Modelling subgrid scale dry deposition velocity of O3 over the Swedish west coast with MM5-PX model

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doi:10.1016/j.at

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Atmospheric Environment 40 (2006) 415–429

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Modelling subgrid scale dry deposition velocity of O3 over theSwedish west coast with MM5-PX model

J.-F. Miaoa,�, D. Chena,b, K. Wyserc

aDepartment of Earth Sciences, Goteborg University, PO Box 460, 405 30 Goteborg, SwedenbLaboratory for Climate Studies/National Climate Center, China Meteorological Administration, 100081 Beijing, PR China

cRossby Centre, Swedish Meteorological and Hydrological Institute (SMHI), 601 76 Norrkoping, Sweden

Received 10 May 2005; received in revised form 18 August 2005; accepted 6 September 2005

Abstract

A mesoscale meteorological model (MM5) coupled with an advanced land surface model (PX LSM) is used in

this study to model high-resolution (2 km) dry deposition velocity of ozone over the Swedish west coast, together

with a newly revised dry deposition parameterization for air-quality models with emphasis on non-stomatal resistance.

The important air-surface exchange processes for air quality (surface fluxes of heat, moisture and momentum)

are also simulated by this model. The modelled subgrid scale variability of the dry deposition velocity and its

dependence on land use, terrain height and synoptic conditions are investigated. It is found that a systematic difference in

the deposition velocity modelled by different resolutions exists, and the difference varies diurnally and daily. The subgrid

scale variation is significant, which has a clear impact on the area-averaged deposition velocity. The deposition velocity

depends strongly on land use and weather conditions, but not on topography for the area studied. Meteorological

conditions at subgrid scales play an important role in determining the deposition velocity. It is also concluded that the dry

deposition velocity simulated in this study is reasonable, and that a 6-km resolution would be practically good enough to

resolve the inhomogeneity of the surface properties for dry deposition studies in this area. The variation range of dry

deposition velocity over different land use categories and the corresponding resistances are outlined. Moreover, the

difference in the estimated dry deposition velocitiy between the methods using fractional land use and using dominant land

use is compared.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Mesoscale meteorological model; Land surface model; Dry deposition; Non-stomatal resistance; Air-surface exchange

1. Introduction

Dry deposition is the process by which atmo-spheric trace chemicals are transferred by airmotions to the surface of the Earth, and is a

e front matter r 2005 Elsevier Ltd. All rights reserved

mosenv.2005.09.057

ing author. Fax: +4631 773 1986.

ess: [email protected] (J.-F. Miao).

primary mechanism to cleanse the atmosphere and deliver chemical doses to the surface (Wesely,1989). Dry deposition flux of a trace gas isusually expressed as the product of concentrationof the trace gas at a reference height and thedeposition velocity. This velocity is oftenparameterized as a function of aerodynamicresistance, quasi-laminar sublayer resistance and

.

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ARTICLE IN PRESSJ.-F. Miao et al. / Atmospheric Environment 40 (2006) 415–429416

canopy (surface) resistance (e.g. Wesely, 1989). Theparameterization has been widely used in regionalscale air-quality modelling (e.g. Pleim and Xiu,1995), as well as global atmospheric chemistry andclimate interaction studies (e.g. Ganzeveld andLelieveld, 1995).

Mesoscale meteorological models are often usedas drivers for dry deposition modelling. Drydeposition modelling for gaseous species (e.g.ozone) to a large extent depends on land surfaceprocess parameterization in mesoscale models(Pleim et al., 2001). This is because accurate drydeposition modelling requires a meteorologicalmodel to provide reliable information on globalradiation, surface air temperature, humidity, wind,soil moisture, friction velocity and atmosphericstability (Xiu and Pleim, 2001). The interactionwith vegetation is described with a canopy resis-tance that can be separated into stomatal and non-stomatal portions. The separation leads to morerealistic diurnal variation of dry deposition (Zhanget al., 2003). In previous studies, non-stomatalresistance is typically prescribed according to seasonand land use (e.g. Wesely, 1989), thereby ignoringmany processes that can affect the depositionpathway (Zhang et al., 2003).

In atmospheric grid models, model output vari-ables represent Reynold’s average mean statevariables within a grid square horizontally, whichcovers tens of square kilometers to thousands ofsquare kilometers. Within such a grid square, thesurface characteristics such as soil moisture, vegeta-tion, and topography are assumed to be uniform. Inreality, however, surface characteristics are rarelyhomogeneous. Thus, a problem arises when thesubgrid scale effect becomes significant for anapplication (e.g. Chock et al., 2002). Furthermore,air pollution models are often constrained by theirspatial resolution, which limits the understanding offine scale variability of processes such as drydeposition.

Subgrid scale spatial variations over land surfacehave significant effects on dry deposition velocities.To model the dry deposition velocities over hetero-geneous terrain correctly in a numerical model, Maand Daggupaty (2000) presented an averaging

method to determine an effective dry depositionvelocity. Zhang and Brook (2001) introduced asubgrid velocity scale to account for the generationof turbulent fluxes by subgrid motions, and thenestimate subgrid variations of wind speed andfriction velocity in order to obtain a more realistic

deposition velocity over the grid box. Alternatively,one may study the effect of grid resolution byconducting model runs with different grid size in themodelling system, and then determine the differencein model results (Chock et al., 2002). This approachcannot only be used to estimate the mean depositionover a grid where subgrid variation is taken intoaccount, but also be useful in studying the subgridvariability. In the past, this approach is to someextent limited by model parameterizations, avail-ability of land use data at higher resolutions, as wellas the computer speeds. However, with the devel-opment of atmospheric models (e.g. convectiveparameterization, PBL and land surface processparameterizations, and nesting scheme) and theadvances in computer hardware and parallel com-puting, it has become feasible to run high-resolutionatmospheric models with complex physics processesfor air-quality studies nowadays. This approach isused in this study to examine the impacts of thesubgrid scale effect on the dry deposition velocity ofozone.

