On the influence of surface heterogeneity on latent heat ...directory.umm.ac.id/Data...

Ž .Atmospheric Research 54 2000 59–85www.elsevier.comrlocateratmos

On the influence of surface heterogeneity on latentheat fluxes and stratus properties

Katja Friedrich 1, Nicole Molders)¨LIM-Institut fur Meteorologie, UniÕersitat Leipzig, Stephanstraße 3, D-04103 Leipzig, Germany¨ ¨

Received 26 August 1999; received in revised form 16 November 1999; accepted 6 December 1999

Abstract

A mesoscale atmospheric model is used to examine the three-dimensional structure andevolution of low extended stratus over various synthetic landscapes of different heterogeneity inmid-latitudes in spring. The simulation results substantiate that surface heterogeneity nonlinearlyinfluences the distributions of latent heat fluxes, vertical motions, and cloud-water presupposedthe length of the patches of equal surface type is about 10 km or larger than that. For low degrees

Ž .of heterogeneity large patch sizes , a great coverage by lowly evapotranspiring, but stronglyheating patches may enhance vertical motion. Moreover, this constellation may increase thecloud-water amount of low extended stratus as compared to that of the other heterogeneouslandscapes or that with the highest domain-averaged daily sum of latent heat fluxes. Althoughthere exists a relationship between the degree of heterogeneity and the modulation of latent heatfluxes as well as cloud-water amount, the kind of surface characteristics is also important for themodulation of the properties of low extended stratus. q 2000 Elsevier Science B.V. All rightsreserved.

Keywords: Degree of heterogeneity; Latent heat fluxes; Low extended stratus; Mesoscale modeling; Surfaceatmosphere interaction

1. Introduction

Flying over a landscape in mid-latitudes presents a fantastic view of patchy fieldswith various surface properties and different sizes. Obviously, this surface heterogeneity

) Corresponding author. Tel.: q49-341-9732-872; fax: q49-341-9732-899.Ž .E-mail address: [email protected] N. Molders .¨

1 Present affiliation: DLR, Institut fur Physik der Atmosphare, Oberpfaffenhofen, Postfach 1116, 82230¨ ¨Wessling, Germany.

0169-8095r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved.Ž .PII: S0169-8095 99 00050-2

( )K. Friedrich, N. MoldersrAtmospheric Research 54 2000 59–85¨60

can be significant at the mesoscale or global scale. The varying nature and structure ofland-surface result in different fluxes of momentum, water vapor, matter, and heat due todifferences in water availability, surface temperature, plant and soil characteristics as

Ž .well as hill slopes e.g., Li and Avissar, 1994 . Thus, the meteorological processesŽ .taking place in the atmospheric boundary layer ABL and at the interface earth–atmo-

sphere are, among others, governed by surface characteristics and surface discontinu-ities. This impact is exacerbated, for instance, by moistening of the ABL through

Ž .evapotranspiration, the rising of lighter, moist air as compared with dry air , and theadditional ascending motion induced by surface thermal heterogeneity. Therefore, it hasto be expected that the degree of heterogeneity may affect the water and energy fluxesas well as cloud formation.

The impact of surface characteristics and discontinuities on the ABL was investigatedŽin many theoretical and numerical studies as well as field experiments e.g., Anthes,

1984; Pinty et al., 1989; Mahrt et al., 1994; Zhong and Doran, 1995; Molders and¨.Raabe, 1996 . Investigating interactions between land cover and cloud cover by means

Ž .of GOES satellite data on a 18=18 grid, O’Neal 1996 hypothesized that it could bepossible to define a measure denoted as ‘‘degree of surface heterogeneity’’ within largerareas to test whether areas with greater land-surface heterogeneity have significantly lessor larger cloud cover. The intensity of thermally induced mesoscale circulations betweenvegetated and bare soil areas — so-called vegetation breezes — was found to be

Ždirectly related to the characteristics of the bare soil and surface fluxes Mahrt et al.,.1994; Hong et al., 1995 . In general, upward motion in such mesoscale circulations is

Ž .stronger than thermal cells induced by turbulence e.g., Seth and Giorgi, 1996 . Theirability to transport moist, warm air upward increases the amount of water that can becondensed and precipitated. In a relatively dry atmosphere, clouds and precipitation

Žappear to be randomly distributed when the domain is homogeneous e.g., Avissar and.Liu, 1996; Seth and Giorgi, 1996 . However, when the landscape structure triggers the

formation of mesoscale circulations, they concentrate on the originally dry parts of thedomain. A negative feedback is created, which tends to eliminate the effect of the

Žlandscape discontinuities and spatially homogenize soil moisture content e.g., Avissar.and Liu, 1996 .

Most studies on the atmospheric impact of land-surface heterogeneity were carriedŽout for arid or semiarid regions and convective precipitating clouds e.g., Anthes, 1984;

.Mahrt et al., 1994; Zhong and Doran, 1995 . In these regions, it is of interest, forinstance, for water management, irrigation purposes or limitations of grazing, whetherthere exist land-use pattern distributions that favor cloud formation. Even in highmid-latitudes, where, however, the atmosphere is usually relatively humid, the analysesof aircraft data show that moisture variability is likely to have an impact on relative

Ž .humidity variations e.g., Frech et al., 1998 . Therefore, it has to be expected that, here,cloudiness may be modulated by the underlying surface too. As a consequence of therelatively humid atmosphere in mid-latitudes, however, low extended stratus frequentlyoccurs in the boundary layer under anticyclonic conditions, especially in spring, autumn,and winter. Thus, the aim of this paper is to examine how surface heterogeneity mayaffect latent heat fluxes, vertical motions as well as the properties of low extendedstratus. These investigations are of main interest for environmental questions. Namely,

( )K. Friedrich, N. MoldersrAtmospheric Research 54 2000 59–85¨ 61

Žin mid-latitudes, land-surface conditions are anthropogenically altered e.g., throughurbanization, deforestation and afforestation, subsidy politics, open-pit mining and

.recultivation of open-pit mines, etc. . These land-use changes go along with modifica-tions of surface heterogeneity. Since low extended stratus may significantly alter

Ž .photolysis rates Molders et al., 1995 as well as evapotranspiration, and, thus, ground-¨Žwater recharge, land-use changes may not only affect the water and energy cycle e.g.,

.Molders, 1998 , but also the trace gas concentrations. Moreover, since in mid-latitudes,¨extended low stratus is often supercooled, here, land-use changes that contribute toenhance stratus should be avoided in areas of airports due to the danger of icing.

2. Model description and initialization

The nonhydrostatic meteorological model GEesthacht’s SImulation Model of theŽ .Atmosphere GESIMA, Kapitza and Eppel, 1992; Eppel et al., 1995 is used in our

study. Its dynamical part is based on the anelastic equations.The physical features of the cloud module are based upon a five water-class

Ž .cloud-parameterization scheme Molders et al., 1997 . In this scheme, saturation adjust-¨Ž .ment follows Lord et al. 1984 . Note that, in the case of low extended stratus, as

investigated in our study, condensation and evaporation of cloud-water are the cloud-mi-crophysical processes of most importance.

