SO2, sulfate and HNO3 deposition velocities computed using regional landuse and meteorological data

,~tmOSld~ric E m ~ m w e m

Vol, 20, No, 5. pp. 949-964, 1986

0004--6981/116 $3.00 + 0.00 Pergamon Press Lid.

Pnnted in Great Britain.

SO2, SULFATE AND HNOa DEPOSITION VELOCITIES COMPUTED USING REGIONAL LANDUSE AND METEOROLOGICAL DATA C. J. WALCEK,R. A. BROSr and J. S. CHANG National Center for Atmospheric Research*, Boulder, CO 80307, U.S.A. and M. L. WESELY Argonne National Laboratory, Argonne, IL 60439, U.S.A. (First received 7 August 1985 and in final form 19 September 1985)

Ah~i~iet--Deposition velocityfields were generated for SO2, sulfate and HNO3 over eastern United States and southeastern Canada by combining detailed landuse data with meteorological information predicted using a mesoscale meteorology model When there is significant variation in land type within an averaging area, it was found that subgrid scale meteorological variations can significantly influence area-averaged deposition velocities. The assumption that uu, is constant over the averaging area can realistically address the subgrid variations in wind speed and friction velocity. For a 3-day springtime simulation, domain.averaged mid-day SO,, sulfate and HNO3 deposition velocities at a height of approximately 40 m were found to be 0.8 cm s- ~, 0.2 cm s- t and 2.5 cm s- ~, respectively. At night, the deposition velocities were approximately 50%, 45% and 70% of the corresponding daytime values for SO2, sulfate and HNO a. Using a simple parameterization to account for rainfall-wetted surfaces increased domain-averaged SOa deposition velocities by up to a factor of two, indicating thai precipitation can significantlyenhance dry deposition of SOs. Key word index: Dry deposition, SO2 deposition, sulfate deposition, HNO3 deposition, acid deposition.

I. I N T R O D U C T I O N

Emissions of sulfur and nitrogen compounds and their ultimate removal are widely recognized for their role in the delivery of sulfate, nitrate and acidity to sensitive ecosystems. According to preliminary estimates of Garland (1978), dry deposition of SO 2 and sulfate appears to be a major contributor to total deposition (wet and dry) of sulfur on a regional scale, in addition, sulfate and HNO 3 deposition have been identified by Galloway and Likens (1981) as the two major contributors to both wet and dry acid deposition in the eastern United States. In an attempt to understand the processes responsible for acid deposition and to quantify the effects that any reduction of pollutant emissions might have on acid deposition, several long-range transport/dq)osition models have been developed (Carmichael and Peters, 1984; Eliassen et al., 1982). These models parameterize dry deposition of trace and particles in terms of a deposition velocity, vd. This velocity, when multiplied by the concentration of pollutant over a given surface, yields the flux of pollutant to the surface. Early regional models of *The National Center for Atmospheric Research is sponsored by the National Science Foundation. 949

sulfur transport (e.g. Bolin and Persson, 1975; Sheih, 1977) often assumed that deposition velocities remained constant in either time or space, although strong variations soon became apparent (Garland, 1978). The assumption that deposition velocities remain constant in either time or space will severely limit a model's ability to predict sulfur or nitrogen deposition on an episodic basis, since deposition measurements show considerable variability depending on the type of surface, insolation, local meteorological conditions and season. Several recent field studies of sulfur deposition to surfaces such as forests (Hicks et al., 1982) and grasslands (Wesely et al., 1985) have shed some quantitative insight on the factors which cause the observed variability in deposition velocities. These studies have shown considerable variability over a given land type and have also shown systematic variations in deposition velocity between different land types. In addition, deposition velocities for sulfur were found to correlate with momentum or heat fluxes in the lowest atmospheric layer (Hicks et ai., 1982). As a result, several empirical parameterizations have been developed to describe the observed variability (Wesely et al., 1985: Shcih el al., 1979). These paramcterizations allow deposition velocities to be estimated over large areas.

950

C.J. W~acFx eta/.

Sheih et ai. (1979) used these lxuameterizations to generate deposition velocity fields for use in mesoscale transport models. His study demonstrated that deposition velocities vary considerably over the northeastern United States. That study assumed a rather crude, uniform meteorological characterization of stability and wind speed over the entire United States, and thus did not accurately assess the inherent temporal and spatial variability in meteorology and its effect on deposition velocities. In a recent extension of Sheih et al.'s study, Masse and Voldner (1983) mapped monthly average SO2 and sulfate deposition velocities over Canada and eastern United States. In their study, meteorological parameters were averaged during an entire month to obtain the meteorological information necessary to compute deposition velocities. This averaging procedure cannot fully resolve the effects of diurnal variability in deposition velocities. To calculate deposition velocities at grid points representative of grid areas, the usual procedure (Sheih et al., 1979) is to average deposition velocities linearly after being weighted by the fraction of area covered by each land type within each grid cell. The deposition velocity variations within averaging areas have not been reported, which limits evaluation of the realism of the results produced. Also, a rather fundamental shortcoming arises in these modeling approaches as a result of neglecting the effect of rainfall on SO2 deposition velocities, which prevents simulation of the enhancement of deposition velocities observed by Fowler (1978) above rain-wetted surfaces. Due to such deficiencies, previous dry deposition models cannot be readily utilized to estimate deposition velocities for episodic mesoscale deposition models. The following discussion presents a technique for calculating SOa, sulfate, and HNO3 deposition velocity maps using a resistance modeling parameterization scheme together with a fine-resolution (1 h in time, 80 km in space) meteorological and landuse database. Information about subgrid distribution of land among several landuse categories is retained when computing grid-average deposition velocities. A sensitivity analysis oftbe treatment of subgrid scale landuse variations is also performed. Deposition velocities are then predicted and analyzed for a 3-day springtime period. In the final section, a preliminary analysis of the effect of rainfall-wetted surfaces on SO2 deposition velocities is presented.

2. DEPOSITION VELOCITY CALCULATIONS

Deposition velocities inferred from observations of trace-gas and particulate deposition to surfaces have shown considerable variability. Much of this variation can be accounted for by meteorological variability and differences in surface characteristics over which the measurements were performed. A series resistance modeling approach has been employed to account for these effects. The resistance model, as outlined by

Wesely and Hicks (1977) and Fowler (1978), among others, follows an electrical analog by assuming that the deposition velocity is inversely proportional to the sum of three resistance terms

Vdm (ra +rb.Frs) -1.