To our knowledge, so far there are few studies onthe subgrid scale effects on dry deposition velocitythat: (1) consider both the land use variations andtheir induced variations of meteorological condi-tions at subgrid scales, (2) use a high-resolutionmesoscale model coupled to an advanced land usemodel, which deals with important air-surfaceexchange processes for air quality (surface fluxesof heat, moisture and momentum), (3) incorporate adry deposition parameterization including varyingnon-stomatal resistance, and (4) conduct manycontinuous days and nights for distinguishingdiurnal cycles of dry deposition velocities and theirbehaviors under different synoptic situations. Theselimitations motivate this study. Therefore, theobjectives of this study are: (1) to estimate fine-scale dry deposition velocity of ozone (O3) byimplementing the state-of-the-art mesoscale meteor-ological model (MM5) coupled with an advancedland surface model (PX LSM) (Xiu and Pleim,2001) and a newly revised parameterization for drydeposition parameterization for air-quality modelswith emphasis on non-stomatal resistance (Zhang etal., 2003), (2) to evaluate the model performance atdifferent horizontal resolutions by comparingthe modelled meteorological variables with theobserved ones, (3) to examine the subgrid scalevariability of the dry deposition velocity, and (4) torelate this variability to land use and synopticconditions.

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(a)

(b)

(c)

(d)

Fig. 1. Lambert projection map showing the nested grid

configuration and the model terrain (m) prescribed in the

domains. Domains 1, 2, 3, and 4 (denoted by D1, D2, D3 and

D4) have a horizontal grid resolution of 54, 18, 6 and 2 km,

respectively. Locations of two observational sites: Jarnbrott (J)

for near surface temperature and wind, and Lejonet (L) for global

radiation, are indicated in domain 4 for model evaluation.

J.-F. Miao et al. / Atmospheric Environment 40 (2006) 415–429 417

2. Model description and set up

2.1. Meteorological and land surface models

The present study uses version 3.6 of the fifth-generation PSU/NCAR mesoscale model (MM5)(Grell et al., 1995) with the Pleim-Xiu (PX) landsurface model (LSM) (Xiu and Pleim, 2001) option,hereafter denoted as MM5-PX model.

MM5 is a limited-area, non-hydrostatic, terrain-following s-coordinate primitive equation modeldesigned to simulate or predict mesoscale atmo-spheric circulations. It has been widely used in air-quality modelling (e.g. Hogrefe et al., 2001).

The Pleim-Xiu LSM (PX LSM) is an optionimplemented in the MM5. Its key elements include asurface model dealing with soil moisture andevaporation, and a non-local closure PBL model(Xiu and Pleim, 2001; Pleim et al., 2001; Pleim andXiu, 2003). It represents soil moisture and tempera-ture in two layers (a 1-cm surface layer and a 1-mroot zone layer), as well as canopy moisture, andhandles soil surface, canopy and evapotranspirationmoisture fluxes. It uses the percentage land use andsoil data to aggregate soil and vegetation properties,rather than using a single dominant type.

Stomatal conductance, a key parameter inestimating the dry deposition velocity in thisstudy, is parameterized depending on root-zonesoil moisture, air temperature, humidity, photo-synthetically active radiation (PAR), and severalvegetation parameters such as leaf area index (LAI)and minimum stomatal resistance in MM5-PXmodel.

2.2. Model setup and initialization

The domain of the MM5-PX simulation is shownin Fig. 1. The model is set up with four nesteddomains (D1, D2, D3, and D4) with horizontal gridresolution of 54, 18, 6, 2-km, respectively. Thesefour domains consist of 50� 50, 64� 55, 62� 52,and 40� 46 horizontal grids (N–S direction�E–Wdirection), respectively. D1 has the size of2700 km� 2700 km covering Scandinavia, and isused to simulate the large scale meteorologicalconditions. The inner three domains with fineresolution are used to model mesoscale and local-scale variability, which allows the subgrid effect bestudied. Interaction is only permitted from outer toinner domains (one-way nesting). All domains have34 vertical levels and the model top at 100 hPa.

About 18 levels are within the lowest 2 km andthe lowest model level (half-s level) is at approxi-mately 19m.

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Fig. 2. Dominant land use category used in the innermost model

domain (D4) with 2 km� 2 km resolution. Red color denotes

urban or built-up land (called ‘urban’ for short), yellow dryland

cropland and pasture (‘crop’), green evergreen needleleaf forest

(‘forest’), and dark blue water bodies (‘water’, including sea and

lake). The subdomain below the dot dash black line is referred to

as an Overlapped Area of 54 km� 54 km over which the grids of

domains 1–4 can cover simultaneously.

J.-F. Miao et al. / Atmospheric Environment 40 (2006) 415–429418

The following physical option is used in D1–D4:Kain–Fritsch convection scheme including shallowconvection (KF2, Kain, 2004), Dudhia simple icemicrophysis scheme, Rapid Radiative TransferModel (RRTM) longwave scheme (Mlawer et al.,1997), and Dudhia cloud-radiation shortwavescheme for all domains. No data assimilation isused in the model to keep the modeled variablesindependent of the observed variables that are usedfor comparison. The same physical option is usedfor different domains to enable the modelledmeteorological variables to be consistent betweenthe different resolutions.

The model is driven by the ECMWF operationalanalysis with spatial resolution of 0.51� 0.51 andtemporal resolution of 6 h. The USGS 25-categoryland use/land cover (vegetation type) data andterrain data with high resolution are used, which arederived from 30-s grid resolution (approximately1 km� 1 km) data, respectively. For land use,percentage data for the 25 categories at each gridpoint, which is based on 30-s land use categorydata, is used to aggregate vegetation properties,rather than using a single dominant type. Usingthe percentage land use data enables the fractionof each grid composed of each land use typeto be the same for the various resolutions. Onlyin this way can the subgrid effect be studiedreasonably.

