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Atmospheric Environment 45 (2011) 727e735

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Atmospheric Environment

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Effects of convective parameterization schemes on estimation of the annualwet deposition over Northeast Asia

Hong-Rae Kim, Yun-Jong Kim, Cheol-Hee Kim*

Department of Atmospheric Sciences, Pusan National University, San 30, Jangjeon-Dong, Geumjeong-Gu, Busan 609-735, South Korea

a r t i c l e i n f o

Article history:Received 21 January 2010Received in revised form9 September 2010Accepted 17 September 2010

Keywords:Convective parameterizationsPrecipitationWet depositionNortheast Asia

* Corresponding author. Tel.: þ82 51 510 3687; faxE-mail address: chkim2@pusan.ac.kr (C.-H. Kim).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.09.031

a b s t r a c t

This paper presents technique used to estimate annual total wet depositions of NO3� and SO4

2�, anddescribes their sensitivities arising from various convective parameterization schemes over NortheastAsia. The representative synoptic meteorological conditions for the precipitation were identified byemploying a cluster analysis technique, and four cumulus convective parameterization schemes, theAntheseKuo (AK), BettseMiller (BM), Grell (GR), and KaineFritsch 2 (KF2) schemes, were applied toestimate annual wet deposition simulations. The four convective parameterization schemes were foundto reproduce the overall observed precipitation band for each of the classified synoptic patterns. Whencluster analysis was used with these four schemes, the estimated annual total wet depositions of SO4

2�

and NO3� over South Korea were found to reach 184e197 and 277e337 kton year�1, respectively, with the

highest estimation found with the KF2 scheme. These estimates were higher than the results of thecontinuous full year-long simulations by three dimensional comprehensive acid deposition model, whichfound values of 130 kton year�1 for SO4

2� and 270 kton year�1 for NO3�. There was a 15.2% variability in

the annual total precipitation from the use of the different convective parameterizations of the fourschemes, but the annual total wet depositions of the four cloud parameterization schemes were in goodagreement, with estimated variabilities of approximately 9.1 and 8.8% for SO4

2� and NO3�, respectively. At

less than w10%, these variations were small and negligible in an estimation of the long-term depositionsover the region of Korean Peninsula.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

The rapid increase in the emissions of SO2, NOx, VOCs, andparticulate matter in East Asia has led to a series of environmentalproblems such as the deterioration of ecosystems, air quality issues,and environmental acidification. Of these, acid deposition hasbecome a key issue in Northeast Asia over the last few decadesbecause of the significant damage caused to terrestrial and aquaticecosystems and the harmful effects of long-range transport amongregions (Tamm, 1991; Aber et al., 1989). Generally, two types ofdeposition processes are used for the removal of pollutants from theatmosphere to the surface: dry deposition and wet deposition. Drydeposition refers to the direct collection of gases and particulates onland orwater surfaces.Wet deposition refers to the removal of gasesand particulates by falling precipitation (washout) and rainout.Clouds and precipitation play a critical role in the removal of airpollutants via wet deposition. Wet deposition initially proceeds

: þ82 51 515 1689.

All rights reserved.

with cloud formation via heterogeneous nucleation (Hallberg et al.,1997; Andronache, 2004) and aerosol activation (Zhang et al., 2002),and then with in-cloud scavenging by existing clouds (Hallberget al., 1997; Andronache, 2004) or below-cloud scavenging byfalling precipitation (Andronache, 2004), or both.

All of the physical and chemical processes of clouds influencethe amount and composition of surface precipitation, and may leadto acidic precipitation and adverse environmental effects. Anadditional complication when attempting to accurately simulatewet deposition is that it depends not only on the meteorologicalprocesses and parameters but also on the ambient concentrationsof the depositing species in all phases. These concentrations are, inturn, affected by numerous processes such as emissions, transport,gas and aqueous-phase chemistries, aerosol thermodynamics anddynamics, cloud processing of gases and aerosols, and theirremoval by dry and wet depositions. Because of the complexity ofthe interactions involved in the formation, transport, and removalof gases and aerosols, accurate representation of the processes ofdry and wet depositions by means of numerical models is difficult.However, realistic meteorological and chemical model simulationscannot be achieved without a proper representation of cloud and

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735728

precipitation processes. The impact of any model errors associatedwith the representation of cloud processes cannot be well under-stood without assessing the appropriateness of the model’sconfiguration and associated uncertainties, as well as the likelycauses of any discrepancies between the meteorological andchemical predictions of the model and the observations.