(1)

Aerodynamic resistance to pollutant transport through the lowest atmospheric layer r, is controlled predominantly by turbulent mixing above a surface. Resistance to transport across the atmospheric sublayer in contact with surface elements r b is determined by the rate of transport by molecular and turbulent diffusive transport processes over a surface. Measured deposition velocities are frequently found to be considerably smaller than the inverse sum of the sublayer and aerodynamic resistances, implying that there is an additional resistance encountered at the surface itself. This surface (or canopy) resistance r, is usually inferred from deposition velocity measurements as a residual resistance, computed by subtracting the measured sublayer and aerodynamic resistances from the total resistance. Aerodynamic resistance

Aerodynamic resistance r, proportional to the level of turbulent mixing in the lowest atmospheric layer has been the subject of micrometeorological study for several years (see Businger (1973) and Wyngaard (1973) for an overview). The transport of quantities such as heat, momentum, and water vapor is analogous to the transfer of trace gases and particles near surfaces. Following the procedures used by Sheih et al. (1979), we approximate r, as in (Z/Zo)- ~h r, =

ku,

(2)

Here, k is the yon Karman constant, u. is the friction velocity (root mean covariance between the horizontal and vertical velocity components), =o is the effective level at which the horizontal wind speed approaches zero, commonly referred to as the roughness length. and Ohis an integral diabatic influence function related to the local atmospheric stability. In the form presented in Equation (2), it has been assumed that turbulent transport of a pollutant nonreactive in air is similar to the transport of heat in the lowest atmospheric layer, in agreement with several observations of pollutant transport. Surface roughnesses for typical land types have been reported by Panofsky and Dutton (1984) and are tabulated in Table I for different land types and seasons. Over water, surface roughness is assumed to vary with friction velocity. The following empirical relationship is used: z0 ,.,~ (m) = 0.016 u,a/g + v(9.1 u,)

(3)

where v is the kinematic viscosity of air (Hicks and Liss, 1976). Values for the stability correction function ~h in

SO=, sulfate and HNO2 deposition velocities

951

Table 1. Surface roughness and SOa surface resistance (sin-I) Landuse

Season

Zo (In)

Urban

spring

1.00 1.00 1.00 1.00

snnlwrt~r

early fall late fall winter

1.00

spring

Agriculture

0.03 0.25 0.10 0.005 0.001

SUl]lmer

early fall late fall winter SUlTU'ner

0.02 0.05

early fall

0.05

late fall winter

0.05

Deciduous forest

Coniferous forest

Range

Night

Wetted

1000 1000 1000 1000 200

1000 1000 10130 1000 200

1000 1000 1000 1000 200

10130 0 1000 1000 200

50 70 5OO 50 100

60 120 500 50 100

0 0 100 50 100

1000 1000 1000 1000 200 75

100

200

500

500 50 100

500 50 100

100

140

200

400

0.001

100 500 500 100

140 500 500 100

200 500 500 100

500 500 500 100

100 100 100

spnng summer early fall late fall winter

1.00 1,00 1.00 1.00 1.00

100 60 1000 10130 1000

200 130 1000 1000 1000

400 300 1000 1000 1000

1000 1000 1000 1000 1000

0 0 500 500 1000

spnng summer early fall late fall winter

1,00 1,00 1.00 1.00

150 150 800 800 500

240 240 800 800 500

400 400 800 1000 500

1000 !000 800 1000 500

0 100 100 500

spring

1.00 1.00 1.00 1.00

100 70 800 800 800

200 140 800 800 800

400 300 800 1000 800

1000 1000 800 1000 800

0 0 300 300 800

spring

Forested swamp

Insolation (Watts m - 2) > 400 200-400 0-200

1.00

summer early fall late fall winter

1.00

spnng

~ 0.06

0 0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0 0

0 0

0 0

0 0

0 0

early fall late fall winter

0.10 0.15 0.10 0.10 0.001

50 50 100 100 100

60 60 100 100 100

75 75 100 100 100

100 100 100 100 100

0 0 75 75 100

Agriculturespnng range mixture summer early fall late fall winter

0.03 0.10 0.08 0.02 0.001

75 100 500 200 100

100 140 500 200 100

150 200 500 200 100

250 500 500 200 100

0 0 100 100 100

Water

surfllner

early fall late fall winter

cm see Equation (3)

spnng

Swamp

summer

Equation (2) have been parameterized in terms of the M o n i n - O b u k h o v length scale, L, by Wesely and Hicks (1977). For unstable conditions !0 > z/L > - 1), ~bh can be estimated as Oh ffi exp {0.598 + 0.39 In ( - z/L) - 0.09 [In (- z/L)] 2 } and for stably stratified flow (0 < eh

=

- 5z/L.

z/L <

(4) I) as

(5)

Meteorological model With the approach outlined above for defining the

aerodynamic resistance to deposition, there are several meteorological inputs required for each surface grid point. Surface observations of these meteorological factors could be used to generate the required information, although observations are only available with a relatively coarse spatial resolution and represent the meteorology at a particular point, which may not be representative of a larger area. In this study, we use a prognostic mesoscale meteorological model to generate dynamically consistent fields of meteorological variables with an hourly, 80-kin resolution encompassing the entire eastern United States. The temporal and spatial resolution of the resulting meteorological data

C J. W~¢'~ a al.

