coupling an advanced land surface–hydrology model with the ... · on the understanding of the...

VOLUME 129 APRIL 2001M O N T H L Y W E A T H E R R E V I E W

q 2001 American Meteorological Society 569

Coupling an Advanced Land Surface–Hydrology Model with thePenn State–NCAR MM5 Modeling System.

Part I: Model Implementation and Sensitivity

FEI CHEN AND JIMY DUDHIA

National Center for Atmospheric Research, Boulder, Colorado

(Manuscript received 14 February 1999, in final form 25 July 2000)

ABSTRACT

This paper addresses and documents a number of issues related to the implementation of an advanced landsurface–hydrology model in the Penn State–NCAR fifth-generation Mesoscale Model (MM5). The conceptadopted here is that the land surface model should be able to provide not only reasonable diurnal variations ofsurface heat fluxes as surface boundary conditions for coupled models, but also correct seasonal evolutions ofsoil moisture in the context of a long-term data assimilation system. In a similar way to that in which themodified Oregon State University land surface model (LSM) has been used in the NCEP global and regionalforecast models, it is implemented in MM5 to facilitate the initialization of soil moisture. Also, 1-km resolutionvegetation and soil texture maps are introduced in the coupled MM5–LSM system to help identify vegetation/water/soil characteristics at fine scales and capture the feedback of these land surface forcings. A monthly varyingclimatological 0.158 3 0.158 green vegetation fraction is utilized to represent the annual control of vegetationon the surface evaporation. Specification of various vegetation and soil parameters is discussed, and the availablewater capacity in the LSM is extended to account for subgrid-scale heterogeneity. The coupling of the LSM toMM5 is also sensitive to the treatment of the surface layer, especially the calculation of the roughness lengthfor heat/moisture. Including the effect of the molecular sublayer can improve the simulation of surface heatflux. It is shown that the soil thermal and hydraulic conductivities and the surface energy balance are verysensitive to soil moisture changes. Hence, it is necessary to establish an appropriate soil moisture data assimilationsystem to improve the soil moisture initialization at fine scales.

1. Introduction

For more than a decade, it has been widely acceptedthat land surface processes and their modeling play animportant role, not only in large-scale atmospheric mod-els including general circulation models (GCMs) (e.g.,Mintz 1981; Rowntree 1983, etc.), but also in regionaland mesoscale atmospheric models (Rowntree and Bol-ton 1983; Ookouchi et al. 1984; Mahfouf et al. 1987;Avissar and Pielke 1989; Chen and Avissar 1994a,b,etc.). Mesoscale models that resolve wavelengths from1 to 100 km (i.e., from meso-g to meso-b scales) areoften used for three applications: 1) regional climatesimulations, 2) numerical weather prediction, and 3) airquality monitoring. Therefore, during the last few years,we have witnessed rapid progress in developing andtesting land surface models in mesoscale atmosphericmodels (e.g., Bougeault et al. 1991; Giorgi et al. 1993;Bringfelt 1996; Smirnova et al. 1996; F. Chen et al.1997; Pielke et al. 1997).

Corresponding author address: Fei Chen, National Center for At-mospheric Research, P.O. Box 3000, Boulder, CO 80307.E-mail: [email protected]

One key motivation behind this progress is that in-creasingly finer spatial and temporal resolutions and im-proved planetary boundary layer (PBL) parameteriza-tions used in modern-era mesoscale numerical modelspermit us to realistically simulate the diurnal and ver-tical structure of the PBL. Due to their role of providingthe surface boundary conditions to the atmosphere, theland surface processes are critical in influencing the PBLstructure and associated clouds and precipitation pro-cesses. On the other hand, as mesoscale models continueto increase in spatial resolution, the density of the ob-servation network is unable to capture the initial me-soscale structure at small scales. The majority of suchmesoscale structures that are missed by the observationnetwork are, in reality, a result of land surface forcingby topography, soil moisture, surface vegetation, andsoil characteristics. Therefore, it is paramount that me-soscale models include an advanced and robust landsurface model in order to properly initialize the state ofthe ground during a data assimilation period and to sub-sequently capture the mesoscale structures in the freeatmosphere and PBL forced by the ground surface.

The fifth-generation Mesoscale Model (MM5), jointlydeveloped by The Pennsylvania State University (Penn

570 VOLUME 129M O N T H L Y W E A T H E R R E V I E W

State) and the National Center for Atmospheric Re-search (NCAR), is a mesoscale modeling system thatincludes advanced model physics. It is a communitymesoscale model widely used for numerical weatherprediction, air quality studies, and hydrological studies(Warner et al. 1991; Mass and Kuo 1998). Recently, theMM5 modeling and data assimilation system has beenused for real-time weather prediction with grid incre-ment as small as 1 km for the U.S. Army Test andEvaluation Command (Davis et al. 1999). Hence, cor-rectly treating the land surface processes is becomingincreasingly important for the model to be able to cap-ture local mesoscale circulations induced by land sur-face forcing. The simple land surface model (LSM),conceptually a ground heat budget model, in the currentofficial release of the MM5 model (Grell et al. 1994)is, however, not compatible with the complexity of otherphysics processes in this model. This simple LSM hasthe following major weaknesses: 1) the soil moisturefield is defined as a function of land use and has onlyto seasonal values (summer and winter), but it does notchange during the simulation and, hence, cannot reflectthe impact of recent precipitation; 2) absence of snowcover prediction; 3) relatively coarse resolution landuse; and 4) no vegetation evapotranspiration and runoffprocesses. Although there have been individual effortsto implement sophisticated LSMs in the MM5 and for-mer MM4 system (Giorgi et al. 1993; Lakhtakia andWarner 1994, etc.), these codes are not generally avail-able to the research community at large. In addition, thecurrent MM5 coding structure is inadequate to accom-modate the evolution of soil state variables such as soilmoisture and snow depth. So, in the official release ofthe MM5, the simple model is the only LSM coupledto the MM5. Thus, the overall goals of this study areto 1) modify the structure of the MM5 modeling systemso that it can easily host a series of LSMs developedby the research community, and 2) implement an ad-vanced LSM to improve the simulation of surface heatfluxes, boundary layer, and precipitation processes.