The topographical features for the modellingdomains with different resolutions are shown inFig. 1. As an example of vegetation information,Fig. 2 shows the dominant land use distribution forD4 at 2-km grid resolution, and there are fourdominant land use categories within this domain:Urban, Crop, Forest, and Water. Also, soilmoisture is initialized using the ECMWF data.

Simulations are conducted from 6 May 2001(UTC 00:00) to 20 May 2001 (UTC 24:00) withmodel output saved at hourly intervals. The firstday is discarded as spin-up and the remaining 14days are used for the analyses. The latter periodspans the period of GOTE2001 observation(Borne et al., 2005), during which the hourlyobserved temperature and wind at Jarnbrott, andglobal radiation at Lejonet (locations are shown inFig. 1) are available for evaluating the modelperformance. The period from 7 to 12 May 2001was dominated by a high-pressure system oversouthwestern Sweden, while the period from 13 to20 May 2001 was dominated by a low-pressuresystem.

3. Dry deposition parameterization

According to Wesely (1989), the flux of a tracegas, F c is estimated as:

F c ¼ �CðzÞVdðzÞ, (1)

where C(z) is the concentration at the height z, andVdðzÞ the dry deposition velocity at the height z. Forgaseous species, the Vd is defined as

VdðzÞ ¼1

Ra þ Rb þ Rc, (2)

in which Ra is the aerodynamical resistance betweena specified height (z) and the surface, Rb quasi-laminar boundary layer resistance, and Rc is thebulk surface (canopy) resistance. In this study, the z

is the height of the lowest model level (ca. 19mAGL).

In the MM5-PX model, the aerodynamic resis-tance (Ra), which is a model output variable, iscomputed as below:

Ra ¼1

ku�ln

z

z0� ch

� �, (3)

where z is the height of the lowest model level, z0 theroughness length, u* the surface friction velocity, k

ARTICLE IN PRESSJ.-F. Miao et al. / Atmospheric Environment 40 (2006) 415–429 419

the Von Karman’s constant (0.4), and ch thestability function (Walcek et al., 1986; Xiu andPleim, 2001). Here, z0 is an aggregated parameter byusing percentage land use data and logarithmicaverage within each gridcell.

The quasi-laminar boundary layer resistance(Rb), which is not a model output variable, can becomputed from the related model output. In thisstudy, over the vegetated surfaces, Rb is parameter-ized as

Rb ¼2

ku�Sc

Pr

� �2=3

(4)

(e.g. Erisman et al., 1994), while over the waterbodies, it is parameterized as

Rb ¼1

ku�Sc

Pr

� �2=3

(5)

(e.g. Ganzeveld and Lelieveld, 1995), where Sc is theSchmidt number, defined as the ratio of thekinematic viscosity of air (0.147 cm2 s�1) to themolecular diffusivity of the trace gas in question (i.e.ozone) (DO3

), Pr the Prandtl number (0.72). DO3is

scaled according to the ratio of DH2O=DO3referred

by Erisman et al. (1994).The bulk canopy resistance (Rc) used in this study

is parameterized according to Zhang et al. (2003):

1

Rc¼

1�W st

Rst þ Rmþ

1

Rns, (6)

1

Rns¼

1

Rac þ Rgþ

1

Rcut, (7)

where Rst is the canopy stomatal resistance, Rns thenon-stomatal resistance. Rm is the mesophyllresistance, only dependent on gaseous species(Zhang et al., 2002), given a value of 0 for ozone,Wst the fraction of stomatal blocking under wetconditions, related to the solar radiation, Rac the in-canopy aerodynamic resistance, independent of thegaseous species, Rg the ground resistance, depend-ing on the vegetated surfaces and the gaseousspecies, and Rcut the canopy cuticle resistance fordry and wet conditions. The readers are directed tothe paper of Zhang et al. (2003) for detaileddescription of all these parameters and theirparameterization. In this study, Rst derives fromthe canopy resistance (expressed in Rst/LAI) speci-fied and computed in the MM5-PX model, and Rg isprescribed a value of 2000 sm�1 for O3 over thewater bodies (Zhang et al., 2003).

The stomatal resistance Rst computed in theMM5-PX model for water vapor (H2O) is para-meterized as below:

Rst ¼Rstmin

F1ðPARÞF 2ðw2ÞF 3ðRHsÞF 4ðTaÞ, (8)

where F1, F2, F 3, and F4 are the empiricalstress functions of PAR, root-zone soil moisture(w2), relative humidity at the leaf surface (RHs), andair temperature at the lowest model layer ðTaÞ,respectively, and Rstmin is the minimum stomatalresistance specified according to vegetation species(Xiu and Pleim, 2001). As suggested by Pleim et al.(2001) and Zhang et al. (2003), the Rst for watervapor is then scaled by the molecular diffusivity ofO3 relative to water vapor diffusivity (DH2O=DO3

) tothe stomatal resistance for O3 in order to estimatethe dry deposition velocity of O3.

4. Results and discussion

4.1. Model evaluation

As an example, the 14-day hourly observations oftemperature and wind at one selected site(Jarnbrott) and global radiation at anotherselected site (Lejonet) are used to examine themodel performance at 54, 18, 6 and 2 km resolu-tions. Figs. 3 and 4 show the comparison betweenthe observations and the simulations at the finestresolution. The commonly used statistical measuresare given: (1) standard deviation of the modelled(smod) and observed variables (sobs), (2) mean biaserror (MBE), (3) root-mean-square-error (RMSE),(4) standard deviation of the difference (sdiff), and(5) correlation coefficient (R). For wind direction,mean absolute error (MAE) is also given. Inaddition, when computing MAE and RMSE ofwind direction, wind direction differences areconstrained to be an absolute value between 01and 1801 due to the problems associated withoverlap from 01 and 3601 (Buckley et al., 2004).