952

is of considerably higher resolution in both time and space than data available from an observational network. The data obtained from the model represents area-averaged meteorological quantities for each (80 kin) = area in the model domain. The model was developed by Anthes and Warner (1978) to assist in modeling the transport 9 f atmospheric pollutants. It is a hydrostatic, primitive equation model that has been used in previous studies of mesoscale meteorological phenomena (Kuo and Anthes, 1984). The model has 15 vertical layers between the ground and the 100 mb pressure surface (P~). A terrainfollowing coordinate system is used where the vertical coordinate, o, is equal to (P-Ptop)/(P,~r-Ptopg where P , ~ is the surface pressure and P is the pressure at the surface where o is evaluated. The vertical resolution of the model is variable, with the resolution near the surface being about 80 m, while at upper levels, the resolution is on the order of 1 km or more. All deposition velocities are computed using meteorological parameters predicted at the o--0.995 level, which corresponds to a height of approximately 40 m above the surface. Initial and boundary conditions for the model are provided by the National Meteorological Center global analysis, upgraded by incorporating rawinsonde observational data in the region over which the model is being applied. Surface layer energy and moisture budgets were parameterized using a Blackadar {19"/8) slab model, which incorporates the same landuse database used in the present study. A high resolution planetary boundary layer submodel (Zhang and Anthes, 1982) is utilized to quantify the transport of heat, moisture, and momentum in the lowest atmospheric layers. The meteorology model was used to generate hourly, three-dimensional fields of wind speed, temperature, and water vapor mixing ratio. In addition, hourly fields of surface temperature, pressure, and precipitation rate were produced. These data were used to construct twodimensional fields of friction velocity, u., and Monin-Obukhov length scale, L, at the lowest layer of the meteorological model using parameterimtions developed by Louis (1979). These parameterizations are based on the experimental observations of Businger etai. (1971) and are consistent with Equations (2-5) above. Friction velocity was computed from the stability, surface roughness and wind speed in the lowest atmospheric layer by first computing the bulk Richardson number,

ku [

U* == ln(z/zO) k.

1

?

(1 +4.TRb ) ,

]"

rl- 9.4e

u*=inlz/zo)L

(I+7.4B)J

(6)

'

where g is the acce.leration of gravity, z is the height at which deposition velocities are being calculated, 0 . and 0,I are the virtual potential temperatures at height z and at the ground, and u is the wind speed at height z. The friction velocity is computed in the following

'

Rb>0.

(7)

Rb<0,

(8)

where a = 9.4 [k/In (Z/Zo)]= [[Hblz/zo]' a. The Monin-Obukhov length L in Equations (4) and (5) was calculated from L = 0"~u*'=.

(9)

kgH

H is the virtual potential temperature flux through the lowest atmospheric layer, also parameterized by Louis (1979) as

uAO, r_ k 12rL(l+4.TRb) =I ] J

U=

u,o.[L

j TIt

, R,> 0

(I0)

(l-;-E.)J'9'4"' "l R,
(11)

where A0v= 0 , - 0 r e , and B is defined after Equation (8). Meteorological inputs required to compute u. and L include air temperature above the surface, surface temperature, moisture, and the wind speed over each grid area. These variables were generated by running the mesoscale meteorology model for a 72-h period from 22 to 24 April 1981. During this period, a cyclone developed over the midwest United States, and subsequently intensified as it moved across the eastern United States and off into the Atlantic Ocean. Forecasted meteorology was found to agree reasonably with observations made during the same period (Kuo et aL, 1984). Figures I and 2 show fields of temperature difference between the air and ground and wind speed, for times corresponding roughly to local midnight and local noon as predicted by the meteorological model. One can see that wind speeds are higher over smoother water surfaces, and land areas are significantly more unstable during the daytime.

Sublayer and surface resistance The resistance to trace gas transport through the layer of air in contact with a surface has been discussed by Wesely and Hicks (1977). On the basis of a number of studies, they suggest the following form for r b

21-K1

Rb = V.(0. -- e, s)

0vsU2

fashion:

k-;;.LE.j

~'3 '

(1:)

where K is the thermal diffusivity of air and D s is the molecular diffusivity for a trace gas in air. While numerous expressions exist to describe sublayer resistan(e, except for extremely unstable conditions over very rough surfaces, rb is usually smaller than the aerodynamic resistance. As a result, calculated deposition velocities are relatively insensitive to the form

SOa, sulfate and HNOa deposition velocities

953

,.xr. . .

.

.,,., . ~ ]..!.....

....... -i"

.,.:,_...;.;;_.~ Q~i....~....,--~~~-" ...... + -" ~£.i;j~ ~//[~ ..".. !~i,

Fig. 1. Difference between air temperature at approximately 40 m and ground temperature (K) as predicted by meteorology model at (a) 0500 GMT 24 April 1981 (near local midnight), and (b) 1700 GMT 24 April 1981 (near local noon). Shaded boxes show locations of areas studied in further detail in this analysis.

m

Fig. 2. Wind speed (ms- ') at approximately 40 m above surface as predicted by meteorology model at (a) 0500 GMT 24 April 1981 (near local midnight), and (b) 1700 GMT 24 April 1981 (near local noon).

of parameterization used for r b. Over water and aerodynamically smooth surfaces, the aerodynamic and sublayer resistances for SO= have been combined into a single term, as suggested by Hicks and Liss (1976): (r, + rb)wat¢r = In (ku.z/Dl)-

ku,

Oh

(13)

The surface resistance, r,, is the most difficult term to evaluate at present. Over a given area of land, numerous plant, soil, water, and other material surfaces are

present, each with a characteristic resistance to uptake of a given pollutant. Within plant canopies, r, depends on factors such as stomatal activity, cuticular and mesophyllic resistance, and the solubility or reactivity of a given trace gas. FtLrthet quantification of each of these factors is needed. In this study, surface resistances have been estimated using semi-quantitative correlations between numerous measurements of surface resistance and surface type. Sheih et ai. (1979) eztensively reviewed ~ deposition velocity field measurements and tabulated surface resistances for

954

C J. W ~ ' ~ g a al.

SO, as a function ofland type and season. Their results have been updated and are presented in Table 1. Th/s table shows surface resistance for individual seasons and subdivides daytime resistance into three categories, corresponding to insolation, greater than 400 Watts m -2, between 200 and 400 Watts m -2, and less than 200 Wattsm-'. In addition, there is a night category and a separate category for surfaces wetted by dew or precipitation. In this modeling exercise, dew was assumed to occur if the ground temperature fell below the dew point of the air above the ground. The parameterization used to describe rainfall-wetted surfaces will be described in a later section. Surface resistance for nitric acid vapor, due to its high miscibility on surfaces, was assumed to be zero for all land types, in agreement with the observations of Huebert and Robert (1985).

Particulate sulfate deposition Sublayer and surface resistancesfor deposition of particulate sulfate have been estimated and parameterized from eddy-correlation measurements by Wesely et al. (1985). Following their treatment, the terms rb and rsare combined into a singlequantity Vds, surface deposition velocity: (14)

(r b + rs) = v ~ I .