In the long-wave spectral range, the radiative transfer equation is solved in asimplified two-stream approximation that transforms the radiation flux into an upward

Ž .and downward one Eppel et al., 1995 . These fluxes are coupled by their values at theupper and lower boundaries. To get reliable upper model boundary fluxes, 10 additionalmodel layers are added at the top of the computational domain of the model. The meanspectral heating is calculated by the divergence of the net long-wave radiation fluxŽ . Ž .Eppel et al., 1995 . The spectral extinction coefficients depend on pressure height and

Ž .temperature. In accord with Buykov and Khvorostyanov 1977 , a wavelength-indepen-dent value is assumed for the extinction coefficients of liquid water. Outside the windowregion, water vapor and liquid-water absorption are taken into account. The transmissionis approximated by a sum of exponential terms adjusted to the results of a statistical

Ž .band model for more details, see Eppel et al., 1995 . In the short-wave spectral range,only scattering processes are considered, leading to a simple parameterization of the

Ž .solar flux at the surface see Claussen, 1988; Eppel et al., 1995 . In the case of clouds,Ž .the transmission function is formulated in accord with Stephens 1978 . In doing so, the

optical thickness of a cloud is considered as a function of liquid-water path byŽintegrating the cloud substance densities which are predicted by the cloud-parameteriza-

.tion scheme from the surface to model top.Ž .The treatment of the soilrvegetationratmosphere interaction follows Deardorff 1978

Ž .see also Eppel et al., 1995; Molders, 1998 . Herein, homogeneous soil- and land-surface¨characteristics are assumed within a grid cell. A force-restore method determinessoil-wetness factors. At the surface, the fluxes of sensible and latent heat are calculatedapplying a bulk formulation. Transpiration of plants is considered by a Jarvis-type

Ž .approach Jarvis, 1976 . The soil heat fluxes and soil temperatures are determined by a

()

K.F

riedrich,N.M

oldersrA

tmospheric

Research

542000

59–

85¨

62

Table 1Plant- and soil-specific parameters as used in this study

Surface Thermal Heat Emissivity Albedo Roughness Field Capillary Max. evaporativey3 3Ž .type diffusivity capacity length capacity 10 kgrm s conductivity

y6 2 6 3Ž . Ž . Ž . Ž . Ž .10 m rs 10 Jrm K m m mrs

Grass 0.56 2.1 0.95 0.25 0.02 0.010 8.0 0.04Sand 0.84 2.1 0.90 0.3 0.0004 0.002 0.9 –


Ž .diffusion equation Claussen, 1988; Eppel et al., 1995 where, at 1-m depth, soiltemperature is held constant at the climatological value. The plant- and soil-specificparameters used in this study are listed in Table 1. The surface stress and near-surfacefluxes of heat and water vapor are expressed in terms of dimensionless drag-and-transfer

Ž .coefficients utilizing a parametric model Kramm et al., 1995 .The turbulent flux of momentum for the region above the surface layer is calculated

by a one-and-a-half-order closure scheme. The elements of the eddy-diffusivity tensorare expressed by the vertical eddy diffusivity, K , and horizontal diffusivity, K .M,V M,H

The latter is also related to K by the simple linear relationship, K s2.3K .M,V M,H M,VŽ .K is expressed by the turbulent kinetic energy TKE and mixing length, l, using theM,V

Kolmogorov–Prandtl relation where the mixing length is parameterized by Blackadar’sŽ . Ž .1962 approach, slightly modified by Mellor and Yamada 1974 . The turbulent fluxesof sensible heat and water vapor for that region are expressed as functions of K andM,V

the turbulent Prandtl number, Pr sK rK , and turbulent Schmidt number, Sc st M,V H,V t

K rK , respectively. These characteristic numbers depend on the thermal stratifica-M,V E,VŽ .tion. They are derived from the local stability functions of Businger et al. 1971 and the

assumption that Sc sPr . To determine the TKE, an additional budget equation for thatt tŽquantity is solved, where the energy production due to horizontal shear is neglected for

.more detail, see Kapitza and Eppel, 1992 .The model is initialized using profiles of air temperature and humidity typical for a

Ž .day with extended low stratus in spring Fig. 1 . Surface pressure is 1031.2 hPa. In thecalculation of radiation, a geographical latitude of 51.58N and the 15 May are assumed.Initial soil wetness factor is set equal to 0.5. Soil temperature of 1-m depth is set equalto 280.1 K.

The simulations are integrated for 24 h where the first 6 h serve as the adjustingŽ . 2phase. The inner model domain s test domain encompasses 75=75 km with a

horizontal grid resolution of 5=5 km2. The vertical resolution varies from 20 m close

Ž . ŽFig. 1. Initial profiles of specific humidity q in grkg, u- and Õ-component of wind vector in mrs upperv. Ž . Ž .x-axis , and air temperature T in 8C lower x-axis .


to the ground to 1 km at the top, which is located in 12-km height. Eight levels arelocated below the 2-km height and nine are above. Homogeneously flat terrain isassumed for all simulations.

3. Design of the study

Two simulations are performed assuming alternatively a homogeneous cover by sandŽ .and grass grown on loamy soil Fig. 2 . These simulations and their results are denoted

as HOMS and HOMG, hereafter. Furthermore, sixteen simulations are performed forŽ .different patterns of sand and grass Fig. 2 . These simulations and their results are

addressed according to the coverage by sand, S, and grass, G, their pattern, and thesmallest length of patches given in km. The letters SGS, for instance, stand for adominance by sand, while GSG stands for a dominance by grass. The patches arrangedas stripes parallel or perpendicular to the wind, in form of a checkerboard or a cross are

Ž .denoted as P, R, C, and X, respectively see Fig. 2 .In the following discussion, simulation results obtained by assuming the aforemen-

tioned landscapes are compared with each other. In doing so, the influence of surfacepattern on the water vapor supply to the ABL and on cloud formation as well as theinteraction between cloudiness and evapotranspiration are elucidated.

To examine the influence of surface heterogeneity on latent heat fluxes, verticalmotion, and the properties of low extended stratus, the degree of surface heterogeneity is

Ž .defined by Molders, 1999 :¨dsFrF 1Ž .max

for the inner model domain. This measure considers the total length of boundaries, F ,between areas of different surface types in the domain of interest. In the case of a

Ž .maximum degree of heterogeneity ds1 , each grid cell is also the boundary to anothersurface type. The total length of the boundary equals F . In the case of homogeneity,max

Ž .there exists only one surface type and no boundary ds0 . The degree of heterogeneityas obtained by this measure is listed in Table 2 for the various landscapes.

4. The latent heat fluxes

During the day, the entire domain is totally covered by low-level stratus in allsimulations. Since clouds reduce insolation, the turbulent moisture and heat fluxes areweak. At noon, the homogeneously grass-covered domain, for instance, provides a latentheat flux of 46.4 Wrm2 over the entire domain, while, at the same time, thehomogeneously sand-covered domain provides a latent heat flux of 39.6 Wrm2 over the

Ž .entire domain Table 2 . The domain-averaged latent heat fluxes of the simulations withŽ .heterogeneous surfaces fall between these values Table 2 . Generally, grass-patches

provide greater amounts of water vapor to the ABL than sand-patches. Nevertheless, thedaily sum of the latent heat fluxes does not correlate to the coverage by grass or the

Ž .degree of heterogeneity Table 2 .


Fig. 2. Schematic view of the land-use distributions applied in the numerical experiments.