Coupling a LSM in MM5, or generally speaking inmesoscale models, involves several complex issues. Thefirst problem is to select an adequate but relatively sim-ple LSM for real-time mesoscale weather and hydrologyapplications, because the computational time becomesa serious constraint for most such applications. The se-lection of the LSM is further complicated by the factthat most LSMs require a large number of parametersrelated to the vegetation and soil state. These parametersare difficult to specify at high resolution at continentalscales. Another major problem is related to the initial-ization of soil moisture and temperature fields in themesoscale models, because the soil moisture is a veryimportant component in the land surface modeling sys-tem. Even in a ‘‘perfect LSM,’’ inadequate initial soilmoisture fields can introduce major biases in the par-titioning of surface energy and have a long-lasting effecton the model behavior. Finally, it is necessary to vali-

date/test the coupled MM5–LSM system with availablefields.

The objective of our study is to address and documentthe above issues in the context of coupling an LSM tothe widely used community MM5 modeling system.Given the complexity of these issues, our research ef-forts will be presented in two parts. This first paperreviews problems and recent progress related to the se-lection of LSMs, the specification of land surface fieldsand various parameters, the initialization of soil mois-ture and temperature, and the coupling of the LSM toMM5. In the second paper (Chen and Dudhia 2001),we focus on our effort to validate the coupled MM5–LSM system against field observations.

2. Selection of the land surface model

Following the recognition of the importance of landsurface processes in climate modeling systems, therehave been significant efforts to attempt to more accu-rately represent the land–atmosphere interactions. As aresult of this pursuit, a wide spectrum of LSMs havebeen developed in the last 20 years. One important workalong this line is the introduction of a foliage layer (or‘‘big leaf’’ model) in a soil model proposed by Dear-dorff (1978). In later LSM developments, this conceptof explicitly treating the plant canopy has been adoptedwith variations. The explicit canopy treatment is modestin some models (e.g., Deardorff 1978; Pan and Mahrt1987; Noilhan and Planton 1989) but rather complex inother models (e.g., Dickinson 1984; Sellers et al. 1986;Xue et al. 1991). The complex models employ a com-prehensive treatment of biophysical and radiation in-teractions between the soil surface, vegetation, and theatmosphere. They, of course, have substantially morespecified physical parameters than the more modest can-opy models.

Another type of LSM (e.g., Entekhabi and Eagleson1989; Wood et al. 1992; Schaake et al. 1996) is basedon the understanding of the long-term hydrological cy-cle, and implicitly treats the effect of the vegetationcanopy on evapotranspiration. Some of these hydrolog-ical models are designed and calibrated over large-scaleriver basins and incorporate the effects of subgrid-scalevariability in precipitation and soil moisture. Some re-cent land surface modeling studies, particularly the Pro-ject for Intercomparison of Land-Surface Parameteri-zation Schemes (PILPS) numerical experiments (Shaoand Henderson-Sellers 1996; T. H. Chen et al. 1997;Wood et al. 1998), focused on the evaluation of differentLSMs using long-term observations. These experimentsshowed that, given the same forcing conditions, the sim-ulated surface heat fluxes, soil moisture, and runoff bydifferent LSMs vary considerably. They also helpedmodelers to better understand the limitations of indi-vidual models. Partly motivated by these model inter-comparison experiments, there are efforts to take thestrengths of these LSMs originally designed for atmo-

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spheric applications and apply them to surface hydro-logic models or vice versa (Chen et al. 1996; Liang etal. 1999).

As pointed out by Chen et al. (1996), considering thesignificance of such a wide spectrum of land surfacemodels, it is a big challenge for atmospheric modelersto select a land surface scheme that is appropriate totheir needs. Furthermore, recent numerical experimentsof intercomparing LSMs such as described in Chen etal. (1996) and in PILPS studies (T. H. Chen et al. 1997;Wood et al. 1998, etc.) revealed that sophisticated LSMsdo not consistently outperform the relatively simpleschemes. Partly, this is because it is difficult to accu-rately specify a potentially vast set of physical param-eters, especially at the local scales required by somevegetation/soil/hydrology models.

In order to mitigate the aforementioned weaknessesof the overly simplified LSM in the MM5, a search wasinitiated for an appropriate LSM for weather predictionand hydrological applications. Based upon previous ex-perience in dealing with land surface modeling in theNational Centers for Environmental Prediction (NCEP)operational mesoscale Eta Model, the rationale adoptedhere is to select a LSM with relatively few parametersfor real-time, year-round, and continental-domain ap-plication. The model, however, must still reflect the ma-jor effects of vegetation on the long-term evolution ofsurface evaporation and soil moisture.

Chen et al. (1996) extended the Oregon State Uni-versity LSM (OSULSM), which was originally devel-oped by Pan and Mahrt (1987), to include an explicitcanopy resistance formulation used by Jacquemin andNoilhan (1990) and a surface runoff scheme of Schaakeet al. (1996). They verified the performance of this LSM,alongside that of three other land surface parameteri-zation schemes, from a very simple bucket model to afairly complex simplified simple biosphere (SSiB) mod-el, against 5 month First International Satellite LandSurface Climatology Project (ISLSCP) Field Experi-ment (FIFE) observations. It appears that this OSULSMis superior to the bucket model and performs similarlyto the more sophisticated SSiB model. Tested againstlong-term observations, this LSM is able to reasonablyreproduce the observed diurnal variation of sensible heatfluxes and surface skin temperature. Also, it is capableof capturing the diurnal and seasonal evolution in evap-oration and soil moisture (Chen et al. 1996; T. H. Chenet al. 1997; Chen and Mitchell 1999).