Values for Vd, have been parameterized ~___ecordingto stability as indicated by the Monin--Obukhov length scale: v~, =

•,,

u,/50o, u,

F1

:/L ~ O. (15) /300'~2/31

+t ,S

.l' :/L < o . . 6 )

For highly unstable conditions, Wesely et al. (1985) found that the planetary boundary layer height (Pu) appears to influence Vd,:

Yd, ==O.O009U.( P u / - L) 2/3

P u l l < - 30. (17)

For the following springtime simulation over the northeastern United States, the mid.day planetary boundary layer height was approximated as 1500 m

from data compiled by Holzworth (1967). For sulfate deposition velocity, observations suggest that there is a distinct upper limit which depends on landuse type. As a result, we require that Yd. ~ vm,

where Vmis the observed maximum deposition velocity. Values for vmare tabulated in Table 2 as a function of season and land type. Equations (14) to (17) are based on a limited number of studies that do not include, for example, water surfaces. Nevertheless, we will apply the equations to all land types in order to carry out sensitivity studies.

Landuse information Land data needed by both the meteorological and deposition models were obtained from the United States Environmental Protection Agency. The land data were compiled from a survey of maps and satellite imagery. In the domain between 67 and 105 deg longitude and 24 to 50 deg latitude, landuse information with a resolution of 1/4 deg longitude by 1/6 deg latitude (roughly 20 km resolution) was available. This subregion of the mesoscale meteorology model was used to map deposition velocity estimates. Surfaces were classified into one of nine landuse categories shown in Table 1, and a percentage distribution for each land category was then assigned to each grid area. The 20 km resolution landuse database was mapped onto the 80 km resolution grid used by the meteorology model, with all subgrid landuse information retained. Figures 3(a) and 3(b) show the distribution of agriculture land and deciduous forest over a portion of the modeling domain. Darker shades of grey indicate nearly 100 ~ coverage in each grid cell by the specified land type. Area averaoin# of deposition velocities Deposition velocities were computed by taking an area-weighted average of the deposition velocity over all land types encountered within each (80 km)2 area in the modeling domain as follows Vd

=

EfiVdi,

,~ason

Urban Agriculture Range Dc~:luous forest Coniferous forest Forest/swamp Water Swamp Agriculture/range

Early Late Spring Summer Autumn Autumn 0,I 1,0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

0.1 1,0 1.0 l.O 1.0 1.0 1.0 1.0 1.0

(19)

where fi is the fraction of the averaging area covered by

Table 2. Ma.x~um sulfate deposition velocities ( m s - t )

'land type

(18)

0,I 1.0 1.0 0.1 0.8 0.4 1.0 1.0 1.0

0,I 1.0 1.0 0.1 0.8 0.4 1.0 1.0 1.0

Winter 0,I 1.0 1.0 0.1 0.8 0.4 1.0 1.0 1.0

SO2, sulfate and HNO3 deposition velocities

l

955

~~..

~:i ~'~

I

*.250

Fig. 3. Distribution of two land types over modeling domain. Darker shades correspond to coverage closer to 100~ in each 20 km [gridsquare. (a) Agricultural and (b) deciduous forest distribution.

each of the nine landuse eategnries and v,u is the deposition velocity over each land type contained in the averaging area. A problem arises in calculating the aerodynsmic resistance for an (80 kin) a averaging area if several land types are present within the area. Specifically, each land type will have a characteristic roughness, friction velocity, and wind speed which may

deviate significantly from a grid-averaged value of each of these quantities. For example, a grid cell may be covered predominantly with forest, which has a surface roughness of approximately 1 m, and yet areas of fairly mnooth lakes (Zo < 1 ram) within the forested areas will have significantly different levels of turbulence over them. In this analysis, rather than compute a 'grid

956

C.J. W ~

average' aerodynamic resistance, individual aerodynamic resistances were computed for each land type encountered within the (80 kin) ~ area represented by each grid point. Since meteorological factors are only predicted for the (80 kin)= area, certain assumptions must be made regarding the relationship between the subgrid variations resulting from landuse variations, and the grid-averaged n~eteorological information which is available from the meteorological model. In the following sensitivity study, it was assumed that the atmospheric flow over each land type could be characterized as homogeneous and steady-state; horizontal derivatives of turbulent parameters were assumed to be negligible over each surface type. Transient aerodynamic 'edge effects' which occur between the boundaries of differing lind types are not treated in the following exercise, although these effects may influence area-averaged deposition velocity estimates. Similarity relationships among parameters such as friction velocity, surface roughness, geostrophic wind velocity, and surface sensible heat flux in the atmospheric boundary layer have been formulated and investigated for a number of years (e.g. Estoque, 1973; Tennekes, 1973). A deficiency of concern here is the lack of a good, simple description of how wind stress and wind speed in the surface layer vary over nearby surfaces with different roughness lengths. Sophisticated, extremely fine-mesh numerical modeling cannot he adopted for the present purpose to resolve the local spatial variations. Instead, we rely on the intuitive and commonly observed result that u increases while u. decreases, and visa versa, from one local surface to another with different surface roughness. In support of this approach, computations using bulk transfer equations for a stationary and horizontally uniform atmospheric boundary layer (e.g. from Brutsaert, 1982) indicate that the product u u . at a height of 10 m typically varies by only 10 % as zo varies from 0.I to 30 crn, for a given geostrophic wind velocity, sensible heat flux, and latitude. Since these computations are for uniform surfaces with constant Zo rather than the patchy areas (assumed to have small dimensions compared to the mixing layer height) that we must consider here, the indication that u u , is approximately constant from surface to surface might not be accurate quantitatively. However, this approach is more realistic than assuming a constant wind velocity, as was done by Sheih et al. (1979). This is particularly important when r, in Equation {1) is small and thus reasonably accurate estimates of u. are vital in order to obtain good estimates o f r , , rband hence vd. Hence, we adopt the following approach: MiU* i =

U U * ----- constant,

(2o)

where the nonsubscr/pted variables u and u, refer to grid-averaged wind speed and friction velocity, while the i-subscr/pted variables refer to the corresponding quantities over individual land types within the averaging area.

eta/.