Because of identical initial meteorological conditions, the distribution patterns oflatent heat fluxes differ only due to the different land-use distribution. Primary differ-


Table 2Daily sums of domain-averaged latent heat fluxes, ÝL E, domain-averaged latent heat flux at 1300 LT, ÝL Ev vŽ .13 , coverage by grass, C, size of the largest grass-covered patch, A, and degree of heterogeneity, d , for allsimulations

2 2 2Ž . Ž . Ž . Ž . Ž .Simulation ÝL E Wrm ÝL E 13 Wrm C % A km dv v

HOMG 404.6 46.4 100.0 5625 0.00SGSX25 396.9 41.7 44.4 625 0.16SGSC25 386.1 43.2 44.4 625 0.20GSGP25 379.3 43.7 66.7 1875 0.13SGSP25 378.5 42.8 33.3 1875 0.13GSGR25 373.6 43.7 66.7 1875 0.13SGSR5 370.1 43.3 46.7 375 0.53GSGP5 368.9 43.1 53.3 375 0.53GSGC25 368.6 42.6 55.6 625 0.20SGSP5 368.4 43.1 46.7 375 0.53GSGC5 366.1 42.7 50.2 25 1.00SGSC10 366.0 42.5 48.0 150 0.47GSGC10 361.8 42.8 52.0 150 0.47GSGX25 358.9 44.4 55.6 3125 0.16SGSC5 353.0 42.2 49.8 25 1.00SGSR25 345.3 41.8 33.3 1875 0.13GSGR5 343.9 41.7 53.3 375 0.53HOMS 316.6 39.6 0.0 0 0.00

ences in water-vapor supply result from the different surface characteristics of grass andŽ . Ž .sand Tables 1, 2 and different surface arrangements Fig. 2 . Here, the different

albedo, for instance, leads to differences in net radiation. Hence, incoming energy isdifferently partitioned into the fluxes of sensible and latent heat. Secondary differencesresult from the modified micrometeorological condition that again affects the heatfluxes. Thus, water supply and moisture transport into the ABL differ too. Therefore,with progressing integration time, further differences may arise from the altered thermalstratification, cloudiness, net radiation reaching the surface and, hence, modified evapo-transpiration, as well as due to differences in the advection of momentum, heat, andmoisture. Moreover, at cloud top, the different cloud-water amount leads to changes inŽ .long-wave radiative cooling that again slightly affect the microphysics of the stratus.Although at the tops of low extended stratus, the impact of surface heterogeneity onradiative cooling may be of some importance, for brevity, this article is limited to thediscussion of the influence of surface heterogeneity on latent heat fluxes, verticalmotions, and cloud-water amount.

4.1. Distribution pattern of latent heat fluxes

Surface heterogeneity influences the near-surface atmosphere and flow by the alteredŽsurface characteristics e.g., albedo, roughness length, emissivity, evaporative conductiv-

.ity, etc. . Since the atmospheric moisture and temperature states try to achieve anequilibrium with the respective underlying surface, the micrometeorological conditions


Ž .e.g., near-surface wind, near-surface temperature and humidity, etc. are modified byfluxes when ever a parcel passes a change in the underlying surface. Thus, after passingseveral alternating patches of grass and sand, the micrometeorological condition over agrass-patch located in the western part of the domain, for instance, slightly differ fromthose over grass in the eastern part of the domain because of the frequent modulation ofthe air mass when ever passing a discontinuity. These differences grow with time andwith increasing distance from the first change in the underlying surface. The alteredmicrometeorological properties again modify the sensible and latent heat fluxes. Themagnitude to which a surface may coin the air mass depends on the time it rests abovethe patch and, hence, on the patch size. Thus, if the patch size falls below 10=10 km2

like for SGSR5, GSGR5, SGSP5, GSGP5, SGSC5, and GSGC5, respectively, the fluxdistribution shows hardly an organized response to the underlying surface. Therefore,the results of these studies are not discussed explicitly. For patch sizes larger than 10-kmside-length, for instance, in GSGC25, each patch evokes a discernible and assignable

Ž .own response. These findings broadly agree with Shuttleworth’s 1988 theoreticalconsiderations.

4.1.1. SGSX25 and GSGX25Ž .Rotation of crops may cause differences in surface distribution like SGSX25 Fig. 3

Ž . Žand GSGX25 Fig. 4 , for instance. Juxtaposing the results obtained for 12 LT local.time by simulations with same degree of surface heterogeneity, and patch arrangement,

Ž .but inverted distribution of grass and sand e.g., SGSX25 and GSGX25, Figs. 3, 4Žshows that landscapes with large connected grass-patches in this example the cross, Fig.

. Ž .4 and small isolated sand-patches here the edges of the inner domain may supplymore water vapor to the ABL than those of opposite arrangement of grass and sand. In

Fig. 3. Distribution of latent heat-fluxes in Wrm2 at 12 LT as simulated by SGSX25. Grey patches indicategrass and light grey patches indicate sand, respectively.


Fig. 4. Like Fig. 3, but for GSGX25.

GSGX25 and SGSX25, the surface characteristics of the cross-area govern the distribu-tion and magnitude of heat and moisture exchange. At noon, for instance, the maximum

2 Ž .latent heat flux of SGSX25 amounts more than 46 Wrm over grass Fig. 3 compared2 Ž .with more than 50 Wrm over grass in GSGX25 Fig. 4 .

4.1.2. GSGC25 and GSGX25Comparing the results of simulations with different patch sizes and degree of

heterogeneity, but same fractional coverage of grass shows that large connected grass-Žpatches may provide higher latent heat fluxes than isolated grass-patches e.g., compare

.GSGX25 and GSGC25, Figs. 4, 5 . The latent heat fluxes of the grass-patch located inŽ . 2the center in GSGC25 Fig. 5 , for instance, amounts less than 46 Wrm , while, in

GSGX25, here, locally more than 50 Wm2 are achieved. Thus, one may conclude thatthe mean water vapor supply to the ABL by one patch, among others, depends on itssize. These findings mean that the surface characteristic of the largest connected part, inthe case of GSGX25, the grass-cross, does not only dominate the latent heat-fluxdistribution, but also affects the fluxes of the downwind neighbored patches.

Note that in the checkerboard arrangement of GSGC25, the grass-patches provideŽ .similar fluxes than those in SGSX25 compare Figs. 3, 5 .

4.1.3. GSGC25 and SGSC10GSGC25 and SGSC10 are landscapes of different degrees of surface heterogeneity,

Ž .and different coverage by grass, but similar patch pattern Table 2, Fig. 2 . At noon, forinstance, the latent heat fluxes of GSGC25 and SGSC10 range from 32 Wrm2 to 48

2 Ž .Wrm in both simulations e.g., Fig. 5 . Like in GSGC25, in SGSC10, the latent heatŽ .flux distribution reflects the surface patches not shown . Consequently, in SGSC10, the

latent heat-flux distribution is more heterogeneous than in GSGC25.


Fig. 5. Like Fig. 3, but for GSGC25.

4.2. Daily domain aÕerages

The largest differentials in the daily sums of the domain-averaged latent heat fluxesŽ 2 . Ž .88 Wrm occur between the results of HOMG and HOMS Table 3 . The domain-averaged latent heat fluxes of all simulations assuming heterogeneous surfaces range

Ž .between these two values see also Table 2 . The greatest differential between simula-tions with heterogeneous surface-cover amounts 53 Wrm2 for SGSX25 and GSGR5.On the contrary, the daily sums of domain-averaged latent heat fluxes hardly differ forthe following pairs: GSGP5 and GSGC25, GSGP25 and SGSP25, as well as GSGC5

Ž .and SGSC10 Table 3 . The daily sums of the domain-averaged latent heat fluxes do notalways grow with increasing coverage by grass, size of the grass-covered patches or

Ž .degree of heterogeneity Tables 2, 3 .ŽThe greatest deviations arise between HOMS and nearly all other simulations Table

.3 . In this homogeneously dry and warm, sandy domain, the incoming energy ispartitioned toward higher sensible and lower latent heat fluxes than in the partlygrass-covered domains or than in the totally grass-covered domain. Even for smallfractional coverage by grass, the latent heat flux increases rapidly as compared to

Ž .HOMS Table 3 . Out of all the simulations assuming heterogeneous landscapes,SGSX25 provides the greatest daily sums of the domain-averaged latent heat fluxesŽ 2 . Ž .396.9 Wrm , although it has not the largest amount of grass Table 2 . This behaviormay be explained by the oasis effect. The slightly warmer air, due to the strongerheating of sand than of grass, enhances evapotranspiration. In contrast to SGSX25, thedaily sum of domain-averaged latent heat fluxes is smaller for the ‘‘inverse landscape’’

Ž .GSGX25 which is dominated by grass because the greater grass-coverage of GSGX25leads to a slightly cooler lower ABL than for SGSX25.