The cloud and precipitation processes in atmospheric modelsare handled as two components (1) explicit moisture schemes forgrid-scale resolved clouds and (2) convective parameterizationschemes (CPSs) for sub-grid convective clouds. The explicit mois-ture scheme calculates the cloud properties and precipitation forgrid-resolvable clouds by parameterization of the microphysics,such as the processes for condensation, growth of raindrops, andevaporation of falling raindrops. The convective parameterizationscheme parameterizes a sub-grid convective cloud and its precip-itation on the basis of several implicit assumptions and closureconstraints. Recent studies have revealed that meteorologicalpredictions are sensitive to explicit microphysics schemes andhorizontal resolutions (Gilmore et al., 2004; Queen et al., 2008;Queen and Zhang, 2008a,b). In these studies, the simulatedamounts of wet deposition were found to be appreciably sensitiveto explicit microphysics schemes.

However, convective cloud schemes in association with wetdeposition amounts are another source of uncertainty in modelingstudies. Previous analyses have reported that it is still uncertainwhether different convective cloud schemes can actually influencelong-term wet deposition, given that the different amounts ofprecipitation forced by the different options of convective param-eterization schemes are still apparent forces in the cloud formationprocess. Therefore, this study examined various convectiveparameterization schemes and estimated the impact of the variousoptions of simulating precipitation on long-term wet depositionover Northeast Asia using the fifth generation National Center forAtmospheric Research/Pennsylvania State University (NCAR/PSU)mesoscale model (MM5) (Grell et al., 1994) and the US EPAcommunity multiscale air quality (CMAQ) modeling system (Byunand Schere, 2006). The four convective parameterization schemesincluded the Anthes and Kuo (AK), Betts andMiller (BM), Grell (GR),and new KaineFritsch (KF2) schemes. To project the long-termprecipitation and annual total wet depositions of SO4

2� and NO3�,

a cluster analysis technique was applied and identified the repre-sentative synoptic patterns (clusters) associated with precipitation

100 105 110 115 120 125 130 135 140 1

LON.

20

25

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.T

AL

A

B

C

D

EChina

Korea

Japan

Shantung peninsula

Fig. 1. Model domains of the MM5 (outer) and CMAQ (dashed inner box), and the locatioindicates the domain used for computing the precipitation and wet deposition variabilities

over Northeast Asia. The major uncertainties and variabilities in thesimulated long-term precipitation and annual total wet depositionsof SO4

2� and NO3� were discussed in associationwith the problem of

transboundary air pollution over Northeast Asia.

2. Model domain, methodology, and data used

2.1. Study domain, model description, and convectiveparameterization schemes

This study used the MM5/CMAQmodeling systemwith a 60-kmhorizontal resolution over East Asia, centered on South Korea(Fig. 1). The study area covered Northeast Asia, including all ofChina, Mongolia, the Korean Peninsula, and Japan. There were90 � 60 horizontal grid points, with 29 vertical levels.

The MM5 was run using a modified MRF PBL (planetaryboundary layer), Reiner2 microphysics, and an RRTM (rapid radia-tive transfer model) radiation scheme. The observed surface andupper air measurements in this regionwere also used for the FDDA(four dimensional data assimilation) in the MM5. The initialmeteorological field in the MM5 was obtained from NCEP 1degree � 1 degree reanalysis datasets (DS083.2) with 6 h intervals.CMAQ (v4.6) was run with the gas-phase chemistry of the carbon-bond IV mechanism, configured with the AERO-3 aerosol module.

Four convective parameterization schemes coupled in the MM5model were used: the AntheseKuo (AK: Grell et al., 1994),BettseMiller (BM: Betts and Miller, 1986), Grell (GR: Grell et al.,1994), and KaineFritsch 2 (KF2: Kain, 2002) schemes. TheAntheseKuo scheme is based on column-integrated moistureconvergence, which is applicable if the horizontal grid size isgreater than 30 km. The feedback to the vertical distribution ofheating andmoistening is determined from the normalized verticalprofiles of convective heating and moistening, as well as thevertical eddy-flux divergence of water vapor. The Grell scheme isa one-cloud version of the ArakawaeSchubert scheme, but withparameterized downdrafts, which was originally applied in thePSU-NCAR model. In the GR scheme, clouds are pictured as twosteady-state circulations, caused by an updraft and a downdraft.There is no directmixing between cloudy air and environmental air,except at the top and bottom of the circulations. Note that no cloudwater is assumed to exist in the GR scheme, and all of the water isconverted to rain. The closure used by the GR scheme is the

45 150

A : Seoul

B : Busan

C : Ganghwa

D : Taean

E : Gosan

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Yellow

Sea

ns of the meteorological (AeB) and monitoring sites (CeE). The thick solid inner box.