This LSM was implemented in the NCEP operationalEta Model in February 1996, under the support of theNational Atmospheric and Oceanic Administration’sGlobal Energy and Water Cycle Experiment (GEWEX)Continental-Scale International Project program. Eval-uated against field observations, the coupled Eta–OS-ULSM system indeed improves the short-range predic-tion of the surface heat fluxes, near-surface sensible var-iables, boundary layer, and precipitation (Betts et al.1997; Chen et al. 1998; Yucel et al. 1998). Hence, con-

sidering its relative simplicity and adequate perfor-mance in the NCEP coupled Eta Model, this LSM isselected for implementation in the MM5 model. Anotherreason for selecting this LSM is that a similar LSM isused in the NCEP operational global and regional mod-els, and this should facilitate the initialization of soilmoisture in MM5 (a detailed discussion of the issueregarding the soil moisture initialization is given in sec-tion 5).

3. Brief description of the LSM

A brief description of the soil thermodynamics andsoil hydrology in the OSULSM (see Fig. 1) is providedhere. In particular, the focus will be on the model physicsand parameters that have been changed from the LSMpreviously documented in Ek and Mahrt (1991) andChen et al. (1996). This LSM is based on the couplingof the diurnally dependent Penman potential evapora-tion approach of Mahrt and Ek (1984), the multilayersoil model of Mahrt and Pan (1984), and the primitivecanopy model of Pan and Mahrt (1987). It has beenextended by Chen et al. (1996) to include the modestlycomplex canopy resistance approach of Noilhan andPlanton (1989) and Jacquemin and Noilhan (1990). Ithas one canopy layer and the following prognostic var-iables: soil moisture and temperature in the soil layers,water stored on the canopy, and snow stored on theground. For the soil model to capture the daily, weekly,and seasonal evolution of soil moisture and also to mit-igate the possible truncation error in discretization, weuse four soil layers, and the thickness of each layer fromthe ground surface to the bottom are 0.1, 0.3, 0.6, and1.0 m, respectively. The total soil depth is 2 m, withthe root zone in the upper 1 m of soil. Thus, the lower1-m soil layer acts like a reservoir with a gravity drain-age at the bottom. The depth of the vegetation roots canbe specified as a function of vegetation type, if realisticrooting-depth data are available in the future.

a. Model thermodynamics

The surface skin temperature is determined followingMahrt and Ek (1984) by applying a single linearizedsurface energy balance equation representing the com-bined ground–vegetation surface. The ground heat fluxis controlled by the usual diffusion equation for soiltemperature (T):

]T ] ]TC(Q) 5 K (Q) . (1)t[ ]]t ]z ]z

The volumetric heat capacity, C (J m23 K21), and thethermal conductivity, Kt (W m21 K21), are formulatedas functions of volumetric soil water content, Q [fractionof unit soil volume occupied by water; see Pan andMahrt (1987)]:


FIG. 1. A schematic representation of the OSULSM in the coupled MM5 model.

FIG. 2. Thermal conductivity as function of volumetric soil mois-ture for four soil types: sand, silt, loam, and clay.

C 5 QC 1 (1 2 Q )C 1 (Q 2 Q)C , (2)water s soil s air

420 exp[2(2.7 1 P )], P # 5.1f fK (Q) 5t 50.1744, P . 5.1 , 0,f

andbP 5 log[C (Q /Q) ]. (3)f s s

The volumetric heat capacities are Cwater 5 4.2 3 106

J m23 K21, Csoil 5 1.26 3 106 J m23 K21, and Cair 5

1004 J m23 K21. Here, Qs and Cs are maximum soilmoisture (porosity) and saturated soil potential (suc-tion), respectively, and both depend on the soil texture(Cosby et al. 1984). The above function for computingthe thermal conductivity (Kt), suggested by McCumberand Pielke (1981), has been used in numerous LSMs(e.g., Noilhan and Planton 1989; Viterbo and Beljaars1995). The relationship between the volumetric soilmoisture and thermal conductivity is illustrated in Fig.2. Recently, Peters-Lidard et al. (1998) showed that,when compared to other formulations and the data col-lected in FIFE, this formulation tends to overestimate(underestimate) Kt during wet (dry) periods, and thesurface heat fluxes are sensitive to the treatment of ther-mal conductivity. Hence, the maximum value of Kt iscapped at 1.9 W m21 K21. We plan to test several thermalconductivity formulations in future studies, when rele-vant data are at our disposal.

The layer-integrated form of Eq. (1) for the ith soillayer is:

]T ]T ]TiDz C 5 K 2 K . (4)i i t t1 2 1 2]t ]z ]zz zi11 i

The prediction of Ti is performed using the Crank–Nicholson scheme. The temperature at the lower bound-ary, assumed to be 3 m below the ground surface, isspecified by the annual mean surface air temperature(see section 4 for detail). This also implies that the total


FIG. 3. Hydraulic conductivity as function of volumetric soil mois-ture for four soil types: sand, silt, loam, and clay.

soil column in the current LSM cannot exceed 3 m,although the number of layers is not limited.

b. Model hydrology

In the hydrology model, the prognostic equation forthe volumetric soil moisture content (Q) is

]Q ] ]Q ]K5 D 1 1 F , (5)Q1 2]t ]z ]z ]z

where both the soil water diffusivity D and hydraulicconductivity K are functions of Q, and FQ representssources and sinks (i.e., precipitation, evaporation, andrunoff ) for soil water. This diffusive form of Richard’sequation is derived from Darcy’s law under the as-sumption of a rigid, isotropic, homogeneous, and one-dimensional vertical flow domain (Hanks and Ashcroft1986), and thereby the soil water diffusivity D is givenby D 5 K(Q)(]C/]Q) wherein C is the soil water ten-sion function. In Cosby et al. (1984), K and C are com-puted by K(Q) 5 Ks(Q/Qs)2b13 and C(Q) 5 Cs/(Q/Qs)b, where b is a curve-fitting parameter; Ks, Cs,and b depend on soil type.