The modelled near-surface temperature has anegative bias smaller than 0.5 1C (Fig. 3a), while themodelled near-surface wind speed has a positivebias of 2.0m s�1 (Fig. 3b). All other statisticalmeasures also show a reasonable agreement be-tween the modelled and observed data. Examina-tions on wind direction and global radiation (Fig. 4)show that the difference in modelled wind directionand global radiation between different resolutions isnot significant. In summary, the fair agreement

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0 5 10 15 20 250

5

10

15

20

25

MBE = 0.72RMSE = 2.93

R = 0.70

54 km

0 5 10 15 20 250

5

10

15

20

2518 km

0 5 10 15 20 250

5

10

15

20

256 km

0 5 10 15 20 250

5

10

15

20

252 km

0 5 10 150

5

10

1554 km

0 5 10 150

5

10

1518 km

0 5 10 150

5

10

156 km

0 5 10 150

5

10

152 km

Mod

elle

d T

(°C

)M

odel

led

T (

°C)

Mod

elle

d T

(°C

)M

odel

led

T (

°C)

σmod

= 3.33σ

obs = 3.86

σmod = 3.75

σobs

= 3.86

σmod = 3.31

σobs

= 3.86

σmod

= 2.59σ

obs = 1.58σ

mod = 2.66σ

obs = 1.58

σmod

= 2.65σ

obs = 1.58

σmod

= 3.03σ

obs = 1.58σ

mod = 3.23

σobs = 3.86

σdiff

= 2.84

MBE = -0.29RMSE = 2.6

R = 0.77σ

diff = 2.59

MBE = 0.04RMSE = 2.48

R = 0.77σ

diff = 2.48

Observed T (°C)

Observed T (°C) Observed T (°C)

Observed T (°C)

MBE = -0.04RMSE = 2.48

R = 0.77σ

diff = 2.48

MBE = 2.16RMSE = 2.98

R = 0.64σ

diff = 2.06

MBE = 2.27RMSE = 3.01

R = 0.65σ

diff = 1.97

MBE = 2.51RMSE = 3.45

R = 0.63σ

diff = 2.36

MBE = 2.12RMSE = 2.91

R = 0.67σ

diff = 1.98

Mod

elle

d W

S (

m s

-1)

Mod

elle

d W

S (

m s

-1)

Mod

elle

d W

S (

m s

-1)

Mod

elle

d W

S (

m s

-1)

Observed WS (m s-1)

Observed WS (m s-1) Observed WS (m s-1)

Observed WS (m s-1)

(a) (b)

Fig. 3. Observed (solid line) and modelled (dotted line) hourly near-surface temperature (a) and wind speed (b) at Jarnbrott (mast site)

from 0000 UTC 7 May to 2200 UTC 20 May 2001 (sample number N ¼ 335). Observed near-surface temperature is measured at 3m

AGL, while wind speed measured at 16m AGL; Modelled near-surface temperature (1.5m AGL) and near-surface wind speed (ca. 19m

AGL) are from the closest grid pint to observational site. ‘54 km’, ‘18 km’, ‘6 km’, and ‘2 km’ identify the horizontal grid resolution of the

domains.


between the modeled and observed meteorologicalvariables that are crucial to the calculation of drydeposition, gives us confidence in the model resultsin this study.

4.2. Dry deposition velocity at subgrid scales

A subgrid scale is a concept relative to resolved

grid scale. The variable that can be resolved undercertain grid resolution is called resolved grid scale

variable, while that not resolved under the resolu-tion is called subgrid scale variable (e.g. Zhang andBrook, 2001).

To examine subgrid effects more reasonably, drydeposition velocity referred in this section isestimated as a weighted average of the depositionvelocity over all land use categories within theindividual grid cells in the modelling domains (e.g.Walcek et al., 1986; Ma and Daggupaty, 2000;Zhang and Brook, 2001), and the weighting is basedon fractional land use categories within individualgrid cells (i.e. percentage for 25-category vegetationtype at each grid point), namely,

Vd ¼X

i

f ivdi, (9)

where f i is the fraction of the averaging area(percent coverage) covered by each land usecategory, i, within the gridcell of interest, and vdi

is the deposition velocity over each land usecategory contained in the averaging area. The vdi

is computed as below:

vdi ¼1

Rai þ Rbi þ Rci

(10)

for each land use type within the gridcell, separately.In this study, u� is assumed to be a constant over allareas within each grid, and Rai is further assumed tobe constant for each land use type within thegridcell. Rbi varies only for vegetated surfaces andwater surface within the gridcell, using Eqs. (4) and(5), and following the assumption that u� isconstant. Rci, however, depends on the modeloutput meteorological variables over each grid andspecific surface parameters (i.e. land use typeproperties) within the grid cell.

In this study, subgrid effect is examined bylooking at the results from domains with higherresolution nested within a coarse domain. Theregion where coarse and fine domains overlap canthen be used to study the subgrid scale effects forthe coarse domain. The grids over the overlapped

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8 10 12 14 16 18 200

90

180

270

360

Win

d di

rect

ion

(deg

)

54 km

MBE = -11MAE = 50RMSE = 65

8 10 12 14 16 18 200

90

180

270

360

Win

d di

rect

ion

(deg

)

18 km

MBE = -2MAE = 45RMSE = 60

8 10 12 14 16 18 200

90

180

270

360

Win

d di

rect

ion

(deg

)

6 km


8 10 12 14 16 18 200

90

180

270

360

Day of May 2001

Win

d di

rect

ion

(deg

)

2 km


(a)