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735 729

destabilization or quasi-equilibrium, which can be applied if thehorizontal grid size is greater than 10 km. The KF2 scheme is a newversion of the KaineFritsch scheme (Kain and Fritsch, 1993) thatincludes shallow convection. The KaineFritsch scheme is similar tothe FritscheChappell scheme, with improvements to the detrain-ment effect and cloud model. Convection in the KF2 is determinedby the convective available potential energy (CAPE) at a grid point.Once convection is triggered, the CAPE is assumed to be removedfrom a grid column within an advective time period (Kain andFritsch, 1993). Unlike the other three CPSs, the BM scheme isa lagged convective adjustment scheme, which adjusts the model’sthermal and moisture structures to specified reference profiles thatreflect the quasi-equilibrium state established by deep convection(Betts and Miller, 1986). The details of each of these four convectiveparameterization schemes can be found in the literature (Grellet al., 1994; Kain, 2002; Betts and Miller, 1986).

2.2. Cluster analysis

It is difficult to conduct a long-term simulation of meteorologicalfields with various detailed cumulus parameterization options ona regional scale due to many reasons such as the complexity and lackofmodeling input data. One option for a long-termmodeling study isto statistically identify the number of representative periods. If theidentified periods are representative of typical meteorologicalconditions, the annual or long-term deposition can be estimatedwithout heavy computational costs, by aggregating the short timeperiod simulationsderived fromtheuseof a small numberof episodicmodel runs. Inmanyprevious studies, distinct synoptic patternswereidentified using cluster analysis to develop a synoptic climatology(Eder et al., 1994).

In this study, the cluster analysis techniquewas used to estimatethe amounts of long-term precipitation and wet deposition in 2002over the Northeast Asia, and the results of Korean Peninsula werecompared to those of a continuous year-long simulation by threedimensional comprehensive acid deposition model.

The cluster analysis technique employed here was a three-stepprocess consisting of a principal component analysis (PCA) and twostages of cluster analysis techniques. It was used to classify severalrepresentative clusters of synoptic patterns from a large number ofinterrelated datasets. Eder et al. (1994) stated that the central idea ofa PCA is to reduce the dimensionality of a voluminous dataset con-sisting of a large number of interrelated input datasets to explain thevariance. Here, the size of the dataset was reduced using a PCA, withthe original data matrix reconstructed into representative andprincipal components, in which most of the essential informationwas explainable by retaining the first few principal components. Thenext step involved a two-stage clustering technique that used bothhierarchical (average linkage) and non-hierarchical techniques(K-mean clustering). First, a hierarchical method was used to deter-mine the initial number of clusters, which was subsequently modi-fied using a non-hierarchical method to produce the final solution.

Hourly observation surface meteorological data were used toselect the days with precipitation. Individual days were classified asprecipitation days when precipitation was observed in both Seouland Pusan, in South Korea, as indicated in Fig. 1. This resulted ina total of 59 days with precipitation in 2002. For these days withprecipitation, daily 1.5-km-height pressure fields were obtainedfrom the 60 � 90 model grid points of a year-long continuous runby CSU RAMS (Colorado State University regional atmosphericmodeling system; Pielke et al., 1992), with the 59 precipitation daysformulated into an original matrix using the 59 days � 5400 gridpoints (¼318,600 analyses) as the dimensions. CSU RAMS isused in the Comprehensive Acid Deposition Model (CADM; Parket al., 2005) as a meteorological model in the Long-range

Transboundary Air Pollutants in Northeast Asia (LTP) projectorganized by China, Japan, and Korea (NIER, 2005, 2009).

A weighting technique in the PCA was applied to the 1.5-km-height pressure pattern chosen for the annual total precipita-tion days. The amount of precipitation observed was used asa weighting factor to produce a statistically more distinctivedecomposition by giving a relative weight to each precipitation dayin the input dataset. Only the first few resultant principal compo-nents were derived, which exhibited a large total variation. Theappropriate number of retained principal components wasapproximated using the screen test (Cattell, 1966).