The hydraulic conductivity, K, as well as the diffu-sivity, D, are highly nonlinearly dependent on the soilmoisture, as demonstrated in Fig. 3. It can change sev-eral orders of magnitude, even for a small variation insoil moisture, particularly when the soil is relatively dry.Cuenca et al. (1996) indicate that, for a bare-soil case,the diurnal partitioning of surface energy into latent heatand sensible heat is greatly affected by the soil waterparameterization. Given such a sensitivity of hydraulicconductivity to soil moisture fluctuations and input soilproperties, it is important to further investigate alter-native parameterization schemes using better datasets toestimate the soil hydraulic properties in future studies.

Integrating Eq. (5) over four soil layers, as used inthe MM5 model, and expanding FQ, we obtain

]Q ]Q1d 5 2D 2 K 1 P 2 R 2 E 2 E , (6)z z d dir t1 1 11 2]t ]zz1

]Q ]Q ]Q2d 5 D 2 D 1 K 2 K 2 E , (7)z z z t2 1 2 21 2 1 2]t ]z ]zz z1 2

]Q ]Q ]Q3d 5 D 2 D 1 K 2 K 2 E , (8)z z z t3 2 3 31 2 1 2]t ]z ]zz z2 3

and

]Q ]Q4d 5 D 1 K 2 K , (9)z z z4 3 41 2]t ]zz3

where is the ith soil layer thickness, Pd the precip-dzi

itation not intercepted by the canopy, and Eti the canopytranspiration taken by the canopy root in the ith layerwithin the root zone layers (the root zone has threelayers in the coupled MM5–LSM). At the bottom of thesoil model, the hydraulic diffusivity is assumed to bezero, so that the soil water flux is due only to the ‘‘grav-itational’’ percolation term , also named subsurfaceKz4

runoff or drainage.Following the recent trend to merge the traditional

LSM with hydrological models to better represent therunoff mechanism, we adopt the surface runoff modelin the Simple Water Balance (SWB) model to calculatethe surface runoff R. The SWB model (Schaake et al.1996) is a two-reservoir hydrological model that istypically calibrated for large river basins and that takesinto account the spatial heterogeneity of rainfall, soilmoisture, and runoff. The surface runoff, R, is definedas the excess of precipitation not infiltrated into thesoil (R 5 Pd 2 Imax). The maximum infiltration, Imax ,is formulated as

D [1 2 exp(2kdtd )]x iI 5 P ,max d P 1 D [1 2 exp(2kdtd )]d x i

4 KsD 5 DZ (Q 2 Q ), and kdt 5 kdt , (10)Ox i s i ref Ki51 ref

where di is the conversion of the current model timestep dt (in terms of seconds) into daily values (i.e., di

5 dt/86 400); Ks is the saturated hydraulic conductivity,which depends on soil texture; and kdtref 5 3.0 and Kref

5 2 3 1026 m s21 are specified based on our PILPS2(c) experiments (Wood et al. 1998). Further work needsto be done to calibrate these parameters over variousbasins with different precipitation climatologies.

The total evaporation, E, is the sum of 1) the directevaporation from the top shallow soil layer, Edir; 2) evap-oration of precipitation intercepted by the canopy, Ec;and 3) transpiration via canopy and roots, Et. That is,E 5 Edir 1 Ec 1 Et.

Based on a sensitivity test of Betts et al. (1997), weadopt here a simple linear method (Mahfouf and Noilhan


1991) for computing the direct evaporation from theground surface:

Q 2 Q1 wE 5 (1 2 s )bE and b 5 , (11)dir f p Q 2 Qref w

where Ep is the potential evaporation, which is calcu-lated by a Penman-based energy balance approach thatincludes a stability-dependent aerodynamic resistance(Mahrt and Ek 1984), Qref and Qw are the field capacityand wilting point, and sf is the green vegetation fraction(cover), which is critical for the partitioning of totalevaporation between bare soil direct evaporation andcanopy transpiration.

The wet canopy evaporation is determined by

nWcE 5 s E , (12)c f p1 2S

where Wc is the intercepted canopy water content, S isthe maximum canopy capacity (chosen here to be 0.5mm), and n 5 0.5. This is similar to the formulationsof Noilhan and Planton (1989) and Jacquemin and Noil-han (1990). The budget for intercepted canopy water is

]Wc 5 s P 2 D 2 E , (13)f c]t

wherein P is the input total precipitation. If Wc exceedsS, the excess precipitation or drip, D, reaches the ground[note that Pd 5 (1 2 sf )P 1 D in Eq. (6)]. The canopyevapotranspiration is determined by

nWcE 5 s E B 1 2 , (14)t f p c 1 2[ ]S

where Bc is a function of canopy resistance and is for-mulated as

D1 1

RrB 5 , (15)c D1 1 R C 1c h Rr

where Ch is the surface exchange coefficient for heatand moisture; D depends on the slope of the saturationspecific humidity curve; Rr is a function of surface airtemperature, surface pressure, and Ch; and Rc is thecanopy resistance. Details on Ch, Rr, and D are providedby Ek and Mahrt (1991). The canopy resistance, Rc, iscalculated here following the formulation of Jacqueminand Noilhan (1990), where