To utilize Equation (20), we assume that the grid averaged wind speed u taken from the output of a mesoscale meteorology model conforms to a logarithmic wind profile in which the surface roughness length is averaged as follows:

Zo = exp [Zfi In z0~],

(21)

where fl is the ar~ fraction of landuse apportioned to each land category within the grid cell and z~ is the surface roughness for each landuse type, tabulated in Table 1. Equation (2I) is derived with the assumption that the average surface wind stress seen 'looking down' from the Ekman layer is simply the areaweighted average of stress values above each patch of surface, each with a certain value of zo. By using Equation (21) to determine the average surface roughness, together with Equation (20), one can predict wind speeds over the land types within each grid area. When this distribution of wind speeds is averaged, it was found that the resulting area.averaged wind speed was consistent with the mean wind provided as input from the deposition model. Once the grid-averaged friction velocity was computed, values for u. over each land type encountered within the grid area were computed from Equation {20). Aerodynamic resistances and values for Vd,were then computed over each land area before computing the area-average deposition velocity. Values for deposition velocities over the individual land types, vdi, within a grid area, as well as grid. averaged deposition velocities, Vd, were found to be sensitivity to the method with which meteorological conditions were assumed to vary over the different land types in an averaging area. There are several reasons for this sensitivity. Deposition velocity is influenced by two meteorological factors: atmospheric stability (temperature difference between the air and ground);and the wind speed. An example of the hourly variation in these two parameters over an (80 kin) 2 area in upstate New York and Vermont [the upper, shaded box in Fig. 1(a)] as predicted by the previously described meteorology model is shown in Fig. 4(a). These meteorological data will be used to demonstrate the sensitivity of deposition velocity calculations to subgrid scale land variations. The meteorological conditions shown in Fig 4(a) occurred over a mixture of land types within the (80 kin)z area. The apportionment was as follows: urban 4.7~o; agriculture 18.1 ~; deciduous forest 45 ~; coniferous forest 8.1%; forested swamp 1.9~; water 22 %. Rarely are all land types present within a given (80 kin) 2 area, but subgrid scale landuse variations will have the most pronounced effect on averaged deposition velocities under conditions when there is significant variation in lind type in the averaging area. Meteorological factors will have their most pronounced effect on deposition velocities if the surface resistance to pollutant uptake is small or depends strongly on meteorological factors, This is the case for nitric acid vapor, and thus the following sensitivity analysis was performed by calculating HNO 3 deposition velocities. Sulfate deposition velo-

SOz, sulfate and HNO3 deposition velocities

.._.,

6 E W I,IJ

4

4 3

2

2

0

i A o ~

_Z

_.--__.-.i.. ""~'1 '~,

~

,

II

I

I

I

t

I

#

ib -2

'i,,, ,,. -.."Zi tl 0

I

I

I

I

2

4

6

8

II

I

I

I

I

I

o

I

I

-3 I0 12 14 16 18 20 22 24

TIME (GMT) 2.3 APRIL 1081

Fig. 4{a). Hourly variation of wind speed and virtual potential t~mImraturc difference between surface and approximately 40 m as predicted over an area in upstate New York on 23 April 1981. Conditions were used in deposition velocity sensitivity analysis shown in Fig. 4(b).

E

3

~ .

I

I

I

I

~%.

%.

!

~: "='1

...."V=X .x, "X --

I

I

"%

#"

I

I

/U

s ¢onit

~¢"

1

I

UUo =consl

"'~/

"rl, " -.,=/ ' -~

~.~

I

glt

"X

I

,o,const" v.~

i

[ °o

I 2

I 4

I 6

I 8

TIMEIGMT)

II I I 0 12

I 14

I 16

23 APRIL

I I IR 20

12

24

1981

Fi& 4('0). Sensitivity.analysis of grid-averaged deposition velocity to subgrid landus¢ treatment. Constant u assumes that wind speed remains constant over all land types. Constant u, uses a logarithmically averaged surface roughness to compute a mean u,. Constant r a uses a logarithmically averaged surface roughness together with the mean wind speed to compute r=. Constant u u , allows u and u, to vary under the constraint that the product of the two is equal to the product of the grid-averaged u, and u.

cities will also be sensitive to meteorological variability

since its surface resistance,as parameterized here, is highly correlated with frictionvelocity. Figure 4(b)summarizes the hourly H N O s deposition velocitiescalculatedby varying the method by which meteorological factors were assumed to differ over land types encountered within the averaging area.One approximation can be made by assuming thatthe wind speed, u,at the heightat which depositionvelocitiesarc being calculatedremains constant over allland types in the area. For the distributionof land types chosen in this sensitivityanalysis, this assumption produced relativelyhigh deposition velocities,primarilybecause disproportionatelylarge values of us for the roughest surfaces were produced, which caused very large deposition velocitiesvia Equations (1)and {2).Rather than a constant wind velocity,observations suggest that the wind speed at a given levelwillbc lower over A~ 2 n : % J

957

rougher surfaces, and ~ g h e r over smoother surfaces under the same geostrbphtc forcang. An alternative approach is to assume a constant friction velocity over all land types. The friction velocity can be computed using a logarithmically averaged surface roughness ['Equation (21)1. This produces a wind field distribution over the land types, which when averaged, equals the mean wind provided as input to the deposition model. This approach allows wind speeds to vary over different land types, with higher winds over smoother surfaces. Using this method, friction velocities in the rougher areas of the grid are predicted to be considerably smaller than with the constant u method. Using u, equal to a constant value implies that friction velocity is insensitive to surface roughness variations within a small area, which is inconsistent with the computations by Estoque (I973) and the limited observations of Hicks and Wesely (1981) showing that u, is greater over areas with higher roughness. A very simple alternative approach can be taken by neglecting subgrid ianduse variations altogether, and computing a grid-averaged u, and aerodynamic resistance based on an averaged surface roughness over the grid area. This method underestimates friction velocity and overpredicts the aerodynamic resistance, since the averaged Zo will be smaller than the true zo in rougher areas. As a result, deposition velocities are severely underpredicted in regions of high roughness. In this paper, we use the approximation that u u . is constant in order to calculate deposition velocities over each land type in an averaging area. Predicted velocities lie between the extremes predicted using the previously described techniques, since both wind speed and friction velocity are allowed to vary over different land types in a self-consistent manner; this approximation produced wind speed distributions over the grid area which, when averaged, are consistent with the original wind speed provided as input by the meteorological model. Figure 5 shows how deposition velocity varied over individual land types within the upstate New York averaging area at 14 G M T of this sensitivity analysis. Spring values of z o in Table 1 are assumed. The logarithmically averaged roughness for the grid area was 8.5 crn, and roughly 60% of the grid area was composed of forest or urban areas, where the roughness was 1 m. The two land types shown in this figure correspond to the roughest and smoothest land types within the averaging area. With an assumed constant wind speed, the resulting level of turbulence is predicted to be rather high in the forested areas, and thus an excessively large deposition velocity is predicted there. With a constant friction velocity assumed over all land types, deposition velocities in the forested areas were predicted to be somewhat smaller since the friction velocity was computed using the smaller gridaveraged roughness. By assuming u. to b¢ constant, values for the friction velocity are underprcdicted in rougher areas, and overpredicted in the smoother areas