The principal components derived from both the hierarchicaland non-hierarchical algorithms were employed in the two-stageclustering techniques. The hierarchical method was used to deter-mine the number of clusters in advance on the basis of the statis-tical indices (R2, pseudo-F, and pseudo-t2) between clusters fora given number of clusters. The final solution was then producedusing the non-hierarchical method. This two-stage algorithm hasbeen recommended because it is superior to one-stage approachessuch as those that only use the average linkage in terms of clustercohesiveness (Eder et al., 1994). More complete clustering algo-rithms were described by Eder et al. (1994) and Davis et al. (1998).

The representative clusters identified from the three-stepcluster analysis were used to estimate the total annual precipitationand wet deposition from a few disaggregated numerical experi-ments based on the MM5/CMAQmodeling system. The final annualaggregated estimate can be estimated by using the weightingfunction: Vannual ¼ P

i¼1Wi$Vi, where Vannual is the annual estimate

of V, Vi is the result (precipitation or wet deposition) of the modelsimulation for the representative date (cluster), and Wi is theweight of each cluster and the eigen value for each cluster used.

2.3. Data used

We run the CSU RAMS (version 4.4) for the entire year of 2002 inadvance, with the full-year 1.5-km-height pressure fields generatedand used as the input dataset for the cluster analysis method. TheCSU RAMSwas used for a 4-dimensional data assimilation using theanalysis fields, with the simulated wind and temperature valuesfrom the analysis found to be close to those observed (NIER, 2005).

The emission data used here were the same as employed inthe previous study and in the LTP project (NIER, 2005). Fig. 2 showsthe spatial distributions of the rates of SO2 and NOx emissions. Theinitial concentrations were assumed to be zero, with the exceptionof O3, for which an initial concentration of 40 ppb was assumed atthe inflow boundaries.

The daily wet depositionmonitoring data observed at three sitesin South Korea, in Ganghwa, Taean, and Gosan (Fig. 1), werecompared to the results of the model simulation. The GlobalPrecipitation Climatology Project (GPCP) data (Huffman et al.,2001), which has a 1-degree resolution, were used to verify thesimulated precipitation.

3. Results and discussion

3.1. Results of cluster analysis

Table 1 shows the results of both the PCA and hierarchical algo-rithm: pseudo-F, pseudo-t2, and R2. The PCA results suggest that fourprincipal components of the matrix (5400 � 59) are able to explain74.15% of the total variance in the first step of the cluster analysistechnique. In accordance with the technical guideline for a clusteranalysis, the final number of clusters was selected when both thepseudo-F andpseudo-t2weremaximal, with the largest drop in R2 asthe number of clusters decreased (denoted in boldface in Table 1). In

Table 2Number of days with precipitation, representative dates and the simulation periodsfor each identified clusters.

Cluster Number of days (%) Representative date Simulation period

C1 30 (50.85) July 20 July 17eJuly 23, 2002C2 18 (30.51) May 15 May 12eMay 18, 2002C3 11 (18.64) April 29 April 26eMay 2, 2002

10 20 30 40 50 60 70 80 90

X (grid)

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r)di

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SO2(moles/s)

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)dirg(Y

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NOx(moles/s)

a

b

Fig. 2. Horizontal distribution of the rates of emissions (moles s�1) for (a) SO2 and (b)NOx.

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735730

this study, three clusterswere selected from the results inTable 1 andused as input parameters to formulate the non-hierarchical algo-rithm (K-mean analysis) to generate the final cluster solution. Thisnon-hierarchical algorithm is an iterative approach that allows all ofthe episodes to be reclassified, even after they have been groupedinto the same cluster via the hierarchical algorithm.

Table 2 shows the final selection of representative dates and thestatistical indices for three identified clusters. Hereafter, clusters 1,2, and 3 will be referred to as patterns C1, C2, and C3. Weathercharts for each of the three clusters were interpreted, withnumerical simulations performed to determine the range of typicaland noticeable differences in the precipitation conditions for thethree clusters. Seven day simulation periods were considered foreach cluster, including three days before and three after therepresentative dates, while considering the model spin-up time.

Fig. 3 shows the 850-mb weather charts for each of the threepatterns. Pattern 1 (C1) accounted for 50.9% of the total days withprecipitation. In the 850-mb weather chart (Fig. 3a) for July 20,

Table 1Statistics for the first five principal components and statistical indices for deter-mining the number of clusters retained.