RcminR 5 ,c LAIF F F F1 2 3 4

RR /R 1 f 2gcmin cmaxF 5 where f 5 0.55 ,1 1 1 f R LAIgl

1F 5 ,2 1 1 h [q (T ) 2 q ]s s a a

2F 5 1 2 0.0016(T 2 T ) , and3 ref a

3 (Q 2 Q )di w ziF 5 , (16)O4 (Q 2 Q )(d 1 d )i51 ref w z z1 2

where F1, F2, F3, and F4 are subject to 0 and 1 as lowerand upper bounds and they represent the effects of solarradiation, vapor pressure deficit, air temperature, andsoil moisture. Here, qs (Ta) is the saturated water vapormixing ratio at the temperature Ta. The variable Rcmin isthe minimum stomatal resistance, LAI is the leaf areaindex, and Rcmax is the cuticular resistance of the leavesand is set to 5000 s m21, as in Dickinson et al. (1993).The variable Tref is 298 K according to Noilhan andPlanton (1989). Note that the soil moisture stress func-tion is only integrated in the root zone, which reachesthe third soil layer in the current implementation. Allother nonconstant parameters involved in the aboveequations will be discussed in the following section.

c. Snow and sea-ice model

Because this LSM is designed for application over acontinental scale and should be able to deal with varioussurface characteristics, a simple snow and sea-ice modelis included. The snow model has only one layer of snowcover and simulates the snow accumulation, sublima-tion, melting, and heat exchange at snow–atmosphereand snow–soil interfaces. The precipitation is catego-rized as snow when the temperature in the lowest at-mospheric layer is below 08C. The model estimates theheat flux between the soil and the snow by

T 2 Ts soilG 5 K , (17)snow Dsnow

where Ts is the ‘‘skin’’ temperature, Tsoil the temperaturein the first soil layer, and Dsnow the physical snow depththat is assumed to be 10 times the water-equivalent snowdepth. Although the thermal diffusivity for snow, Ksnow,depends on the porosity of snow, it is set to be 0.35 Wm21 K21 in the present model.

Compared to the original OSULSM (Ek and Mahrt1991), a new iterative procedure has been developed torepresent the snow evaporation/sublimation and meltingprocess. First, knowing the snow heat flux, G, enablesone to calculate the potential evaporation Ep using thesurface energy balance equation:


4(1 2 a)S↓ 1 L↓ 2 sT9

5 G 1 r C C |V|(T9 2 T )0 P h a

1 r L C |V|[q (T9) 2 q ], (18)0 y h s a

where r is the air density and Cp the air heat capacity.Here, |V| (wind speed), Ta, and qa (air temperature andspecific humidity) are evaluated at the lowest modellevel. The terms on the left-hand side are the downwardshort- and longwave surface radiation and the upwardlongwave radiation, while the terms on the right-handside are the snow, sensible, and latent heat fluxes. Theskin temperature T9 is the temperature of the surface ifthe snow surface evaporates at the potential rate and isset to the snow surface temperature of the previous timestep. The snow evaporates/sublimates at the followingrate:

E , D $ E dp snow p tE 5 Dsnow , D , E d .snow p td t

Given E, the effective skin temperature, Ts, can becalculated by solving the surface energy balance equa-tion:

4(1 2 a)S↓ 1 L↓ 2 sTs

T 2 Ts soil5 K 1 r C C |V|(T 2 T ) 1 LE. (19)s 0 P h s aDsnow

If the resultant Ts is below 08C, snow will not melt.If Ts . 08C, evaporation/sublimation and melting willcoexist, and the evaporation/sublimation will proceedat a potential rate at the snow surface temperature Tc 508C, where E 5 Ep 5 r0Ch |V|[qs(Tc) 2 qa]. Hence, theamount of snowmelt, hm, which is allowed to drip intothe top soil layer, is calculated as

4(1 2 a)S↓ 1 L↓ 2 sTc

T 2 Tc soil5 K 1 r C C |V|(T 2 T ) 1 LEs 0 P h c aDsnow

1 L h , (20)i m

where Li is the latent heat of fusion and Tc is the snowtemperature that responds to both the evaporation/sub-limation and melting of snow. If hm is greater than theamount of snow left after the evaporation/sublimation,defined as , all the remaining snow (i.e., ) will melt.h9 h9m m

In this case, a new surface skin temperature Ts will bedetermined by the surface energy balance:

4(1 2 a)S↓ 1 L↓ 2 sTs

T 2 Ts soil5 K 1 r C C |V|(T 2 T ) 1 LEs 0 P h s aDsnow

1 L h9 . (21)i m

Still, there are several weaknesses in this simple snow

model: 1) uniform snow cover over a given grid cell,2) only one layer of snow, 3) constant thermal diffu-sivity for snow, and 4) no consideration of snow ageand porosity. Recently, Koren et al. (1999) extendedthis LSM to include a physically based parameterizationof frozen soil and a new snow accumulation/ablationscheme. This new scheme is able to simulate the totalice content of each soil layer. Tested against the datacollected at the Rosemount site in Minnesota, the sim-ulated soil temperature and unfrozen water contentagreed with the observations reasonably well at differentsoil layers. Once this new snow and frozen soil physicsis fully evaluated, it will be included in the coupledMM5.

As for the sea-ice model, because no data on icethickness are routinely available, we attempt to get thesimple aspects of the behavior right, rather than possiblyovercomplicating the model when the initial data willnever justify a sophisticated parameterization. Hence, asimple sea-ice model is implemented to account for theheat transfer among sea-ice layers and at sea-ice–at-mosphere and sea-ice–sea interfaces. The sea-ice packis divided into four equal layers with 0.75-m depth foreach layer, and the total sea-ice pack is 3 m deep. Anarbitrary 0.1-m snowpack is assumed to cover the sea-ice surface. The surface heat transfer and snow meltingover the sea-ice surface is treated the same as in thesnow model. The heat conduction equation [Eq. (4)] isalso applied for the temperature in the sea-ice layerswith three changes: 1) the heat capacity, C, for sea iceis 1.742 3 106 J m23 K21; 2) the thermal conductivity,Kt, is 2.2 W m21 K21; and 3) the lower boundary con-dition for the sea-ice pack (i.e., the temperature at thesea surface below sea-ice pack) is assumed to be 228C.