958

C.J. WAt.C~ et al. I

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)-

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Fig. 5. Variation in HN03 deposition velocity over individual land types within an (80 km)" area in upstate New York at 1400GMT (0900 h local time) in Fig. 4. Sets of bar graphs correspond to different assumptions about subgrid scale meteorological variations as related to grid-

averaged quantities. of the grid. With a constant aerodynamic resistance, there is no variation of deposition velocity with land type. The assumption used in this paper allows both friction velocity and wind speed to vary over different surface roughnesses. According to this approach, one can see from Fig. 5 that within an (80 km)' area, HNO3 deposition velocities can vary from 0.4 em s - i over water areas to up to 2.3 em s - : in rougher forested areas. For the particular distribution of land types in this area, the area-averaged deposition velocity was weighted towards the rougher surface values.

3. R E S U L T S

Hourly deposition velocities over the northeastern United States and southeastern Canada during the period 22-24 April 1981 were calculated. Deposition velocities were first computed in the absence of rainfall-wetted surfaces for SO2, S O l - and HNO3, and analyzed in detail over the modeling domain. Surface resistances anti roughness used in this analysis were taken from the spring season category in Table I. The analysis was repeated to obtain SO, deposition velocities with rain-wetted surfaces included. Deposition velocities in absence of rainfall wetted

gurfoce$ Deposition velocities for times corresponding roughly to local midnight and local noon are shown in

Figs 6, 7 and 8 for SOl, SO42- and HNO3. Deposition velocities for all three pollutants are higher during daytime and tend to correlate in varying degrees with land distribution, wind speed and stability. Sulfur dioxide deposition velocities over the model domain for 23 April 1981 are shown in Fig. 6 for 0500 G M T (midnight) and 1700 G M T (noon). Due primarily to the high resistance to uptake of SO2 on land surfaces at night, deposition velocities are relatively low, ranging between about 0.I and 0.6 cm s-:. During the day, decreases in surface resistance cause deposition velocities to increase dramatically over land areas to values between 0.6 and I.I cm s - I. Forested areas have lower deposit/on velocities than do agricultural areas, despite their lower aerodynamic resistance, indicating that surface resistance dominates the other resistance factors for SO, over land. Since the relatively large SO2 surface resistance for a given solar irradiation is related to land type only, independent of meteorology, deposition velocities strongly resemble the distribution of land types over the domain. Over ocean, SO, deposition velocities vary between 0.3 and 1.3 cm s - : and show little diurnal variability. Deposition velocity patterns correlate well with wind speed patterns in oceanic regions. In general, the model-predicted deposition velocities agree well with the range of observed deposition velocities compiled by McMahon and Denison (1979) for SO, over numerous land types. Sulfate deposition velocities for local midnight and noon are shown in Figs 7(a) and ?(b). During the day over land, SO~- deposition velocities vary between 0.05 and 0.6 cm s- 1, and are 0.2-0.3 cm s- ~ over the moderately rough surface of the central U.S., consistent with the measurements of Wesely et al. (1985). During unstable conditions, sulfate deposition velocity depends on the Monin-Obukhov length scale, L [see Equation (16)]. As a result, SO2,- deposition velocities tend to correlate more strongly with plots of temperature difference between the ground and air. At night under more stable conditions, deposition velocities are lower, ranging between 0.03 and 0.2 em s - 2. The values shown in Fig. ?(a) for the central U.S. are somewhat high for night-time conditions, resulting from the slightly unstable conditions following the recent passage of a cold front. Sulfate deposition velocities over ocean areas are influenced by both stability and wind speed variations, and range between 0.03 and 0.09 cm s - :, showing little diurnal variability. Nitric acid vapor deposition velocity maps are shown in Fig. 8. Smooth oceanic or lake areas are clearly outlined as regions of much lower deposition velocity, despite the higher wind speeds in these areas, indicating the strong dependence of HNO3 deposition velocity on u, and thus surface roughness. On the other hand, over land, areas of high HNO3 deposition velocities correlate with areas of high roughness and high wind speed. During the day over land, deposition velocities of HNO3 between 2 to 6 em s- : are typical, while at night, the velocities range from 0.9 to

SO,, sulfate and HN03 deposition velocities

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Fig. 7. Sulfate deposition velocities (can$- ,) evaluated at approximately 40 m above the surface for (a) 0500 GMT, 24 April 1981 (near local midnight) and (b) 1700 GMT, 24 April 1981 (near local noon).

5.2 cm s- ~. The highest deposition velocities predicted by this model occurred in areas of high wind speed and surface roughness. Over oceans, nitric acid deposition velocities vary spatially between 0.3 to 1.3 cm s- 1 but, as for SO2 and sulfate, have little diurnal variability. Calculated HNO 3 deposition velocities presented for the central U.S. in Fig. 8(b) agree favorably with the observations of Hueben and Robert (1985) who found the daytime average deposition velocity at a height of 1 m over grass to be near 2.5 cm s- t Figures 9-10 show time variations of deposition

velocity at individual points (grid cell averages) along with the associated meteorological factors we use to compute the deposition velocity. Areas represented by each of these plots are shown as shaded boxes on Fig. 1(a~ Figure 9 corresponds to the area in upstate New York that was used in the previous sensitivity analysis. This area primarily contains deciduous forest, lakes, and agricultural ianduse types. Figure I0 shows the results for an oceanic point. Both sets of figures indicate that a correlation exists between friction velocity and deposition velocity. Over each surface,

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TIME (h) Fig. 9. (it) H o u r l y meteorological conditions, and (b) S02, S04, and HNO~ deposition velocities at approximately 40 m above the surface averaged over an area in upstate

New York for the time period 22-24 April 198L Shaded areas denote nights.