Number ofclusters

Pseudo-F Pseudo-t2 R2 Eigenvalue

Explainedvariance (%)

Cumulativeexplainedvariance (%)

1 e 24.48 0.00 1685.62 31.22 31.222 24.48 27.08 0.30 1217.63 22.55 53.763 31.15 12.10 0.52 579.08 10.72 64.494 24.54 17.61 0.57 521.53 9.66 74.155 22.72 31.44 0.63 409.15 7.635 81.08

Fig. 3. 850 hPa weather charts at 1200 UTC on (a) July 20 (C1), (b) May 15 (C2), and (c)April 29 (C3) of 2002.

2002. 59-day Rainfall (mm). Obs.

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1003005007009001100130015001700

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2002. Annual estimate of Rainfall (mm). AK.

100500100020004000600080001000012000

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602002. Annual estimate of Rainfall (mm). BM.

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100500100020004000600080001000012000

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2002. Annual estimate of Rainfall (mm). GR.

100500100020004000600080001000012000

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602002. Annual estimate of Rainfall (mm). KF2.

100500100020004000600080001000012000

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a

b

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Fig. 4. Spatial distribution of the estimated annual observed (GPCP) and simulated precipitation: a) GPCP data, b) AK, c) BM, d) GR, and e) KF2.

400

600

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1200

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1600

P)

mm(

noitatipice

Annual Estimate of Domain Averaged Precipitation

STD = 15.2 %

146.4 %

111.2 %

171.7 %

138.6 %

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735 731

a band of low pressure existed from southern China to northeastJapan, with the North Pacific high pressure system located over thesouthern part of Japan. Precipitation was widely observed oversouthern China, South Korea, and Japan, representing the typicalsynoptic pressure pattern of heavy rainfall (called Changma periodin Korea) over Northeast Asia during summer.

Pattern 2 (C2) accounted for approximately 30.5% (18 rainy days)of the 59 rainy days. In the 850-mbweather chart for May 15, 2002(Fig. 3b), the weather over Korea, Japan, and eastern China wasinfluenced by a low surface pressure system centered on the yellowsea, and it was very cloudy and rainy in these regions. This lowpressure system originated in the south China region, movednorth-eastward, and gained in intensity. These events arefrequently observed during both cold and warm seasons.

Pattern 3 (C3) accounted for 18.6% (11 rainy days) of the 59 days.In the 850-mb weather chart for April 29, 2002 (Fig. 3c), a troughsteadily extended southwestward, and a low pressure systemlocated over eastern Russia passed over eastern China, headingtoward Korea. In the surface chart (not shown), the low pressuresystem over eastern China approached Korea, producing rainfallover eastern China, Korea, and western Japan.

0

200

OBS AK BM GR KF2

Fig. 5. The domain averaged amounts of rainfall observed and found from the foursimulations (AK, BM, GR, and KF2) over the target domain (Fig. 1).

3.2. Results of simulated precipitation

In order to assess the feasibility of this approach, the annualtotal precipitation was estimated using the weighting function of

the 3 clustered cases, and its uncertainty and variability wereanalyzed. The annual precipitation was calculated usingPannual ¼ PC1�30þ PC2� 18þ PC3� 11, where Pannual denotes theestimated annual precipitation, and PC1, PC2, and PC3 are thesimulated daily precipitations for the three representative cases, C1,C2, and C3, respectively.

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735732

Fig. 4 shows both the annual rainfall GPCP data and the simu-lated annual precipitations estimated from the three cluster cases.The observed precipitation band from southern China to the northof Japan, through South Korea, was reproduced by the CPS simu-lations with a certain degree of success, although there appeared tobe some underestimations over central and northern China, alongwith overestimations over southern China, the area south of theYellow Sea, and southern Japan. The precipitation over South Koreawas slightly underestimated because unrealistic precipitationbands were simulated over the area south of the Yellow Sea andJapan, and excessive rainfall cells were located over areas south ofJapan and north of Korea.

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2002. Annual estimate of Wet dep. SO4-2 . AK.

11020304050607080

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2002. Annual estimate of Wet dep. SO4-2 . BM.

11020304050607080

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2002. Annual estimate of Wet dep. SO4-2 . GR.

11020304050607080

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2002. Annual estimate of Wet dep. SO4-2 . KF2.

11020304050607080

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a

b

c

d

Fig. 6. The spatial distribution of the estimated annual wet depositions (kg ha�1) of SO42� (le

(c) and (g) GR, and (d) and (h) KF2.

