| 一个人观看的视频www高清免费观看| 国产老妇伦熟女老妇高清| 久久精品国产亚洲网站| 久久亚洲国产成人精品v| 男女边摸边吃奶| 亚洲国产欧美在线一区| 欧美成人一区二区免费高清观看| 免费看日本二区| 熟女电影av网| 日韩大片免费观看网站| 久久韩国三级中文字幕| 老师上课跳d突然被开到最大视频| 国产毛片a区久久久久| 美女大奶头视频| 淫秽高清视频在线观看| 最近2019中文字幕mv第一页| 人妻一区二区av| 亚洲自偷自拍三级| 一级av片app| 老司机影院毛片| 亚洲最大成人av| 国产色爽女视频免费观看| 日韩一本色道免费dvd| 国语对白做爰xxxⅹ性视频网站| 亚洲丝袜综合中文字幕| 国产精品福利在线免费观看| 大片免费播放器 马上看| 成人午夜精彩视频在线观看| 久久久久精品久久久久真实原创| 在线免费十八禁| 日韩,欧美,国产一区二区三区| 日韩三级伦理在线观看| 国产单亲对白刺激| 麻豆成人av视频| 亚洲精品乱久久久久久| 亚洲不卡免费看| 黄色一级大片看看| 日日撸夜夜添| 亚洲成人一二三区av| 国产精品一区二区在线观看99 | 伊人久久精品亚洲午夜| 有码 亚洲区| 国产色婷婷99| 99久久精品热视频| 亚洲精品456在线播放app| 尾随美女入室| 床上黄色一级片| 欧美丝袜亚洲另类| 国产成人freesex在线| 亚洲自偷自拍三级| 韩国av在线不卡| 亚洲色图av天堂| 国产国拍精品亚洲av在线观看| 国产美女午夜福利| 内射极品少妇av片p| 免费播放大片免费观看视频在线观看| 偷拍熟女少妇极品色| 91精品国产九色| 边亲边吃奶的免费视频| 免费看美女性在线毛片视频| 韩国av在线不卡| 国产伦在线观看视频一区| 日韩欧美精品v在线| 中文精品一卡2卡3卡4更新| 成人无遮挡网站| 欧美成人精品欧美一级黄| 一级毛片黄色毛片免费观看视频| 男女下面进入的视频免费午夜| 国模一区二区三区四区视频| 久久久a久久爽久久v久久| 婷婷色麻豆天堂久久| 激情 狠狠 欧美| 国产乱来视频区| 少妇熟女aⅴ在线视频| 久久久久久久久大av| 六月丁香七月| 欧美成人午夜免费资源| 欧美变态另类bdsm刘玥| 欧美日韩在线观看h| 午夜精品在线福利| 伦精品一区二区三区| 久久久久久久久久久免费av| 亚洲国产精品sss在线观看| 亚洲av成人av| 国产片特级美女逼逼视频| 久久久久国产网址| 一级片'在线观看视频| 亚洲av在线观看美女高潮| 免费高清在线观看视频在线观看| 午夜福利视频1000在线观看| av在线亚洲专区| 国产黄a三级三级三级人| 欧美高清性xxxxhd video| 日本与韩国留学比较| 久久久色成人| 国产精品.久久久| 欧美性猛交╳xxx乱大交人| 亚洲在线观看片| 日韩中字成人| 精品人妻熟女av久视频| 亚洲,欧美,日韩| 国产精品三级大全| av在线老鸭窝| 日韩中字成人| 日韩 亚洲 欧美在线| 日韩欧美 国产精品| av国产久精品久网站免费入址| 伦精品一区二区三区| 亚洲欧洲日产国产| 伊人久久国产一区二区| 欧美日韩精品成人综合77777| 麻豆精品久久久久久蜜桃| 国产女主播在线喷水免费视频网站 | 国产三级在线视频| 能在线免费观看的黄片| 欧美日韩一区二区视频在线观看视频在线 | 久热久热在线精品观看| 男的添女的下面高潮视频| 男插女下体视频免费在线播放| 亚洲最大成人中文| 久久久精品欧美日韩精品| 国产激情偷乱视频一区二区| 七月丁香在线播放| 狠狠精品人妻久久久久久综合| 国产av在哪里看| 久久国内精品自在自线图片| 亚洲经典国产精华液单| 一个人免费在线观看电影| 一级毛片aaaaaa免费看小| 97超视频在线观看视频| 欧美+日韩+精品| 免费看美女性在线毛片视频| 中国美白少妇内射xxxbb| 噜噜噜噜噜久久久久久91| 国产日韩欧美在线精品| 我的女老师完整版在线观看| 中文字幕亚洲精品专区| 少妇人妻一区二区三区视频| 久久精品夜色国产| 亚洲真实伦在线观看| 人妻系列 视频| 日本与韩国留学比较|