Table 3Ž . ŽDaily sums of the domain-averaged latent heat flux upper values and cloud-water mixing ratio lower values

. Ž .in brackets . Deviations values vertical minus horizontal line are given in the upper and lower triangle of thetable for latent heat fluxes and cloud-water, respectively. Note that fluxes and mixing ratios are rounded forclarity of table

2Wrm HOMG SGSX25 SGSC25 GSGP25 SGSP25 GSGR25 SGSR5 GSGP5grkg

HOMG 405 y8 y19 y26 y26 y31 y35 y36Ž .12.6

SGSX25 y0.7 397 y11 y18 y19 y23 y27 y28Ž .13.3

SGSC25 y0.7 0 386 y7 y7 y12 y16 y17Ž .13.3

GSGP25 0.1 0.8 0.8 379 0 y5 y9 y10Ž .12.5

SGSP25 y0.7 0 0 y0.8 379 y5 y8 y10Ž .13.3

GSGR25 0 0.7 0.7 y0.1 0.7 374 y4 y5Ž .12.6

SGSR5 0 0.7 0.7 y0.1 0.7 0 370 y1Ž .12.6

GSGP5 0 0.7 0.7 y0.1 0.7 0 0 369Ž .12.6

GSGC25 0.1 0.8 0.8 0 0.8 0.1 0.1 0.1

SGSP5 0 0.7 0.7 y0.1 0.7 0 0 0

GSGC5 0 0.7 0.7 y0.1 0.7 0 0 0

SGSC10 0.3 1 1 0.2 1 0.3 0.3 0.3

GSGC10 0 0.7 0.7 y0.1 0.7 0 0 0

GSGX25 0.1 0.8 0.8 0 0.8 0.1 0.1 0.1

SGSC5 y0.1 0.6 0.6 y0.2 0.6 y0.1 y0.1 y0.1

SGSR25 0.1 0.8 0.8 0 0.8 0.1 0.1 0.1

GSGR5 0 0.7 0.7 y0.1 0.7 0 0 0

HOMS 0.3 1 1 0.2 1 0.3 0.3 0.3

Looking at the simulations with checkerboard-like landscapes, in the sand-majorizedŽ .landscapes with large patch sizes e.g., SGSC25, SGSC10 , the daily sums of domain-

averaged latent heat fluxes exceed those of their grass-majorized counter-pairs like inŽ .SGSX25 e.g., GSGC25, GSGC10 . Here, SGSC10 and GSGC5 provide the same sum

Ž 2 .366 Wrm ; Table 3 . Out of the checkerboard-arranged landscapes, SGSC5 providesŽ 2 .the smallest daily sum of domain-averaged latent heat fluxes 353 Wrm ; Table 3 . In


GSGC25 SGSP5 GSGC5 SGSC10 GSGC10 GSGX25 SGSC5 SGSR25 GSGR5 HOMS

y36 y37 y39 y39 y43 y46 y52 y60 y61 y88

y28 y29 y31 y31 y35 y38 y44 y52 y53 y80

y17 y18 y20 y20 y24 y27 y33 y41 y42 y69

y10 y11 y13 y13 y17 y20 y26 y34 y35 y62

y10 y11 y13 y13 y17 y20 y26 y34 y35 y62

y5 y6 y8 y8 y12 y15 y21 y29 y30 y57

y1 y2 y4 y4 y8 y11 y17 y25 y26 y53

0 y1 y3 y3 y7 y10 y16 y24 y25 y52

369 y1 y3 y3 y7 y10 y16 y24 y25 y52Ž .12.5y0.1 368 y2 y2 y6 y9 y15 y23 y24 y51

Ž .12.6y0.1 0 366 0 y4 y7 y13 y21 y22 y49

Ž .12.60.2 0.3 0.3 366 y4 y7 y13 y21 y22 y49

Ž .12.3y0.1 0 0 y0.3 362 y3 y9 y17 y18 y45

Ž .12.60 0.1 0.1 y0.2 0.1 359 y6 y14 y15 y42

Ž .12.5y0.2 y0.1 y0.1 y0.4 y0.1 y0.2 353 y8 y9 y36

Ž .12.70 0.1 0.1 y0.2 0.1 0 0.2 345 y1.4 y28

Ž .12.5y0.1 0 0 y0.3 0 y0.1 0.1 y0.1 344 y27

Ž .12.60.2 0.3 0.3 0 0.3 0.2 0.4 0.2 0.3 317

Ž .12.3

the sand-dominated checkerboard landscapes, the daily domain-averaged latent heatfluxes are arranged according to the patch size.

ŽIn simulations with stripes orientated parallel to the wind GSGP25, SGSP25,.GSGP5, SGSP5 , the daily sums of domain-averaged latent heat fluxes increase with the

patch sizes and fractional coverage by grass in the domain. Here, for the same patchsizes, the degree of surface heterogeneity is the same and the fluxes differ only slightly


for the same degrees of surface heterogeneity. However, no correlation of the daily sumsof domain-averaged latent heat fluxes to patch size and arrangement is found in

Ž .simulations with stripes perpendicular to the wind GSGR25, SGSR25, GSGR5, SGSR5 .SGSC25 and SGSX25 have the same fractional coverage by grass, but different

degree of surface heterogeneity. Thus, in this case, the different sums of domain-aver-Žaged latent heat flux result from the different degrees of surface heterogeneity Tables 2,

.3 .All these findings suggest that the orientation of the pattern to the wind and the patch

size cause differences in the daily sums of the domain-averaged latent heat fluxes. ThisŽ .broadly agrees with the Molders’ 1999 findings who investigated the sensitivity of the¨

impact of land-use changes to the direction of wind. Moreover, the results suggest thatboth the fractional coverage and the degree of heterogeneity concurrently affect thelatent heat fluxes.

5. Vertical motions

As discussed before, different surface characteristics and discontinuities may lead todifferent moistening of low-level atmosphere through transpiration and different airheating. Induced by surface thermal heterogeneity, the ascending motions differ. Notethat since the vertical velocities are volume averages representing volumes of thethickness of the model layer times 5=5 km2, the magnitude of vertical velocitydepends on grid size. Generally, the inclusion of a finer grid increases ability ofmeteorological models to produce larger vertical motions because small-scale horizontaltemperature gradients and velocities can be resolved. To avoid differences resulting fromgrid size, in our study, all simulations are performed with the same grid size of 5=5km2 as pointed out already in Section 2.

In all simulations, vertical velocities do not exceed 0.05 mrs. As mean verticalvelocities are small, turbulence, in principle, is an important contributor to verticaltransport processes, energetics and physics of low extended stratus. In absolute magni-tude, however, the turbulence level is low in low extended stratus.

The pattern of vertical motions depends on patch distribution, patch size, andmodulation of the air mass by upwind surface heterogeneity. Water vapor is transportedto higher levels by upward motions. In the ABL, a distinct pattern of ascent and descentonly develops for some patch arrangements, namely, SGSX25, GSGC25, SGSC10, andGSGX25. In the following, the vertical motions at 12 LT will be exemplary examinedfor these simulations.