The variability of the simulated precipitation arising from thefour different CPS options was also analyzed. Fig. 5 illustrates theannual total precipitations from the GPCP data and the spatiallyaveraged precipitation, along with the deviation of the fourdifferent CPS runs over the sub-domain centered on South Korea(thick solid inner box as indicated in Fig. 1). Over the entire modeldomain, the estimated amounts of annual precipitation in all of theCPS runs were very close to that observed, ranging from 101.4% forAK to 91.6% for GR. However, the amounts of precipitation over thesub-domain were overestimated in the CPS runs. The ratio of thesimulated precipitation to GPCP data ranged from 111.2% for BM to171.7% for GR, with the standard deviation (15.2%) relatively larger

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2002. Annual estimate of Wet dep. NO3- . AK.

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2002. Annual estimate of Wet dep. NO3- . BM.

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2002. Annual estimate of Wet dep. NO3- . GR.

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2002. Annual estimate of Wet dep. NO3- . KF2.

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e

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ft panel) and NO3� (right panel) from all four simulations: (a) and (e) AK, (b) and (f) BM,

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735 733

than that for the entire domain (4.0%) due to the overestimatedprecipitations in C1 and C3 over the southern part of South Korea.This variability was due to this target region being located betweenthe continent and ocean, and thereby being affected by thedifferent responses of the convective parameterization schemes tothe characteristic synoptic features over northeast Asia. Theseeffects tend to produce a heavy rainfall band across the KoreanPeninsula and result in differences in the precipitation positionsand rainfall amounts for the convective parameterization schemes.In Lee and Park (2002), the simulated maximum precipitationposition was revealed to have a positioning error ranging from 100to 200 km, and the simulated maximum rainfall amounts wereabout 70% of those observed. Therefore, even a small positioningerror can result in a large variability in the amount of precipitationover the inner domain.

3.3. Results of wet deposition and comparison with measurements

Fig. 6 shows the spatial distributions of the amounts of annualwet depositions of SO4

2� and NO3� estimated by theweighted sum of

the three clusters (C1, C2, and C3). Each of the four CPS simulationshad similar overall patterns of wet deposition distributions for SO4

2�

and NO3�, with no significant bias. A high wet deposition region for

SO42� and NO3

� was found over the Shantung Peninsula of centralChina and the Yellow Sea, along with a region of high precipitationalong the southern coastline of China in all of the CPS simulations,reflecting the precipitation patterns and high emission sourceslocated over the downwind regions.

Table 3 shows the annual estimates of the wet depositionamounts for both SO4

2� and NO3� obtained from all of the CPS

simulations, along with the corresponding measurements at threemonitoring sites: Ganghwa, Taean, and Gosan, as indicated in Fig. 1.The simulated wet depositions of SO4

2� were 15e20% lower thanthe measurements at Ganghwa and Taean, and 37e45% belowthose at Gosan, indicating that all the CPS simulations under-estimated the SO4

2� wet depositions. The only value that wasnumerically closewas the AK simulation of SO4

2� at Taean, with onlysmall differences between the simulation and measurement. Thesimulated NO3

� wet depositions were found to vary from about 2 toover 5 kg ha�1, with underestimations at both Ganghwa and Taean(except for AK, which only overestimated). However, the simulatedNO3

� wet depositions at Gosan were within a few percent of the

Table 3Observed and simulated daily and annual amounts of wet depositions of (a) SO4

2� and (b

Cluster 1 (7/20) Cluster 2 (5/15)

Ganghwa Taean Gosan Ganghwa Taean Gosan

(a) SO4�2 (kg ha�1)

OBS 0.53 <0.01 0.26 0.20 0.37 0.10AK 0.02 0.25 0.10 <0.01 <0.01 <0.01BM <0.01 <0.01 0.06 <0.01 <0.01 <0.01GR 0.03 <0.01 0.10 <0.01 <0.01 <0.01KF2 <0.01 <0.01 0.08 <0.01 <0.01 <0.01

(b) NO3� (kg ha�1)

OBS 0.44 <0.01 0.22 0.19 0.31 0.12AK 0.03 0.36 0.24 <0.01 <0.01 <0.01BM <0.01 <0.01 0.11 <0.01 <0.01 <0.01GR 0.04 <0.01 0.24 <0.01 <0.01 <0.01KF2 <0.01 <0.01 0.20 <0.01 <0.01 0.01

(c) Precipitation (mm)OBS 19.5 <0.1 14.9 4.3 12.2 8.0AK 1.1 0.4 54.7 0.1 <0.1 <0.1BM <0.1 <0.1 75.8 <0.1 <0.1 <0.1GR 0.1 <0.1 124.7 <0.1 <0.1 <0.1KF2 <0.1 <0.1 71.4 <0.1 <0.1 <0.1

measurements. As a result, all the CPS simulations were found tounderestimate the annual total wet depositions of both SO4

2� andNO3

�. The potential reasons for these discrepancies include theuncertainties in the model input data such as the emission data andmeteorological data, which had a coarse horizontal resolution of60 km.