4. Land surface characteristic fields andparameter specification

In this LSM, there are two primary variables uponwhich other secondary parameters (such as minimal can-opy resistance and soil hydraulic properties) are deter-mined. These two variables are the vegetation type andthe soil texture. For vegetation classification, we utilizethe 1-km resolution U.S. Geological Survey’s (USGS)SiB model vegetation categorization, which has 16 landcover classes (see Table 1). This land cover dataset isderived from the 1-km satellite Advanced Very HighResolution Radiometer (AVHRR) data obtained fromApril 1992 through March 1993. In fact, in addition tothe SiB categories, this land cover dataset also providesseveral other land cover classifications (Loveland et al.1995). Using North America as an example, in additionto the relatively simple 16-category (SiB land coverlegend) classification there is a very detailed 205-cat-egory (seasonal land cover legend) classification. TheUSGS land cover data have been used in other regionalcoupled models (Pielke et al. 1997). Considering thedifficulty in specifying physical parameters for a large


TABLE 1. Vegetation-related parameters in the LSM, which include roughness length (Z0) in m, minimal stomatal resistance (Rcmin) in sm21. Here, Rgl is the visible solar flux for which F1 [see Eq. (16)] is about to double its minimum value, and hs is a parameter in F2.

Vegetation type Albedo Z0 (m) Rcmin Rgl hs

1) Broadleaf-evergreen trees2) Broadleaf-deciduous trees3) Broadleaf and needleleaf trees4) Needleleaf-evergreen trees5) Needleleaf-deciduous trees (larch)6) Broadleaf trees with groundcover7) Groundcover only8) Broadleaf shrubs with groundcover9) Broadleaf shrubs with bare soil10) Dwarf trees/shrubs with groundcover (tundra)11) Bare soil12) Cultivations13) Wetland14) Dry coastal complex15) Water16) Glacial

0.110.120.120.10.110.190.190.250.250.160.120.190.120.190.190.80

2.6530.8260.81.0890.8540.8560.0750.2380.0650.050.0110.0750.040.0750.010.011

150100125150100

7040

300400150

40.0150400

999

303030303065

100100100100

100100100

999

41.6954.5351.9347.3547.354.5336.3542.042.042

36.3560

200

999

number of vegetation classifications at the microscale,we chose the 16-category SiB classification for the cou-pled MM5–LSM. However, we retain the capability ofexpanding it to include two or three additional cate-gories to account for unique land cover types found insome limited areas. This 1-km resolution land use da-taset provides not only a detailed spatial distribution ofvegetation, but also a delineation between water bodiesand land surface for MM5 high-resolution applications.When the MM5 horizontal grid increment is larger than1 km, the dominant vegetation type in each grid box isselected to represent the ‘‘grid level’’ vegetation char-acteristics.

While the vegetation type is an annually invariantfield, some vegetation characteristics may vary season-ally and can be specified either in lookup tables or ina satellite-derived dataset. Here, we adopt both tech-niques in the coupled MM5–LSM system. For instance,the green vegetation fraction, sf , defined as the grid-cell fraction for which midday downward solar inso-lation is intercepted by photosynthetically active greencanopy, acts as a fundamental weighting coefficient inpartitioning the total evaporation into the three com-ponents of evaporation. The sensitivity tests of Jac-quemin and Noilhan (1990) and Betts et al. (1997)showed it to be an important first-order parameter. Withimproved technology, the remotely sensed data obtainedfrom satellites provides excellent continuous estimatesof the evolution of the vegetation state at global scales.

Sellers (1987) derived an important interrelationshipamong leaf area index (LAI), absorbed photosyntheti-cally active radiation (APAR, which is closely relatedto the green vegetation cover), and satellite AVHRR/normalized difference vegetation index (NDVI). Hefound that under specified canopy properties, the APARwas linearly related to NDVI and curvilinearly relatedto LAI. It is, however, difficult to simultaneously deriveLAI and sf from a single-product NDVI, unless one ofthem is prescribed. According to Gutman and Ignatov

(1998), it is more justified to prescribe LAI and derivesf . In addition, while sf and LAI are about equallyimportant, the natural variability (a priori uncertainty)in sf seems substantially higher. Therefore, in the cur-rent coupled MM5–LSM, the LAI is prescribed, but sf

is assigned by the monthly 5-yr climatology of greenvegetation cover data with 0.158 resolution derived fromAVHRR (Gutman and Ignatov 1998). The seasonal var-iation of this green vegetation fraction is shown in Fig.4, wherein the green vegetation fraction in the agricul-tural areas located in the central and eastern UnitedStates dramatically changes from winter to the summerpeak growing season. However, the LAI and sf shouldbe allowed to change seasonally in the coupled modelif future appropriate and consistent data become avail-able.

Another critical vegetation parameter, which may bederived from satellite, is the surface albedo. Neverthe-less, the spatial resolution of currently available albedo-climatology data are relatively coarse (e.g., the quarterly18 albedo from the ISLSCP Initiative-I) compared tomesoscale model resolution of about 5–30 km. Hence,the albedo as well as the roughness length are specifiedaccording to the given dominant vegetation type, whichhas a higher resolution than the ISLSCP data, in thecurrent model. Other vegetation-related physical param-eters are compiled from various sources (e.g., Dormanand Sellers 1989; Dickinson et al. 1993; Mahfouf et al.1995) and are listed in Table 1. Among these parameters,the minimum canopy resistance may be the most im-portant one.