8 10 12 14 16 18 200

200

400

600

800

100054 km

MBE = 53RMSE = 178R = 0.79

8 10 12 14 16 18 200

200

400

600

800

100018 km

MBE = 39RMSE = 169R = 0.81

8 10 12 14 16 18 200

200

400

600

800

10006 km

MBE = 39RMSE = 163R = 0.82

8 10 12 14 16 18 200

200

400

600

800

1000

Day of May 2001

2 km

MBE = 36RMSE = 170R = 0.81

Glo

bal r

adia

tion

(

W m

-2)

Glo

bal r

adia

tion

(W m

-2)

Glo

bal r

adia

tion

(

W m

-2)

Glo

bal r

adia

tion

(

W m

-2)

(b)

Fig. 4. (a) Hourly variation of observed (solid line) and modelled (dotted line) near surface wind direction at Jarnbrott, and

(b) hourly variation of observed (solid line) and modelled (dotted line) global radiation at Lejonet for the period between 0000 UTC 7May

2001 and 2200 UTC 20 May 2001 (sample number N ¼ 335). Observed wind direction is measured at 16m AGL, while modelled near

surface wind direction is for the lowest model level (ca. 19m AGL) at the closest grid point to observational site. Observed global radiation

is measured near the surface, while modelled global radiation is surface downward shortwave radiation at the closest grid point to

observational site. Top: 54-km grid resolution; Middle top: 18-km grid resolution; Middle bottom: 6-km grid resolution; Bottom: 2-km

grid resolution. Note that: statistical parameters (MBE, RMSE and R) of global radiation are based on daytime period, not on the entire

time period.


area in this study are shown in Table 1 with the gridpoints in (Y, X) within respective domains.

To demonstrate the effect of subgrid scales on drydeposition velocity of ozone, the averaged Vd overthe overlapped area for different grid resolutions atselected sites is presented in the following. Thesepoints are selected considering availability of gridsfor respective overlapped area and land use com-plexity (combination).

4.2.1. 54-km grid resolution Vd versus subgrid Vd

One grid point in D1 represents a 54 km� 54 kmgridcell, corresponding with 3� 3 grid points in D2,9� 9 grid points in D3, and 27� 27 grid points inD4. The Overlapped Area (Fig. 2) is an area of54 km� 54 km, denoted by S11 (Table 1), havingdifferent percentage of urban, crop, forest and waterwithin it.

The daily variation and the average diurnal cycleof area-averaged (54 km� 54 km) Vd at S11 site for54-, 18-, 6-, and 2-km grid resolutions is shown inFig. 5. A systematic difference between the differentresolutions exists, and the difference varies diurnallyand daily. The mean Vd at 54-km resolution issignificantly different from than those from otherthree resolutions (Fig. 5a), especially during day-time (Fig. 5b), indicating the importance ofinhomogeneity of the surface properties and solarradiation. In contrast, the difference between 6 and2-km resolutions is small.


In order to further examine the effect of subgridscales on dry deposition velocity, four representa-tive sites in D2 are selected: S21, S22, S23, and S24(Table 1). They are denoted by the grids (23, 28),

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Table 1

Percentage of land use category (LUC) at selected sites (‘area’) and the corresponding grids to different domains

Site % of LUC D1 (54 km) D2 (18 km) D3 (6 km) D4 (2 km)

S11a Urban: 3.8% (16, 22) (22:24, 28:30) (22:30, 28:36) (1:27, 19:45)

Crop: 6.8%

Forest: 70.0%

Water: 19.3%

S21b Urban: 20.5% — (23, 28) (25:27, 28:30) (10:18, 19:27)

Crop: 10.5%

Forest: 27.7%

Water: 40.3%

S22b Urban: 28.9% — (24, 28) (28:30, 28:30) (19:27, 19:27)

Crop: 19.0%

Forest: 40.2%

Water: 10.9%

S23b Urban: 0.8% — (23, 30) (25:27, 34:36) (10:18, 37:45)

Crop: 3.9%

Forest: 89.1%

Water: 6.2%

S24b Urban: 9.1% — (24, 29) (28:30, 31:33) (19:27, 28:36)

Crop: 5.6%

Forest: 78.0%

Water: 7.3%

S31c Urban: 99.9% — — (27, 29) (16:18, 22:24)

Crop: 0.1%

S32c Urban: 20.1% — — (26, 29) (13:15, 22:24)

Crop: 49.1%

Forest: 22.3%

Water: 8.5%

S33c Forest: 100% — — (27, 33) (16:18, 34:36)

S34c Urban: 37.9% — — (27, 30) (16:18, 25:27)

Crop: 32.9%

Forest: 27.2%

Water: 1.9%

aAn overlapped area of 54 km� 54 km for D1–D4, consisting of 1� 1 grid cell in D1, 3� 3 grid cells in D2, 9� 9 grid cells in D3, and

27� 27 grid cells in D4.bAn overlapped area of 18 km� 18 km for D2–D4, consisting of 1� 1 grid cell in D2, 3� 3 grid cells in D3, 9� 9 grid cells in D4.cAn overlapped area of 6 km� 6 km for D3–D4, consisting of 1� 1 grid cell in D3, 3� 3 grid cells in D4.


(24, 28), (23, 30), and (24, 29) in D2, respectively,representing one 18 km� 18 km size of gridcell inD2. The area covered by one grid point in D2 iscovered by 9 and 81 gridpoints in D3 and D4,respectively. Regarding land use category, thesefour sites have different combinations of urban,crop, forest and water within an area of18 km� 18 km.

Fig. 6 shows the daily variation of area-averaged(18 km� 18 km) Vd resolved by 18-, 6- and 2-kmgrid resolutions at the four sites. Once againsignificant differences between different grid resolu-

tion, especially those between 18- and 6-km gridresolutions, exist depending mainly on the land usecombinations. For instance, the difference betweenthe 18- and 6-km grid resolutions, can be as high as5–32% at S23 site, depending on weather condi-tions. For Vd from 6- and 2-km grid resolutions, thedifference can reach by 14% at S21 and S22 sites.


