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

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).

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

?Corresponding author:DAN Li

Email:danli@tea.ac.cn