5.1. SGSX25

In Fig. 6, the vertical wind distribution of SGSX25 is exemplary shown at twoŽrepresentative WE cross-sections. In the cross-section at 35 km counted from the

. Ž .south , sand exists only while, in the cross-section at 60 km counted from the south ,Ž .grass dominates Fig. 6 . As pointed out before, sand heats more strongly, but supplies

less water vapor to the ABL than grass. Due to the stronger heating upward motionsŽ .develop over sand at the 35-km cross-section Fig. 6b . Descent occurs over the northern


Ž . Ž . Ž .Fig. 6. WE cross-section at a 60 km and b 35 km both counted from the south of vertical winddistribution in cmrs as simulated by SGSX25. At 60 km, maximum and minimum values are 0.2 and y1.1cmrs, respectively. At 35 km, maximum value is 0.9 cmrs. The black underlined parts represent grass.

Ž .and southern grass-dominated part cf. Fig. 6a . Here, however, over sand, the down-Ž .ward motions exceed those occurring over grass Fig. 6a .

5.2. GSGX25

Ž .Despite of the same degree of heterogeneity like SGSX25 Table 2 , in GSGX25, aŽ .totally different pattern of vertical motion establishes Figs. 6, 7 . In GSGX25, namely,

Ž . Ž .ascent occurs in the WE cross-sections at 25 Fig. 7c and 15 km Fig. 7d , whileŽ . Ždescent is found for the WE cross-sections at 35 Fig. 7b and 60 km Fig. 7a; always

.counted from the south . In contrast to its inverse counterpart SGSX25, in GSGX25,descent or ascent cannot be related to the surface type dominant in the WE cross-section

Ž . Ž . Ž . Ž . Ž .Fig. 7. WE cross-section at a 60 km, b 35 km, c 25 km, and d 15 km all counted from the south ofvertical wind distribution in cmrs as simulated by GSGX25. At 15 km, maximum and minimum values are1.2 and y0.1 cmrs. At 25 km, maximum and minimum values are 1.3 and y0.1 cmrs. At 35 km, maximumand minimum values are 0.3 and y0.7 cmrs. At 60 km, maximum and minimum values are 0.2 and y1.6cmrs. The black underlined parts represent grass. The dashes stand for the grass-sand boundary.


Ž . Ž . Ž .cf. Fig. 7 . The WE cross-sections at 60 descent and 15 km ascent , for instance, areboth dominated by sand. In the WE cross-sections at 35 km, the descent enhances

Ž .toward the east Fig. 7 . This different behavior results from the altered heating andevapotranspiring, which modifies thermal stratification and vertical motions. This find-ing means that the surface distribution and patch size, i.e., the degree of heterogeneity,are not the only factor that determines the atmospheric response. Additionally, the kindof surface characteristics plays an important role. The effects of heterogeneity andsurface characteristics are juxtaposed in the atmospheric response. The effects may evenenhance each other in their impact.

5.3. GSGC25

Ž .In the case of GSGC25 not shown , the vertical motions are similar to those ofSGSX25 for the WE cross-section at 35 and 60 km, because of the same patch

Ž .distribution here see Fig. 2 . In the area of ascent located between 25 and 50 kmŽ .counted from the south , however, ascent is less continuously in GSGC25 than inSGSX25 due to the grass-patch in the middle of the domain in GSGC25. Perturbationsoccur above the borders of different surface types, i.e., the additional grass-patch onlyslightly modifies the vertical motions.

5.4. SGSC10

For a patch size of 10=10 km2, the pattern of vertical motion is quite complicateŽ .Fig. 8 . Since the wind turns to the left when approaching the surface, it has a more

Ž . Ž . Ž . Ž . Ž .Fig. 8. WE cross-section at a 60 km, b 30 km, c 20 km, and d 10 km all counted from the south ofvertical wind distribution in cmrs as simulated by SGSC10. At 10 km, maximum and minimum values are 0.3and y0.3 cmrs. At 20 km, maximum and minimum values are 0.3 and y0.2 cmrs. At 30 km, maximum andminimum values are 0.7 and y0.1 cmrs. At 60 km, maximum and minimum values are 0.1 and y1.3 cmrs.The black underlined parts represent grass.


northern component than at height. Thus, due to the modulation of the advected air massby the upwind surface pattern, a different pattern of vertical motions establishes in the

Ž .northern than in the southern part of the domain e.g., Fig. 8 . In the northern part, forŽ .instance, at the WE cross-section at 60 km counted from the south , there is stronger

Ž .descent over the grass–sand boundary looking from the west . Note that this cross-sec-Ž .tion is dominated by grass. At the WE cross-section at 30 km counted from the south

Ž .ascent is stronger over sand–grass boundary looking from the west . This cross-sectionis dominated by sand. Based on these findings, one may conclude that descent or ascentdepends on the dominant surface type of the cross-section in the northern cross-sections.

Ž .At the WE cross-section at 10 or 20 km counted from the south , however, the verticalŽ .motions cannot be assigned to the dominance of the cross-section e.g., Fig. 8 . Several

Ž .small areas of descent and ascent establish Fig. 8 . These facts mean that after the airmass has passed alternating relatively small patches several times, a less distinct, butstill ‘organized’ behavior of vertical motions establishes.

6. Cloudiness

After reaching the condensation level, water vapor condenses and low extendedstratus is formed. In all simulations, the domain is totally covered by extended lowstratus during the entire simulation time. The wet adiabatic cooling rates are on the orderof 1 Krh. Consequently, radiation and wet adiabatic cooling are approximately equalcontributors to low extended stratus.

At noon, cloud bases are at a height of 250 m and cloud tops are at a height of 450 m.In HOMG and HOMS, the cloud-water mixing ratios of the stratus are horizontallyuniform throughout the entire simulation. At noon, for instance, the cloud-water ofHOMG amounts 0.124 and 0.557 grkg at a height of 250 and 450 m, respectively. Atthe same time, the cloud-water of HOMS amounts 0.108 and 0.556 grkg at theseheights. Obviously, at a height of 450 m, the cloud-water mixing ratios of HOMS andHOMG hardly differ. In the case of heterogeneous surfaces, deviations from thesecloud-water values may be related to the effects of heterogeneity on water vapor supply,heating, and vertical motions. Secondary differences result from the modified radiativecooling at cloud top caused by the altered cloud-water distribution. For brevity, theseslight effects will not be discussed here.

No apparent response of the low extended stratus to the underlying surface is foundfor the landscapes with stripes parallel or perpendicular to the wind, no matter of the

Ž .stripe size SGSR25, GSGR25, SGSP5, GSGP5, SGSR5, GSGR5 . The same is true forŽ .landscapes with a patch size of 5 km GSGC5, SGSC5 . Therefore, the results of these

simulations are not further discussed.The findings of the following subsection show that the vertical appearance of the low

extended stratus is strongly modulated by the surface characteristics in the case of lowdegree of heterogeneity. If the low extended stratus is modulated by a landscape, thecloud-water mixing ratios are greater at higher levels over the ascent areas than over thedescent areas or lower layers. For the sake of brevity, in this section, only the mostdistinct examples of stratus modulation by surface heterogeneity are discussed for thespatial and temporal development.


6.1. Daily sums of domain-aÕeraged cloud-water

When comparing the daily sums of domain-averaged latent heat fluxes with those ofŽ .cloud-water Table 3 , a correlation is found between the water vapor supply to the

atmosphere by turbulent latent heat fluxes and the amount of cloud-water in simulationSGSX25 and SGSC25.