To further distinguish 2-km gird resolution Vd

from 6-km gird resolution Vd, four sites in D3having different land use combinations are selected:

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7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

0.2

0.3

0.4

0.5

0.6

Day of May 2001

54 km18 km 6 km 2 km

0 3 6 9 12 15 18 21 240.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time (UTC)


Vd (c

m s

-1)

Vd (c

m s

-1)

(a)

(b)

Fig. 5. (a) Daily variation of 24-h mean dry deposition velocity (Vd) averaged over the ‘Overlapped Area’ (S11) referred to in Fig. 2 for

54-, 18-, 6-, and 2-km horizontal grid resolutions; (b) Diurnal cycle of area-averaged dry deposition velocity (Vd) for 54-, 18-, 6-, and 2-km

horizontal grid resolutions over S11. The diurnal cycle is based on 14-day hourly modelled data (7–20 May 2001). In the legend, ‘54 km’

refers to ‘1� 1 at 54 km’, ‘18 km’ refers to ‘3� 3 at 18 km’, ‘6 km’ to ‘9� 9 at 6 km’, and ‘2 km’ to ‘27� 27 at 2 km’.


S31, S32, S33, and S34 (Table 1). They represent anarea of 6 km� 6 km. The area covered by one gridpoint in D3 is covered by 9 gridpoints in D4.

As shown in Fig. 7, the Vd difference between6- and 2-km grid resolutions is still evident. Forexample, it can reach by 28% at S32 site. Also, theextent of difference varies with the degree ofcomplexity of land use combination within indivi-dual gridcell. In general, this difference is smallerthan that between 54- and 18-km grid resolutions.

4.2.4. Meteorological conditions and aerodynamical

resistance at subgrid scales

To investigate the reasons for the difference of Vd

resolved by various resolutions presented, thedifference in the average diurnal cycle of area-

averaged near-surface temperature and wind, andsurface heat fluxes over the overlapped area (S11)between different grid resolutions is shown in Fig. 8,whereas that in the friction velocity is shown inFig. 9a. The results indicate that the meteorologicalvariables for subgrid areas differ from grid averagevalues. Compared to relatively small differencebetween 6- and 2-km modelled results, the differ-ence between 54-, 18-, and 6-km modelled results intemperature, wind, sensible heat flux and frictionvelocity is evident. For latent heat flux, thedifference among all resolutions is significant,especially during daytime hours. This suggests thatthe subscale heterogeneity plays an important rolein determining the modelled meteorological vari-ables, on which the Vd depends.

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7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

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0.3

0.4

0.5

0.6

0.7

0.818 km 6 km 2 km

7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

0.2

0.3

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0.5

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0.7

0.8

7 8 9 10 11 12 13 1415 16 17 18 19 200

0.1

0.2

0.3

0.4

0.5

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0.7

0.8

Day of May 2001

7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Day of May 2001

(a) (b)

(c) (d)

Vd (

cm s

-1)

Vd (

cm s

-1)

Fig. 6. Daily variation of 24-h mean dry deposition velocity (Vd) averaged over an area of 18 km� 18 km for 18-, 6-, and 2-km horizontal

grid resolutions (1 point in D2, while 9 points in D3, 81 points in D4) at four selected sites: (a) S21, (b) S22, (c) S23, and (d) S24 (cf. Table

1). In the legend, ‘18 km’ refers to ‘1� 1 at 18 km’, ‘6 km’ to ‘3� 3 at 6 km’, and ‘2 km’ to ‘9� 9 at 2 km’.

7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9 6 km 2 km

7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

7 8 9 10 11 12 13 14 15 16 17 18 19 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Day of May 20017 8 9 10 11 12 13 14 15 16 17 18 19 20

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Day of May 2001

Vd (

cm s

-1)

Vd (

cm s

-1)

(a) (b)

(c) (d)

Fig. 7. Daily variation of 24-h mean dry deposition velocity (Vd) averaged over an area of 6 km� 6 km for 6- and 2-km horizontal grid

resolutions (1 point in D3, while 9 points in D4) at four selected sites: (a) S31, (b) S32, (c) S33, and (d) S34 (cf. Table 1). In the legend,

‘6 km’ refers to ‘1� 1 at 6 km’, and ‘2 km’ to ‘3� 3 at 2 km’.


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0 3 6 9 12 15 18 21 240.1

0.2

0.3

0.4

0.5

0.6

0.7

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0.9

Time (UTC)


0 3 6 9 12 15 18 21 245

10

15

20

25

30

35

40

45

Time (UTC)

54 18 6 20

0.1

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Resolution (km)54 18 6 2

0

0.5

1

1.5

2

2.5

Resolution (km)54 18 6 2

0

0.5

1

1.5

2

2.5

3

3.5

Resolution (km)

u* (m

s-1

)

Var

ianc

e of

T (

°C)

Var

ianc

e of

WS

(m

s-1

)

Var

ianc

e of

Vd

(10-2

cm s

-1)

Ra

(s m

-1)

(a) (b)

(c) (d) (e)

Fig. 9. Top: same as Fig. 5b but for (a) friction velocity (u*), and (b) aerodynamical resistance (Ra); Bottom: mean spatial variance of

temperature (c), wind speed (d), and dry deposition velocity (e) at the lowest s level over S11 site (54 km� 54 km area) (cf. Fig. 2) resolved

by different resolutions. The time-averaged period is from 0000 UTC 7 May to 2400 UTC 20 May 2001 (temporal sample number:

N ¼ 337).