The soil texture is determined by using the 1-km res-olution multilayer 16-category soil characteristics da-taset developed by Miller and White (1998), which isbased on the U.S. Department of Agriculture’s State SoilGeographic Database and provides information on thesoil texture, bulk density, porosity, and available watercapacity, etc. We use the soil texture class of the firstsurface soil layer in this dataset to apply to the soil


FIG. 4. Seasonal variation of NOAA/NESDIS 0.158 green vegetation fraction.

TABLE 2. Soil-related paramters in the LSM. The hydraulic properties are volumetric water content at saturation (Qs), saturation soil suction(Cs), hydrualic conductivity at saturation (Ks), field capacity (Qref), and wilting point (Qw). Here, b is an exponent in the function that relatessoil water potential and water content.

Soil type Qs (m3 m23) Cs (m) Ks (m s21) b Qref (m3 m23) Qw (m3 m23)

1) Sand2) Loamy sand3) Sandy loam4) Silt loam5) Silt6) Loam7) Sandy clay loam8) Silty clay loam9) Clay loam10) Sandy clay11) Silty clay12) Clay13) Organic material14) Water15) Bedrock16) Other (land–ice)

0.3390.4210.4340.4760.4760.4390.4040.4640.4650.4060.4680.4680.439

0.250.421

0.0690.0360.1410.7590.7590.3550.1350.6170.2630.0980.3240.4680.355

7.590.036

1.07E261.41E255.23E262.81E262.81E263.38E264.45E262.04E262.45E267.22E261.34E269.74E273.38E26

9.74E281.34E26

2.794.264.745.335.335.256.668.728.17

10.7310.3911.55

5.25

11.5511.55

0.2360.2830.3120.360.360.3290.3140.3870.3820.3380.4040.4120.329

0.2330.283

0.010.0280.0470.0840.0840.0660.0670.120.1030.10.1260.1380.06

0.0940.028

texture throughout the whole soil column for a givenmodel grid cell. Four soil parameters, porosity (Qs),saturated metric potential (Cs), saturated hydraulic con-ductivity (Ks), and slope of the retention curve (b), are

specified by the soil analysis of Cosby et al. (1984) (seeTable 2).

The field capacity (Qref) and wilting point (Qw) arenot provided by Cosby et al. (1984) and need to be


FIG. 5. Annual mean air temperature of 1987–88 adopted to theMM5 topography.

computed. The presumed water content at which internaldrainage allegedly ceases, termed the field capacity, had,for a long time, been accepted almost universally in soilphysics as an actual physical property for each soil type.However, there is no standard technique to measure orcalculate it. In some literature, it is assigned to be 75%of the maximum soil moisture (porosity) (Noilhan andPlanton 1989) or the soil moisture value when the hy-draulic conductivity, K, equals 0.1 mm day21 (Wetzeland Chang 1987). Yet, it is a very important parameterin the LSM formulations (11) and (16), which determinethe evaporation rate, since it is assumed that there is nosoil moisture stress when the soil moisture is above thefield capacity. Here, we assume that a drainage flux of0.5 mm day21 through the bottom of the root zone issmall enough to be disregarded and that the soil moistureat this drainage rate is equal to the field capacity. Thiscriterion is given by Hillel (1980). The wilting point isdefined as the critical soil moisture at (or below) whichthe evaporation process ceases; that is, it will be difficultto extract additional water that is closely tied to soilparticles. As in Wetzel and Chang (1987), the wiltingpoint is the soil moisture value when the soil waterpotential adjacent to the vegetation roots drops to 2200m with respect to the ground surface.

As indicated by Chen et al. (1996), the spatial dis-tribution of soil moisture and, hence, evaporation is farfrom homogeneous in the natural world. For instance,even though the area-averaged soil moisture representedby a grid box of an atmospheric model is at the wiltingpoint, the soil moisture in some subarea can be higherthan the wilting point, and vice versa. Therefore, evap-oration does occur over these subgrid areas and may bea significant contributor to the area-averaged latent heatflux during dry periods, as demonstrated by analysis offield observations (e.g., Stewart and Verma 1992). Chenet al. (1996) suggested that using a nonlinear soil mois-ture stress function can partly account for the soil mois-ture heterogeneity and can maintain a nonzero evapo-ration beyond the wilting point and reduce the evapo-ration when the area-averaged soil moisture is near thefield capacity. This idea is adopted in the MM5–LSM,but we simply increase (decrease) the value of fieldcapacity (wilting point) to save computational time.Thus, these two parameters are calculated for each soiltype as

1/(2b13)291 2 5.79 3 10Q 5 Q 1 and (22)ref s 1 2[ ]3 3 Ks

21/b200Q 5 0.5Q . (23)w s1 2Cs

To close the thermal diffusion equation [Eq. (4)], weneed a lower boundary condition on temperature at 3m below the ground surface. Because the observed soiltemperature at this depth is not available, the annuallyaveraged air temperature at 2 m is used as a substitute.

To our knowledge, the 18 3 18 2-m air temperature inthe ISLSCP dataset is the only fine- (in a relative sense)resolution data available on the global scale. Althoughthis 2-yr monthly 6-hourly 2-m air temperature is basedon the European Centre for Medium-Range WeatherForecasts (ECMWF) model analysis and is strongly af-fected by the model physics, it looks very similar to the58 3 58 NCAR climatological 2-m temperature butshows finer structure due to its higher resolution. It,however, depends on the ECMWF model orography.Hence, this temperature is first adjusted to the 1000-mblevel using the ECMWF annual analyzed surface pres-sure (also available from the ISLSCP dataset) accordingto the standard atmosphere lapse rate. It is then adjustedto the MM5 orography following the same procedureso it reflects the MM5 high-resolution topography, forinstance over the Rocky Mountain areas as shown inFig. 5.