Ž . Ž .Although simulations SGSX25 44.4% grass , SGSC25 44.4% grass , SGSP25Ž . Ž . Ž33.3% grass as well as SGSC5 49.8% grass have less grass than HOMG 100%

.grass , their daily sums of domain-averaged cloud-water are higher due to upwardŽ .moisture transport enhanced by the stronger surface heating of sand Table 3 . On the

Ž .other hand, the counterparts of these simulations, namely, GSGC25 55.6% grass ,Ž . Ž . Ž .GSGX25 55.6% grass , GSGP25 66.7% grass , and GSGC5 50.2% grass provide

Ž .lower or equal daily amounts of cloud-water than HOMG Table 2 . This finding meansthat the combination of heating and evapotranspiring patches may enhance the cloud-water amount of low extended stratus as compared to the case with less surface heating.The differential in the daily sums of domain-averaged cloud-water between GSGC25

Ž .and SGSC25 as well as between GSGX25 and SGSX25 exceeds 0.8 grkg Table 3 .These differences may suggest that not the amount of land-use, but the land-usedistribution, i.e., the degree of surface heterogeneity, plays the major role for cloud-wateramount. Comparing the daily sums of domain-averaged cloud-water mixing ratiosprovided by the simulations with the heterogeneous landscapes, the greatest differences

Ž .occur for SGSC10 to SGSX25, SGSC25, and SGSP25, respectively Table 3 .The daily sums of the cloud-water mixing ratios of GSGC10 and SGSC10 broadly

Žagree with those of the simulations that assume nearly the same amounts of grass e.g.,.GSGC5 50.2%, SGSR5 46.7%, GSGR5 53.3%, GSGP5 53.3%, SGSR5 46.7% . Here,

the daily sums of domain-averaged cloud-water differ about 0.3 grkg. The greaterimportance of the surface distribution than that of the fractional coverage by grass isalso manifested by the daily sums of the domain-averaged cloud-water between HOMGand HOMS, which differ 0.3 grkg, although grass supplies more water vapor to the

Ž .atmosphere than sand e.g., Tables 2, 3 .

6.2. Horizontal distribution of cloud-water

Ž . Ž .In SGSC10 e.g., Fig. 9a , and SGSX25 e.g., Fig. 9b , the distributions of cloud-waterclearly reflect the heterogeneity of the underlying surface in both cloud levels. In

Ž .GSGC10 not shown , a clear relation of the cloud-water amount to surface heterogene-ity exists for the lower part of the low extended stratus. In the upper part, therelationship is less distinct.

Surprisingly, the structures of the cloud-water distribution provided by GSGC25Ž . Ž .e.g., Fig. 9c are similar to those of SGSX25 e.g., Fig. 9b . The opposite is true for

Ž . Ž .GSGX25 e.g., Fig. 10 and SGSC25 not shown . All other landscapes hardly modulateŽthe cloud-water distribution e.g., at 12 LT up to 0.01 grkg at a height of 450 m, and up

.to 0.005 grkg at a height of 250 m except SGSP25 and GSGP25. In the case of thesesimulations, a slight, but not distinct modulation parallel to the stripes can be detectedŽ .e.g., at 12 LT about 0.03 grkg . Nevertheless, further discussion only focuses on cases


Fig. 9. Distribution of cloud-water mixing-ratio in grkg at 12 LT at a height of 450 m height as simulated byŽ . Ž . Ž .a SGSC10, b SGSX25, and c GSGC25, respectively. Grey patches indicate grass and light grey patchesindicate sand, respectively.

with a clear modulation of cloud-water by surface heterogeneity, namely, SGSX25,GSGC25, and SGSC10, respectively. Focus is on the level of maximum cloud-water at12 LT.

6.2.1. SGSX25Less cloud-water occurs over both the sand- and grass-patches in SGSX25 than in

Ž .HOMG or HOMS. In SGSX25 Fig. 9b , at a height of 450 m, higher values ofcloud-water are found above the WE-oriented sand-stripe in the middle than over thealternating grass–sand–grass area in the northern and southern part of the domain at 12


Fig. 10. Like Fig. 9, but for the distribution of cloud-water mixing-ratio in grkg at 12 LT for GSGX25 at aŽ . Ž .height of a 250 m and b 450 m, respectively.

LT. This maximum of cloud-water can be explained as follows: The sand-patches heatŽstronger and provide greater sensible heat fluxes than the grass-patches cf. Friedrich

.and Molders, 1998 . Thus, air rises over the sand-patches and is replaced by moist air¨from the neighbored upwind grass-patches that evapotranspire at a higher rate thansand-patches. Consequently, upward transport is enhanced over the sand-stripe and leadsto the increased cloud-water mixing ratios. In SGSX25, less cloud-water is formed

Žabove the sand-patches in the northern part in the WE-orientated area between 50–75


. Žand 25–50 km counted from the south and the southern part in the WE-orientated area. Ž .between 0–25 and 25–50 km counted from the south than over grass Fig. 9b .

Especially at the northern and southern edges of the domain, there exist higher valuesabove grass-patches than sand-patches.

The effect of the different water vapor supply from grass and sand is visible at aŽ .height of 450 m behind the grass–sand boundary Fig. 9b . In the northern part as well

as in the southern part, cloud-water decreases behind the grass-patch with 0.005–0.020grkg and increases after passing the sand with up to 0.010 grkg.

6.2.2. GSGC25Ž .In GSGC25, at a height of 450 m, Fig. 9c , the structures in the cloud-waterŽ .distribution are similar to those of SGSX25 Fig. 9b . Despite of the grass-patch in the

center of the domain, in GSGC25, there exists no response to this patch in thedistribution of cloud-water. On the contrary, in GSGC25 like in SGSX25, distinctresponses in the cloud-water mixing ratio exist at the northern and southern WE-orien-

Ž .tated stripe 0–25 and 50–75 km counted from the south , providing different valuesover grass than sand.

6.2.3. SGSC10Ž .In SGSC10 Fig. 9a , less cloud-water is formed than in HOMG or HOMS. In the

northern most WE-directed stripes, a distinct response to the underlying surface is foundwith an increase and a decrease of cloud-water after passing either a grass- or a

Žsand-patch. In the middle WE-orientated part of the domain between 25 and 50 km. Žcounted from the south , maximum cloud-water mixing ratios exceed 0.56 grkg Fig.

.9a . The clear differences found for the latent heat fluxes over grass and sand are notŽ .reflected in the cloud-water distribution Fig. 9a . Seemingly between 20 and 50 km

counted from the southern edge of the inner model domain, the cloud-water distributionbehaves more like that of a WE-orientated, homogeneously covered surface than that of

Ž .a heterogeneous surface Fig. 9a . Although, a response is visible, the types of surfacedo not provide a distinct assignable response in cloud-water mixing ratios.

6.3. Vertical distribution of cloud-water

As aforementioned, on average, the amount of cloud-water increases from cloud-baseŽ . Ž .to cloud-top e.g., up to 0.4 grkg at noon for all simulations e.g., Fig. 10 . Since there

is no relationship between the distributions of the surface and cloud-water for thelandscapes with patch lengths of 5 km as well as those with stripes of 25 km widthperpendicular to the geostrophic wind direction, the vertical distribution of cloud-waterin these landscapes is not further discussed, here.

If the cloud-water distribution clearly shows structures at a height of 250 m, theseŽstructures will disappear at the 450-m level and new structures will build up e.g., Fig.

.10 . As pointed out already, in GSGC10, a clear relation between the cloud-waterdistribution and surface heterogeneity exists only for the lower part of the low extended

Ž .stratus. In our study, the greatest and most distinct changes of cloud-water distributionŽ . Ž .with height are found for GSGX25 e.g., Fig. 10 and GSGC25 not shown at noon, for

which these cases are exemplary discussed in detail. For landscapes with large patchesŽ .i.e., low degree of heterogeneity , cloud-water is at a minimum in lower levels in the


Žascent areas, while at higher levels, maximum values of cloud-water are found e.g., Fig..10 .