However, the variations arising from the four CPS options werenot pronounced at all three measurement sites for both SO4

2� andNO3

�, with the exception of AK at Taean. The only values that werenumerically close were the AK simulations of the SO4

2� and NO3� at

Taean, with only small differences between the simulation andmeasurement. The overall underestimations of the SO4

2� and NO3�

at Ganghwa and Taean were partly because none of the CPSsimulations approximated the amounts of rainfall well for clusters1 (C1, July 20) and 2 (C2, May 15) at the Ganghwa and Taean gridpoints, even though the overall precipitation band over the targetdomain was well replicated, with a certain degree of success. Inturn, the model underestimated the wet deposition amounts forboth SO4

2� and NO3� at the Ganghwa and Taean grid points, as

indicated in Table 3. Likewise, the overestimation of NO3� by all of

the CPSs at Gosan, and by the AK scheme at Taean, occurred for thesame reason, the simulation of greater amounts of rainfall in clus-ters 1 (C1) and 2 (C2). This was because the accuracy of theprecipitation simulation varied from case to case, with the positionof the maximum precipitation over the Korean Peninsula beingbiased by more than 100 km between the four CPS schemes (Leeand Park, 2002). Previous investigations pointed out that, of thefour CPS schemes, the AK scheme produces the best accuracy forthe location of the maximum amount of rainfall when usinga coarse grid resolution (i.e., 30-km horizontal resolution).

Fig. 7 shows the domain averaged wet deposition, as well as itsvariation, from each of the four CPS simulations for SO4

2� and NO3�

over South Korea. The estimated wet depositions over the targetdomainwere found to be 184e197 kton for SO4

2� and 277e337 ktonfor NO3

�. These annual total wet depositions estimated using thecluster analysis techniquewere higher than the values found by thecontinuous year-long simulations by CADM during the LTP project,which were 130 kton year�1 for SO4

2� and 270 kton year�1 for NO3�

(NIER, 2005, 2009), with no significant differences. The variabilitiesin the wet depositions of SO4

2� and NO3� had significantly small

standard deviations of 9.1% (w17 kton) for SO42� and 8.8%

(w32 kton) for NO3�, with much smaller values of 4.2% for SO4

2� and

) NO3�, and (c) precipitation at 3 monitoring sites: Ganghwa, Taean and Gosan.

Cluster 3 (4/29) Annual total

Ganghwa Taean Gosan Ganghwa Taean Gosan

<0.01 0.90 0.03 17.64 19.19 10.370.15 0.32 0.07 2.36 11.09 3.800.13 0.31 0.24 1.47 3.44 4.300.13 0.32 0.16 2.37 3.53 4.650.14 0.24 0.19 1.53 2.69 4.44

<0.01 0.56 0.23 10.63 13.06 5.050.21 0.43 0.14 3.14 15.36 8.630.19 0.40 0.35 2.10 4.37 7.240.18 0.54 0.29 3.21 5.69 10.390.19 0.32 0.29 2.11 3.55 9.39

<0.1 55.0 1.0 698.6 1071.2 655.43.6 13.1 18.0 74.1 160.0 1838.89.4 16.2 38.3 104.7 178.0 2694.93.6 14.4 40.1 41.6 158.8 4183.28.6 17.7 35.2 95.2 194.4 2528.5

0

2

4

6

8

10

12

)ah/gK(

noitisopeDte

W

Annual Estimate of Wet Deposition

AK

AK

BM

BM

GR

GR

KF2

KF2

STD = 9.1%

STD = 8.8%

SO4-2 NO3-

Fig. 7. The domain averaged amounts of wet depositions from the four simulations(AK, BM, GR, and KF2) over the small domain (Fig. 1).