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Fig. 10. Same as Fi& 9 except over an ocean area.

SO,, sulfate and HN03 deposition velocities friction velocity itself is roughly prol:ortional to wind speed, with some modulation by the temperature difference. Differences in HI~O3 deposition velocity for ocean as compared to land areas can be attributed in large part to differences in surface roughness, while time-varying resistances for land surfaces induce a strong diurnal variability in SO t deposition velocity that is less evident over ocean regions, for which the surface resistance is always zero. Figure 11 shows domain.averaged deposition velocities and associated atmospheric variables for 22-24 April 1981. The daily oscillation of stability is apparent in the temperature difference plot. For this 3-day period, the domain averaged mid-day SOt, sulfate and HNO3 deposition velocities were computed to be 0.8, 0.2 and 2.5 eros -l, respectively, at a 40-m height. Domain-averaged deposition velocities at night were computed to be approximately 0.35, 0.09, and 1.7 em s - ~. When averaged over the entire domain, one can see that HNO3 deposition velocities are highly correlated with friction velocity, while sulfate de-

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961

position velocities are more negatively correlated with temperature difference. The large diurnal cycle in SO, deposition velocities is due in part to meteorological factors, although most of the variation can be attributed to changes in surface resistance. Under conditions when the aerodynamic resistance dominates (HNO3 deposition, or SOt deposition to water), strong vertical gradients of pollutant concentration can exist. Since the flux of pollutant remains roughly constant near a surface, the deposition velocity will decrease with height. Measurements of deposition are usually made between I and 10 m above a surface, which is lower than the height at which deposition velocities are calculated in this study. Under neutral conditions with a typical roughness of 1 cm, the velocity estimates presented in this study can be scaled to a 5-m height by adjusting them up by approximately 30 ~. Scaling to a height of I m requires about a 75% increase, although these factors vary depending on the stability and surface roughness. This correction would not apply to conditions when the surface resistance to deposition is significant, as is the case for sulfate deposition or SO, deposition to most land surfaces.

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The parameterizations used in the preceding maps of deposition velocities were developed from several field programs designed to monitor dry deposition of trace gases and particles in the absence of rainfall. Rarely are dry deposition measurements performed during rainy periods, but the surface resistance of surfaces thoroughly wetted is expected to be very small, just as for open water. Based on a number of measurements of SO 2 deposition to agriculture crops, for example, Fowler (1978) has suggested that when foliage is wet with rain or dew, SO, surface resistance is negligible. During the three day period analyzed in this exercise, an appreciable amount of rain fell as a fairly intense mid=latitude cyclone swept across the domain, thus making local land surfaces wet. In the following exercise, the SO, surface resistance is set to zero during any hour in which rainfall is predicted to occur within a grid area. This approximation is obviously too simple, but is useful as a parameterization to evaluate the effect that rainfall may have on SO, dry deposition. Figure 12 shows SO t deposition velocities as a function of time at the upstate New York area with and without rainfall=wetted surfaces. One can see that rain began at the end of the second day of the model simulation, and persisted throughout most of the third day. During the third day, SO, deposition velocities were predicted to approach the H N O 3 deposition velocities, since the surface resistance was set to zero. During rainy periods, SO, deposition velocities in excess of 2 cm s- t are not uncommon at this location, where significant surface roughness b present. Figure 13 shows SO, deposition velocities averaged over the entire domain with and without rainfall wetted sur-

962

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Fig. 12. SO= deposition velocity computed during the period 22-24 April 1981 averaged over an (80 kin)= area in upstate New York. Solid curve computed deposition velocity without lecounting to for rainfalL Dashed curve allows surfaceresistanceto vanish when precipitation wets surface,

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Fig. 13. Deposition velocities averaged over model domain for the period 22-24 April 1981. Dashed curve corresponds to SO: deposition velocity without rainfall effect included. Dotted curve includes rain-wetted surface effect. Solid curve is HNO3 depotifion velocity, which represents upper limit to SO= deposition velocity. faces. One can see that average SO= deposition vei• ocities increase to 1.0-1.6 cm s- t during the day, and at night, domain-averaged deposition velocities are between 0.3 and 1.0 em s-t. For this 3-day simulation, SO= dry deposition velocities are significantly enhanced, primarily because this simulation time was chosen to include the formation and development of a mid-latitude cyclone, and thus represents a relatively rainy period. Figure 14 shows maps of SO= dry deposition velocities averaged for two nights between 04 and 08 G M T with and without rainfall effects included. Rainfall occurring at night would have a very pronounced effect on SOa deposition velocities since surface resistances are highest at night when surfaces

A method for computing deposition velocity maps using realistic, fine resolution meteorological and landuse distributions has been outlined. Using this scheme, hourly deposition velocities of SO2, SO~- and H N O 3 were generated for a 3-day period during spring over the eastern United States, southeastern Canada, and surrounding oceanic regions. Deposition velocities were evaluated at a height of 40 m, corresponding to the lowest layer of the mesoscale meteorology model. Deposition velocities were found to have a strong diurnal variability, especially over land areas. Domain averaged mid-day SO2, sulfate and HNO3 deposition velocities were found to be 0,8 c m s - : , 0.2 cm s- t and 2.5 cm s - :, respectively. At night, the deposition velocities were approximately 50, 45 and 70% of the corresponding daytime values. These estimates of deposition velocity should not be taken as accurate climatological averages, however, because the simulation was limited to only a few days. In areas where there is significant variation in land type within an averaging area, it was found that subgrid scale meteorological variations over the different land types can produce significant changes in computed average deposition velocities. In these situations, it appears that assuming constant uu. within the averaging area can realistically address the subgrid variability in wind speed in friction velocity. On the other hand, a popular assumption that the wind speed is constant over all land types in an averaging area appears to produce questionable results over areas when the roughness is significantly different than the average roughness. Two fields of SO= deposition velocities were compared. In one, rainfall was not allowed to enhance SO~ deposition velocities, while in a separate run, the effect of rainfall wetted surfaces was simulated. The scheme presented here for treating rain-wetted surfaces, although simple, suggests that dry deposition can be significantly enhanced when it is occurring over wetted surfaces. The somewhat high deposition velocities predicted during rainy periods by this model might overpredict sulfur deposition, since surfaces might shed water or dry quickly by evaporation and thus prevent surface resistances from decreasing to zero. In addition, actual dry deposition fluxes of: pollutant to wetted surfaces during raining periods will be somewhat reduced since air concentrations may be lowered due to washout of pollutant by the rainfall. There are limits to the range over which the parameterizations used in this study are applicable. These parameterizations are based on relatively sparse deposition measurements to homogeneous surfaces.