6.3.1. GSGX25In GSGX25, for example, the maximum of cloud-water is found in the middle

Ž .WE-orientated stripe between 25 and 50 km counted from the south at a height of 250Ž .m Fig. 10a . Here, the specific cloud-water values exceed those at the edges by more

than 0.020 grkg. Obviously, this middle grass-stripe governs the cloud-water distribu-tion at the southern edges. The lower values of cloud-water occurring over sand-patches

Ž .at the corners Fig. 10a , may be explained by, on average, lower relative humidity andslightly warmer air occurring over sand than grass. As mentioned before, more water

Ževapotranspires over the grass-cross. The steady lifting without disturbance by a change.in the underlying surface; Fig. 7 supports the formation of the cloud-water maximum.

At a height of 450 m, the cloud-water mixing ratio exceeds 0.560 grkg in the southernŽ .part of the domain Fig. 10b . Minimum values of cloud-water mixing ratios amount

Ž .0.515 grkg at the interface sand–grass at 50 km counted from the south and at theŽ .interface grass–sand at 50 km in WE-direction counted from the south; Fig. 10b . Note

that the behavior of SGSC25 is similar.

6.3.2. GSGC25Ž .Looking at the cloud-water distribution of GSGC25 at 450 m height Fig. 9c , for

instance, low cloud-water mixing ratios are found over the northern and southern partwhere more grass exists than over the middle part and descent occurs. The opposite is

Ž .true for 250 m not shown . Here, namely, low cloud-water mixing ratios are found overŽ .the strong ascent zones at 30–40 km counted from the south at the 250-m level.

Greater mixing ratios of cloud-water are found over the decent zones of the grass-Ždominated WE-cross-section between 5–20 and 55–75 km both counted from the

.south; Fig. 8 left . Note that the behavior of SGSX25 is similar.

6.4. Temporal deÕelopment of cloud-water distribution

In the diurnal course, the amount of cloud-water is related to the magnitude of thesensible and latent heat fluxes with a delay of about 3 h. Increasing latent heat fluxes

Ž .enhance cloud formation positive feedback . Thus, the liquid-water content of the lowextended stratus is maximal at about 15 LT for all simulations. Later on, this enhanced

Žcloudiness reduces insolation and, hence, the fluxes of sensible and latent heat negative.feedback , which again slightly diminishes cloud-water. At about 18 LT, cloud base

starts to sink for most of the simulations.As pointed out before, only SGSX25, GSGX25, SGSC25, GSGC25, SGSC10, and

GSGC10, provide a clear response in cloud-water distribution to the underlying surface.Moreover, GSGC25 and SGSX25 as well as GSGX25 and SGSC25 provide a similarbehavior concerning the distribution pattern of cloud-water and the former pair behavesopposite to the latter pair. For these reasons, the discussion of the temporal developmentof cloud-water distribution can be exemplary limited to that of SGSX25 and SGSC10,respectively.


6.4.1. SGSX25Ž .As mentioned already, at 12 LT Fig. 9b , areas of low cloud-water mixing ratios are

Ž .found in the northern and southern parts above the sand-patches Fig. 9b . Themaximum in the amount of cloud-water occurring over the middle sand-stripe resembles

Ža maximum in sensible heat flux at 12 LT above the sand-stripe not shown; for a.discussion of the sensible heat fluxes see Friedrich and Molders, 1998 and a maximum¨

Ž .in latent heat flux above the grass-corners Fig. 3 . Note that in the northern and

Fig. 11. Like Fig. 9, but for the distribution of cloud-water mixing-ratio in grkg for SGSX25 at a height ofŽ . Ž .450 m at a 15 LT and b 18 LT, respectively.


southern sand-patches, the latent heat fluxes increase slightly behind 40 km in WE-direc-Ž .tion see Fig. 3 .

ŽThe extension of the area of high cloud-water mixing ratios increases until 15 LT cf..Figs. 9b, 11a . This increase agrees with the maximum of the domain-averaged latent

heat flux at 13 LT. The cloud-water maximum that at 12 LT occurs above theŽsand-stripe in WE-direction at the interface grass–sand at 40–60 km counted from the

. Ž .south , shifts northwards with progressing time cf. Figs. 9b, 11a,b . The same is true forŽ .the minimum of cloud-water 0.545–0.540grkg . The distinct upward motion occurring

Ž . Ž .over sand Fig. 6 breaks down in the late afternoon not shown . Accordingly, theŽ .highest values of cloud-water occur at 18 LT in the grass-covered parts Fig. 11b , while

over the sandy parts the cloud-water amount decreases rapidly. In the northern andsouthern parts, the sand-patches do not influence cloud-water amount at the height of450 m. These findings suggest that surface heterogeneity may not only affect the spatial,but also the temporal development of low extended stratus.

6.4.2. SGSC10In SGSC10, the cloud-water distribution shows a similar behavior with time than

Ž . Ž .SGSX25 Figs. 9b, 11 . In SGSC10 Fig. 9a , at a height of 450 m, however, a clearresponse of cloud-water to surface heterogeneity only exists at 12 LT. The maximum

Ž .values achieved in the mixing ratios of cloud-water more than 0.560 grkg are found inthe WE-orientated area between 25–40 km at 12 LT, 35–55 km at 15 LT, and 40–60

Ž . Ž .km all counted from the south at 18 LT not shown . Between 15 and 18 LT, theamount of cloud-water decreases agreeing with the decrease of the domain-averagedlatent heat flux.

7. Conclusions and outlook

Simulations assuming identical model configurations and meteorological initial condi-tions, but different synthetics landscapes of various degree of heterogeneity are per-formed with a meso-b-scale meteorological model. Although turbulence and radiativecooling may play a role for low extended stratus, the main focus is on the impact ofsurface heterogeneity on latent heat fluxes and the properties of low extended stratus.

Under the meteorological situation assumed in this case study, a clear relationshipexists between the underlying surface and the temporal and spatial distribution ofcloud-water for a homogeneous patch-size length of 25 km. Herein, the land-use patternmay be squares or crosses, but not stripes that are parallel or perpendicular to the wind.Patch sizes of 10=10 km2 also provide an obvious response to the underlying surface,while simulations with a patch-size length smaller than 10 km do not show any responseto the land-use distribution at all, i.e., there exists a lower limit for the development of adistinct response of the low extended stratus. This finding suggests to investigate by useof large-scale models, whether there also exists an upper limit for the size of theunderlying surface for the response of extended low-level stratus.


The results substantiate that the amount of cloud-water does not depend primarily onthe amount of grass or sand. Large sand-patches are able to force the required upwardmotions due to great enough fluxes of sensible heat. Nevertheless, adjacent wet patchesare necessary to provide sufficient moisture by latent heat fluxes for modification of lowextended stratus over the sand-patches, i.e., the moisture transport induced by thesurface heterogeneity must be sufficiently great to achieve a modulation of low extendedstratus. This means that the fraction of land use, the distribution and the interactionbetween the neighboring patches concurrently affect cloud-water distribution of a lowextended stratus. Consequently, cloud-water mixing ratios may substantially increase ifthe degree of heterogeneity is low and the fractional coverage of low evapotranspiring,but strongly heating patches slightly exceeds that of the inverse characteristics.

The amount of cloud-water also agrees with the maximum of latent and sensible heatfluxes. Therefore, the amount of cloud-water, independently of the patch size andarrangement, increases in the early afternoon with a temporal offset of 3 h to those ofthe latent and sensible heat fluxes, and decreases toward the evening hours. Thestructure of the cloud-water distribution changes depending on the patch arrangementand patch size with increasing simulation time. Thus, it may be concluded that landscapeheterogeneity may not only influence the spatial, but also the temporal development oflow extended stratus.