0 3 6 9 12 15 18 21 248

9

10

11

12

13

14

15

16

1754 km18 km 6 km 2 km

0 3 6 9 12 15 18 21 243

3.5

4

4.5

5

5.5

6

6.5

0 3 6 9 12 15 18 21 24-50

0

50

100

150

200

250

300

Time (UTC)

0 3 6 9 12 15 18 21 240

20

40

60

80

100

120

140

160

180

Time (UTC)

Win

d sp

eed

(m s

-1)

Tem

pera

ture

(° C

)S

ensi

ble

heat

flux

(W

m-2

)

Late

nt h

eat f

lux

(W m

-2)

(a) (b)

(c) (d)

Fig. 8. Same as Fig. 5b but for (a) temperature, (b) wind speed at the lowest s level, (c) sensible heat flux, and (d) latent heat flux.


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X-grid

Y-g

rid

5 10 15 20 25 30 35 40 45

5

10

15

20

25

30

35

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Fig. 11. Same as Fig. 10 but for dry deposition velocity (Vd)

estimated using dominant land use.


The mean diurnal cycle of area-averaged aero-dynamical resistance (Ra) over the S11 site is shownin Fig. 9b in order to compare the difference in Ra

between various different resolutions. Here, theaverage of resistance is defined as the reciprocal ofaveraged conductance, i.e.,

hX i ¼1

1=NPN

i¼11=X i

, (11)

where X denotes Ra, and N is the sampling numberover time and space. The results indicate that thesystematical difference exists between various reso-lutions. The difference between 54- and 2-kmresolutions, for example, can reach as high as 19%.

The mean spatial variance of temperature andwind speed at the lowest s-level, and dry depositionvelocity over S11 site (1 point in D1, while 9 pointsin D2, 81 points in D3, 729 points in D4) is alsoshown in Fig. 9. As can be seen, the spatialvariability increases with the increase of the resolu-tion. This implies that subgrid effect is significant.

4.2.5. Spatial distribution of dry deposition velocity

Fig. 10 shows the spatial distribution of the14-day mean Vd resolved with 2-km grid resolution.It is clear to be seen that the spatial variation of Vd

at subgrid scale is evident.

4.3. Land use effect on dry deposition velocity

To examine the effect of land use on Vd

statistically, an estimate of dry deposition velocity

X-grid

Y-g

rid

5 10 15 20 25 30 35 40 45

5

10

15

20

25

30

35

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Fig. 10. Spatial distribution of the 14-day mean dry deposition

velocity (Vd) computed (aggregated) using fractional land use at

the height of 19m over D4 with the 2-km horizontal grid

resolution. The time-averaged period is from 0000 UTC 7 May to

2400 UTC 20 May 2001 (temporal sample number: N ¼ 337).

analyzed in Section 4.3 and Section 4.4 is based ondominant land use within the gridcell, not aggre-gated on fractional land use. It is worth noting thatdominant land use is only used in computing Vd,not in simulations of the meteorological conditions.

Fig. 11 presents the spatial distribution ofthe 14-day mean Vd estimated using domintantland use over D4 with 2-km resolution. In general,Vd over crop and forest are larger than those overurban, while Vd over water bodies (sea and lake) aremuch smaller compared to those over the vegetatedsurfaces, which is due to the the difference in theaerodynamic and thermal characteristics of thesurfaces. There is a sharp contrast in Vd betweenland and water surfaces. Compared to dominantland use distribution shown in Fig. 2, the estimatedVd displays a pattern similar to land use distribu-tion, implying that land use is the dominatingfactor in determing the subgrid scale variation.Also, it is interesting finding out the difference inestimating Vd between using fractional land use andusing dominant land use by comparison of Figs. 10and 11.

The analysis of the temporal variation of Vd

based on land use classification (not shown)indicates that Vd displays a clear diurnal cycle overall the land use categories except for water bodies,but having different magnitude. Moreover, Vd

varies significantly from day to day, indicating theimportance of the varying synoptic conditions.Some statistical parameters of Vd for different landuse categories are summarized in Table 2, whichshows the variation in Vd.

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0 3 6 9 12 15 18 21 240

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UrbanCropForestWaterAll

0 3 6 9 12 15 18 21 240

0.05

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Time (UTC)

7-12 May13-20 May7-20 May

Vd

(cm

s-1

)V

d (c

m s

-1)

(a)

(b)

Fig. 12. (a) Diurnal cycle of dry deposition velocity (Vd) at the

height of 19m averaged over different land use categories in D4

with 2-km grid resolution. The diurnal cycle is based on 14-day

hourly modelled data (7–20 May 2001); (b) Diurnal cycle of area-

averaged dry deposition velocity (Vd) at the height of 19m over

D4 for different synoptic situations: 7–12 May (‘dry condition’)

and 13–20 May (‘wet condition’). The time-averaged period is

from 0000 UTC 7 May to 2300 UTC 12 May 2001 (sample

number: N ¼ 144), and from 0000 UTC 13 May to 2400 UTC 20

May 2001 (sample number: N ¼ 193), respectively. The diurnal

cycle of Vd averaged over the whole period (7–20 May 2001) is

also presented within the plot for comparison.

Table 2

Statistical parameters of dry deposition velocity Vd (unit: cm s�1)

averaged over different land use categories in D4 with 2-km grid

resolution (based the hourly modelled data during the 14-day

period) (sample number N ¼ mn, n ¼ 337, m is the grid number

for each land)

Land use Mean Stda CVb (%) Min Max Median

Urban (m ¼ 57) 0.294 0.161 55 0.005 0.802 0.308

Crop (m ¼ 69) 0.426 0.284 67 0.005 1.508 0.429

Forest (m ¼ 810) 0.419 0.298 71 0.005 1.932 0.383

Water (m ¼ 819) 0.041 0.008 19 0.001 0.048 0.043

aStd denotes the standard deviation.bCV Represents the coefficient-of-variation (dimensionless),

defined as: CV ¼ (Std/Mean)� 100%.