H.-R. Kim et al. / Atmospheric Environment 45 (2011) 727e735734

4.4% for NO3� over the entire domain. In comparison with the

variation in precipitation (a standard deviation of 15.2% arisingfrom the CPS options), the variability in the average wet depositionwas much lower. This suggests that the sensitivities and uncer-tainties between the convective parameterization schemes inestimating the long-term wet depositions of both SO4

2� and NO3�

were significantly small. Thus, these variations of w10% werenegligible, despite the relatively larger variability in the rainfallamounts between the different CPS options. This result was alsoseen in other cases with relatively heavy rainfall bands passing overthe Korean Peninsula, including three cases in this study. In thesecases, the average rainfall intensity during precipitation over theKorean Peninsulawas relatively stronger, but the sulfate and nitrateconcentrations in air were relatively weak compared to the strongemission source areas in central China. In this situation, the initialtime-variation of the wet deposition amounts followed that of theprecipitation, while the later time-variation of the wet depositionstended not to follow that of the precipitation because of thereduced concentrations in air. This process resulted in a smallervariability of the wet deposition compared to that of the precipi-tation among the CPS schemes.

4. Summary and conclusion

MM5/CMAQ modeling was carried out to evaluate the vari-abilities and uncertainties of convective parameterization schemesin their estimations of annual precipitation and the wet depositionsof SO4

2� and NO3�. Four convective parameterization schemes were

employed: the AntheseKuo (AK), BettseMiller (BM), Grell (GR),and KaineFritsch 2 (KF2) schemes, with a cluster technique alsoused to statistically classify the precipitation patterns for the esti-mations of the total average annual wet depositions.

As a tentative approach to the use of a cluster analysis techniquefor long-termestimations, a three-step cluster analysis techniquewasapplied to the annual estimates of the precipitation and wet deposi-tions of SO4

2� and NO3�. Out of the total number of days with precip-

itation inSouthKoreaduring2002, three typical typesof precipitationdays were identified by employing the cluster analysis technique,with the annual total precipitation and wet depositions thenestimated using these three representative episodic cluster dates.

The results showed that all of the CPS simulations wellapproximated the pattern of the observed annual precipitationband located from southern China to the north of Japan throughSouth Korea, although the precipitation over central and northern

China was underestimated, while that over southern China, southof the Yellow Sea, the southern tip of the Korean Peninsula, andsouthern Japan was overestimated. The amounts of rainfall foundby the different CPS simulations over South Korea showed somedegree of variability, with a standard deviation of 15.2%, which wasrelatively large compared with the 4.2% found for the entiredomain. This was due to meteorological characteristics such as thestrong baroclinic instability and a positional error of 100e200 km.

However, the overall total annual wet depositions of SO42� and

NO3� over the target domain simulated with each of the four CPSs

showed similar horizontal precipitation patterns, with standarddeviations of 9.1 and 8.8% for SO4

2� and NO3�, respectively, which

were significantly small. Thus, these variations of w10% betweenthe different CPS options for estimating the long-term deposition ofacidic air pollutants were not significant but negligible. Based onthe analyses of several heavy precipitation cases over the KoreanPeninsula, the relatively strong rainfall intensity during precipita-tion and relatively weak SO4

2� and NO3� concentrations in air over

the Korean Peninsula result in small wet deposition variabilities,despite the relatively large variability in the precipitation amountsbetween the CPS schemes.

This study showed that, primarily, precipitation cases withrelatively heavy rainfall intensities prevailed in the estimations ofthe precipitation and its involvement in the wet depositionprocesses over the region such as Korean Peninsula. This meansthat over a large source region or in relatively weak rainfallintensity precipitation cases, cumulus parameterization schemeoptions have the possibility to intimately affect the wet depositionresults. Therefore, the effect of convective parameterizationrequires judicious consideration when choosing a cumulusparameterization scheme before confidence in a CPS can be claimedwhen simulating long-term wet deposition over other regions.

This study mainly pertained to the identification of the uncer-tainties arising from different CPS options and their impacts onwetdeposition over the Northeast Asia. With the goal of identifyingtheir impacts on the calculation of sourceereceptor relationships,further sensitivity testing of the CPS schemes will be performed toobtain a more precise and robust understanding of the long-rangetransport processes over northeast Asia.

Acknowledgements

This study has been supported by the Korea MeteorologicalAdministration Research and Development program under theGrant CATER 2009-3212. We would like to thank three anonymousreviewers for their helpful comments and suggestions that greatlyimproved our manuscript.

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