A relationship between the maximum of the daily sums of domain-averaged latentŽ .heat fluxes and the maximum of the amount of cloud-water Table 2 is only found in

SGSX25 and SGSC25. Simulations with well-balanced equal parts of wetrcool anddryrwarm parts with respect to patch size and degree of heterogeneity have increaseddaily sums of domain-averaged cloud-water as compared to the others. The results of

Ž . Ž .GSGC25 55.6% grass and GSGX25 55.6% grass , which have the same percentage ofgrass and sand, but different land-use distribution, suggest that, in some cases, theland-use distribution may be of much more importance than the amount of land use forthe daily amount of cloud-water. This fact is also substantiated by the results ofsimulations wherein the percentage of land use differs only slightly and the daily sums

Žof domain-averaged cloud-water differ about 0.3 grkg e.g., GSGC10 and SGSC10 with.52% grass and 48% grass, respectively .

Based on all these findings, it may be concluded that surface heterogeneity mayinfluence the properties of low extended stratus via modified fluxes, variables of state,and vertical motions in humid mid-latitudes. Herein, especially, low degree of hetero-geneity in combination with a slight dominance of low evapotranspiring, but stronglyheating patches may contribute to higher cloud-water mixing ratios, and, hence, denserstratus. Since stratus is often supercooled, land-use changes leading to landscapes of theaforementioned kind may dramatically increase the risk of icing of airplanes.

Acknowledgements

This study was supported financially by DFG under contracts Mo770r1-1 andMo770r1-2 and by BMBF under contract 01LA98494 for which we express our thanks.We also thank the anonymous reviewers for fruitful discussion and helpful comments.


References

Anthes, R.A., 1984. Enhancement of convectional precipitation by mesoscale variations in vegetative coveringin semiarid regions. J. Clim. Appl. Meteorol. 23, 541–554.

Avissar, R., Liu, Y., 1996. Three-dimensional numerical study of shallow convective clouds and precipitationinduced by land surface forcing. J. Geophys. Res. 101D, 7499–7518.

Blackadar, A.K., 1962. The vertical distribution of wind and turbulent exchange in a neutral atmosphere. J.Geophys. Res. 67, 3095–3103.

Businger, J.A., Wyngaard, J.C., Izumi, Y., Bradley, E.F., 1971. Flux profile relationship in the atmosphericsurface layer. J. Atmos. Sci. 28, 181–189.

Buykov, M.V., Khvorostyanov, V.I., 1977. Formation and evolution of radiation fog and stratus clouds in theŽ .atmospheric boundary layer. Izv. Atmos. Oz. Phys. Engl. Ed. 13, 251–260.

Claussen, M., 1988. On the surface energy budget of coastal zones with tidal flats. Contrib. Phys. Atmos. 61,39–49.

Deardorff, J.W., 1978. Efficient prediction of ground surface temperature and moisture, with inclusion of alayer of vegetation. J. Geophys. Res. 84C, 1889–1903.

Eppel, D.P., Kapitza, H., Claussen, M., Jacob, D., Koch, W., Levkov, L., Mengelkamp, H.-T., Werrmann, N.,1995. The non-hydrostatic mesoscale model GESIMA: Part II. Parameterizations and applications. Contrib.Atmos. Phys. 68, 15–41.

Frech, M., Samuelsson, P., Tjernstrom, M., Jochum, A.M., 1998. Regional surface fluxes over the NOPEX¨area. J. Hydrol. 212, 155–171.

Friedrich, K., Molders, N., 1998. A numerical case study on the sensitivity of the water- and energy-fluxes to¨Ž .the heterogeneity of the distribution of land-use. In: Raabe, A., Arnold, K., Heintzenberg, J. Eds. ,

Ž .Meteorologische Arbeiten aus Leipzig III , Wiss. Mitt. Leipzig, Vol. 9, pp. 55–74.Hong, X., Leach, M.J., Raman, S., 1995. Role of vegetation in generation of mesoscale circulation. Atmos.

Environ. 29, 2163–2176.Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found

in canopies in the field. Philos. Trans. R. Soc. London, Ser. B 273, 593–610.Kapitza, H., Eppel, D.P., 1992. The non-hydrostatic mesoscale model GESIMA: Part I. Dynamical equations

and tests. Contrib. Phys. Atmos. 65, 129–146.Kramm, G., Dlugi, R., Dollard, D.J., Foken, T., Molders, N., Muller, H., Seiler, W., Sievering, H., 1995. On¨ ¨

the dry deposition of ozone and reactive nitrogen compounds. Atmos. Environ. 29, 3209–3231.Li, B., Avissar, R., 1994. The impact of spatial variability of land-surface characteristics on land-surface

heat-fluxes. J. Clim. 7, 527–537.Lord, S.J., Willoughby, H.E., Piotrowicz, J.M., 1984. Role of parameterized ice-phase microphysics in an

axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci. 41, 2836–2848.Mahrt, L., Sun, J., Vickers, D., MacPherson, J.I., Pederson, J.R., 1994. Ozone fluxes over patchy cultivated

surface. J. Geophys. Res. 100D, 23125–23131.Mellor, G.L., Yamada, T., 1974. A hierarchy of turbulence closure models for planetary boundary layers. J.

Atmos. Sci. 31, 1791–1806.Molders, N., 1998. Landscape changes over a region in East Germany and their impact upon the processes of¨

its atmospheric water-cycle. Meteor. Atmos. Phys. 68, 79–98.Molders, N., 1999. On the atmospheric response to urbanization and open-pit mining under various¨

geostrophic wind conditions. Meteorol. Appl. Phys. 71, 205.Molders, N., Kramm, G., Laube, M., 1995. The role of parameterized ice microphysics on cloud structures,¨

dynamics and sulfate distribution. Trans. A&WMA, 108–127.Molders, N., Kramm, G., Laube, M., Raabe, A., 1997. On the influence of bulk parameterization schemes of¨

cloud relevant microphysics on the predicted water cycle relevant quantities — a case study. Meteorol. Z.6, 21–32.

Molders, N., Raabe, A., 1996. Numerical investigations on the influence of subgrid-scale surface heterogeneity¨on evapotranspiration and cloud processes. J. Appl. Meteorol. 35, 782–795.

O’Neal, M., 1996. Interactions between land cover and convective cloud cover over midwestern NorthAmerica detected from GOES satellite data. Int. J. Remote Sens. 17, 1149–1181.


Pinty, J.P., Mascart, P., Richard, E., Rosset, R., 1989. An investigation of mesoscale flows induced byvegetation inhomogeneities using an evapotranspiration model calibrated against HAPEX–MOBILHYdata. J. Appl. Meteorol. 28, 976–992.

Seth, A., Giorgi, F., 1996. Three-dimensional model study of organized mesoscale circulation induced byvegetation. J. Geophys. Res. 101D, 7371–7391.

Shuttleworth, W.J., 1988. Macrohydrology — the new challenge for process hydrology. J. Hydrol. 10, 31–56.Stephens, G.L., 1978. Radiation profiles in extended water clouds: II. Parameterization schemes. J. Atmos.

Sci. 35, 2123–2132.Zhong, S., Doran, J.C., 1995. A modeling study of the effects of inhomogeneous surface-fluxes on

boundary-layer properties. J. Atmos. Sci. 52, 3129–3142.

On the influence of surface heterogeneity on latent heat ...directory.umm.ac.id/Data...

Documents

Transcript of On the influence of surface heterogeneity on latent heat ...directory.umm.ac.id/Data...