SO2, sulfate and HNOa deposition velocities

.

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For sulfate, for example, measurements have not been conducted in the field over smooth surfaces; all estimates of sulfate deposition velocity over water should thus be viewed with extreme caution. Considerable variability of land types does occur on a relatively small scale, and during certain periods, significant amounts of rainfall can cover large areas. There is a definite need to measure dry deposition in areas where land type is varying significantly within a given area, and thus verify the model results for such areas. In addition, more detailed dry deposition measurements are needed during periods when surfaces are wetted by rainfall or dew. These results suggest that considerable variability in deposition velocities can occur in both time and space. Long-range transport models must account for this if they are to predict deposition patterns accurately. These methods can be applied to model the deposition of other pollutants which may be of importance in modeling acid deposition. Unfortunately, extensive measurements of species-specific surface resistances would be required before general parameterizations could be derived. The techniques for computing regional deposition velocity maps are readily adaptable for use in Eulerian grid models of atmospheric pollutant transport and deposition, as long as sufficient ianduse and meteorological information is available. For the application outlined here, meteorological data were generated using a mesoscale model, although observational data could be incorporated into this modeling approach. Acknow~lgement--The authors gratefully acknowledge the concise and helpful comments ofGreg McRac, whose insights proved invaluable during the development of this paper.

Discla~.,r--Although the research described in this article has been funded as part of the National Acid Precipitation Assessment Program by the U.S. Environmental Protection Agencyunder Interagency Agreement DW930144-01.I to the National Center for Atmospheric Research, and under Interagency Agreement DW8993(X)60-01 to the U.S. Department of Energy, the results have not been subject to the Agency's required peer and policyreview and therefore do not necessarily reflect the views of the Agency and no official endorsement should be inferred. REFERENCES

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Haugen D. A.), pp. 217-270. American Meteorological Society, Boston, MA. Fowler D. (1978) Dry deposition of SO= on agricultural crops. Atmospheric Environment 12, 369-373. Galloway J. N. and Likens G. E. (198!) Acid precipitation: the importance of nitric acid. Atmospheric Environment 15, 1081-I097. Garland J. A. (1978) Dry and wet removal of sulfur from the atmosphere. Atmospheric Environment 12, 349-362. Hicks B. B. and Liss P. S. (1976) Transfer of SO~ and other reactive gases across the air-sea interface. Tellus 28, 348-3M. Hicks B. B. and Wesely M. L. (1981) Heat and momentum characteristics of adjacent fields of soybeans and maize. Boundary-Layer Met. 20, 175--185. Hicks B. B., Wesely M. L., Durham J. L. and Brown M. A. (1982) Some direct measurements of atmospheric sulfur fluxes over a pine plantation. Atmospheric Environment 16, 2899-2903. Holzworth G. C. (1967) Mixing depths, wind speeds and air pollution potential for selected locations in the United States. ,/. appl. Met. 6, 1039-1044. Huehert B. J. and Robert C. H. (1985) The dry deposition of nitric acid to grass../, geophys. Res. 90, 2085-2090. Kuo Y.-H. and Anthes R. A. (1984) Accuracy of diagnostic heat and moisture budgets using SESAME-79 field data as revealed by observing system simulation experiments. Mon. Wea Rev. ! 12, 1465-1481. Kuo Y.-H., Skumanich M., Haagenson P. L. and Chang J. S. (1984) The accuracy glair parcel trajectories as revealed by observing systemsimulation experiments. In Fourth ,Joint

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Masse C. and Voldner E. C. {1983) Estimation of dry deposition velocities of sulfur over Canada and United States east of the Rocky mountains. Precipitation Scavenging, Dry Deposition, and Resmpension, Vol. 2 (edited by Pruppacher H. R., Semonin R. G. and Slinn W. G.), pp. 991-1002. Elsevier, New York. McMahon T. A., and Denison P. J. (1979) Empirical atmospheric deposition parameters--a survey. Atmospheric Environment 15, 571-585. Panofsky H. A. and Dutton J. A. (1984) Atmospheric

Turbulence, Models and Methods for Engineering Applications. John Wiley, New York. Sheih C. M. (1977) Application of a statistical trajectory model to the simulation of sulfur pollution over northeastern United States. Atmospheric Environment !!, 173-178. Sheih C. M., Wesely M L. and Hicks B. B. (1979) Estimated dry deposition velocities of sulfur over the eastern United States and surrounding regions. Atmospheric Environment 13, 1361-1368. Tennekes H. (1973) Similarity laws and relations in planetary boundary layers. Workshop on Micrometeorology (edited by Haugen D. A.), pp. 177-216. American Meteorological Society. Boston, MA. Wesely M. L., Cook D. R., Hart R. L. and Spser R. E. (1985) Measurements and parameterization of particulate sulfur dry deposition over grass. J. geophys. Res. 90, 2131-2143. Wesely M. L. and Hicks B. B. (1977) Some factors that affect the deposition rates of sulfur dioxide and similar gases on vegetation. J. Air Pollut. Control Ass. 27, 11 I0-I 116. Wyngaard J. C. (1973) On surface layer turbulence. Workshop on Micrometeorology (edited by Haugen D. A.), pp. 101-149. American Meteorological Society, Boston, MA. Zhang D. and Anthes R. A. {1982)A high resolution model of the planetary boundary layer-sensitivity tests and com. parisons with SESAME.79 data. J. appI. Met. 21, 1594.-1609.