4.4. Dependence of dry deposition velocity

The preceding section demonstrates the strongdependence of Vd on land use. To generalize thisfeature, Fig. 12a presents the averaged diurnal cycleof the 14-day Vd based on land use classification. Itcan be seen that Vd displays a clear diurnal cycleover the vegetated surfaces, and shows a strongdependence on land use category. The diurnal cycleof Vd and the range of its magnitude revealed in thisstudy are consistent with the previous observationaland numerical studies (e.g. Brook et al., 1999; Pleimet al., 1999, 2001; Pio et al., 2000; Zhang et al., 2002,2003; Wesely et al., 2001; Michou et al., 2005).

The examination on the topographic effect showsthat the dry deposition is not dependent on theterrain height in this region (not shown). This ispartly due to the fact that the model terrain withinthe D4 is relatively flat, having a model terrainheight of less than 200m for most gridpoints.

The difference between the first half and secondhalf of the whole period indicates that the synopticweather condition has an important role in shapingthe Vd. Southwestern Sweden was dominated by ananticyclonic condition during the period from 7 to12 May, and by a cyclone from 13 to 20 May 2001.The second period is characterized by clear skydays, while the last period by cloudy or rainy days.Thus, the meteorological conditions during the firstperiod can be regarded as dry and the second periodas wet conditions. Fig. 12b plots the averageddiurnal cycle of Vd under these two conditions,together with the averages over the whole period. Itis clearly seen that Vd under wet conditions is muchlarger than those under dry conditions. This agreeswell with the results by Zhang et al. (2003).

4.5. Resistances and friction velocity

To understand the variation in Vd, Table 3summarizes the average values of aerodynamicalresistance, sublayer resistance, canopy resistance,and friction velocity, as well as stomatal resistanceand non-stomatal resistance over different land usecategories. As can be seen, the canopy resistancedominates in the variation of Vd, while theaerodynamical and sublayer resistances are rela-tively small. Stomatal and non-stomatal resistancesdepend to great extent on the solar radiation andcanopy wetness in spite of the fact that themagnitude of the stomatal resistance is larger thanthat of non-stomatal resistance. Compared to theprevious studies, the values of canopy resistance in

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Table 3

Statistical averagea of aerodynamical resistance (Ra), sublayer resistance (Rb), canopy resistanceb (Rc), stomatal resistance (Rst), non-

stomatal resistance (Rns), and friction velocity (u*) over different land use categories in D4 with 2-km grid resolution

Land use /RaS (sm�1) /RbS (sm�1) /RcS (sm�1) /RstS (sm�1) /RnsS (sm�1) u* (m s�1)

Urban (m ¼ 57) 17 11 307 978 439 0.526

Crop (m ¼ 69) 24 13 195 897 244 0.463

Forest (m ¼ 810) 17 12 106 1204 246 0.508

Water (m ¼ 819) 246 18 2000 N/A 2000 0.167

aBased on the hourly modelled data during the 14-day period) (Sample number N ¼ mn, n ¼ 337, m is the grid number for each land use

category within D4); The averaging method of resistance is described in Eq. (11) in the text.bRc is a function of stomatal resistance (Rst) and non-stomatal resistance (Rns) over vegetated surfaces, while specified to be constant

(2000 sm�1) as ground resistance (Rg) over water bodies indicating surface uptake.


this study are within the reasonable range (e.g. Pioet al., 2000; Cieslik, 2004).

In this study, no upper or lower limit of thefriction velocity is set. Therefore, a very smallfriction velocity would lead to a very large value ofaerodynamical, sublayer or in-canopy aerodynami-cal resistance, but this does not significantly affectthe results of dry deposition velocity. The frictionvelocity ranges from 0.009 to 1.678m s�1 for urban,from 0.007 to 1.427m s�1 for crop, from 0.008 to1.751m s�1 for forest, and from 0.004 to 0.485m s�1

for water, respectively. The mean and median valuesare listed in Table 3, which are comparativelyreasonable (e.g. Zhang et al., 2003).

5. Summary

The MM5 mesoscale model coupled with PX landsurface model is used in combination with a newlyrevised parameterization for the dry depositionvelocity of ozone. The subgrid scale variation inthe deposition velocity is studied. Also, this varia-tion is related to land use, topography, and synopticweather conditions.

The results show that a systematic difference inthe deposition velocity modelled by different resolu-tions exists, and the difference varies diurnally anddaily. The effect of subgrid scale on the depositionvelocity is evident, but a 6-km grid resolutionfor model application over the Swedish west coastmight be practically good enough to resolve theinhomogeneity of the surface properties in this area.However, this needs to be validated with measure-ments when available.

Dry deposition velocity depends strongly on landuse which causes direct change in the local drydeposition and indirect effect on subgrid scalemeteorological conditions. This subgrid scale varia-

tion is modulated by weather conditions. However,no significant effect of terrain height could beidentified for the study area. The difference in theestimated dry deposition velocity between themethods using fractional land use and usingdominant land use exists for the 2-km resolutiondomain, but is not significant.

Acknowledgements

The authors greatly appreciate Dr. David Simpson(Norwegian Meteorological Institute), Dr. LeimingZhang (Meteorological Service of Canada), Dr.Laurens Ganzeveld (Max-Planck Institute for Chem-istry), Dr. Jan Willem Erisman (Energy researchCentre of the Netherlands) and Martine Michou(Meteo-France) for helpful discussions and sugges-tions on dry deposition parameterization. Specialthanks go Dr. Katarina Borne for GOTE2001 datasupport, to Dr. Leiming Zhang, who gave us accessesto the source code corresponding to the article on a

revised dry deposition parameterization (Zhang et al.,2003) and commented on the manuscript critically,and to Dr. Laurens Ganzeveld for valuable commentson the manuscript. The authors would also like tothank the Swedish Environmental Protection Agency,and Formas who supported this work. Moreover,two anonymous referees are thanked for theirvaluable comments and constructive suggestions toimprove the manuscript.

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Modelling subgrid scale dry deposition velocity of O3 over the Swedish west coast with MM5-PX model

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