5. Initialization of soil moisture

There is a rich literature demonstrating the impor-tance of soil moisture, particularly its spatial hetero-geneity, in mesoscale processes (e.g., Avissar and Pielke1989; Chen and Avissar 1994a,b; Ziegler et al. 1995).Indeed, the soil moisture is a very important componentof land surface modeling, and it would not make muchsense to implement a sophisticated LSM in mesoscalemodels without a proper soil moisture initialization pro-cedure. However, the initialization of soil moisture incoupled regional models is jeopardized by the fact thatthere are no routine soil moisture observations. Thus,until a soil moisture data assimilation system is devel-oped for the MM5, the initialization of the LSM willlargely depend on soil moisture fields obtained fromanalysis/forecasts from other models. Furthermore, asdemonstrated by Koster and Milly (1997), differentLSMs may have different soil moisture dynamic ranges


TABLE 3. Initial soil moisture fields in MM5–LSM four soil layersobtained from the interpolation of NCEP–NCAR reanalysis, at fourlocations.

Location0.0–0.1-msoil layer

0.1–0.4-msoil layer

0.4–1.0-msoil layer

1–2-m soillayer

Dry point 1Dry point 2Semidry pointWet point

0.160.150.290.39

0.160.150.280.38

0.150.140.260.36

0.140.140.240.34

due to various approaches in treating evaporation andrunoff.

In the current MM5–LSM model, the initial soil mois-ture can be obtained from two forecast/analysis systems,because a similar LSM is used in these systems and thesoil moisture fields are compatible to the MM5–LSMwith regard to its dynamic range. These two modelingsystems are described as follows.

1) The NCEP regional operational Eta Model and itscompanion data assimilation system (EDAS) can beused. Because the Eta/EDAS system has relatively highresolution (running at 32 km since May 1998), its initialsoil moisture can be useful for initializing the coupledMM5–LSM model over the North America region.

2) The NCEP–NCAR reanalysis system can be usedfor retrospective applications and the NCEP global dataassimilation system (GDAS) can be used for real-timeapplications for regions outside of North America andfor historical cases going back 40 yr. Nevertheless, somestudies (e.g., Roads et al. 1997; Chen and Mitchell 1999)point out that the reanalysis tends to have very wet soilmoisture due to a model positive precipitation bias, andthus a climatological soil moisture damping field is ap-plied in the reanalysis system. In addition, the bias inthe soil moisture fields in the reanalysis depends onseason. Thus, when using NCEP–NCAR reanalysis orGDAS soil moisture fields to initialize the MM5 model,it may be necessary to adjust the soil moisture in theinitialization procedure. The following formulations,based on several case studies conducted with the NCEPEta Model while using the GDAS soil moisture as initialconditions, represent a possible choice for subjectivelyincreasing (decreasing) the reanalysis–GDAS soil mois-ture from July to October (Jan–Jun).

When using the reanalysis–GDAS soil moisture toinitialize the MM5 for January through June,

min(0.7SM 1 0.084, 0.35), SM $ 0.28g gSM 5MM5 5SM , otherwiseg

(24)

and for July through December,

SMMM5

0.429SM 1 0.16, 0.21 , SM # 0.28g g5 51.19SM , SM # 0.21,g g

(25)

where SMg is the soil moisture (in volumetric units)from the reanalysis–GDAS and SMMM5 is the initial soilmoisture in the MM5 model. Such modest adjustmentsmay, for instance, slightly decrease soil moisture in east-ern and southern parts of the United States and increasesoil moisture in western parts of the United States.

To demonstrate the sensitivity of the coupled MM5–LSM model to the initial soil moisture fields, the MM5is set up to perform 48-h simulations (starting at 4 Jun

1987). This period is chosen because of the existenceof clear-sky conditions over most of the United States,which allows a better assessment of the land surfaceprocesses. The MM5 model is nested, and the hori-zontal grid increments are 90, 30, and 10 km. Theinitial soil moisture and temperature conditions in theMM5 are obtained from the NCEP–NCAR reanalysis,wherein, as mentioned, a similar LSM is used (Kalnayet al. 1996). For these sensitivity tests, two sets ofsimulations were conducted: 1) the soil moisture ischanged (increased and decreased) by 10%, repre-senting a relatively small change in soil moisture, withrespect to the reanalysis values; and 2) the soil moistureis changed (increase and decrease) by 0.1 in terms ofabsolute volumetric values, representing a largechange, with respect to the reanalysis values. In eachof these simulations, the surface heat fluxes at fourgrid points are studied. These four points are locatedat 1) 35.018N, 109.098W, near the New Mexico andArizona border for the 10% change experiment; 2)35.018N, 115.08W, near Lake Havasu City, Arizona,for the 0.1 change experiment; 3) 34.118N, 97.998W,near Oklahoma City, Oklahoma; and 4) 34.118N,84.08W, near Atlanta, Georgia, for both experiments.The first two points represent dry climatology, and thethird and the fourth points represent semidry and wetclimatology, respectively. Note that the reanalysis vol-umetric soil moisture fields are for two soil layers, 0–10and 10–200 cm, and are interpolated to the four soillayers in the MM5–LSM. The initial soil moisture, in-terpolated from the reanalysis, for the four soil layersat the above four points, are listed in Table 3.

The responses of the coupled model surface heat fluxto the initial soil moisture changes are shown in Figs.6 and 7 for the two dry points and the semidry point.The largest impact of soil moisture on the surface heatfluxes is found for dry soils. At the initial soil moisturevalue of 0.16, a small change of 10% in initial soilmoisture can result in a 30 W m22 variation in surfaceheat fluxes. The influence of initial soil moisture on thesurface heat fluxes seems to be carried over into the24–48-h simulation period. The change of 0.1 in theinitial soil moisture in terms of its volumetric value cancause a change of about 200 W m22. Even for a rela-tively moist soil (see Fig. 7), the uncertainty in initialsoil moisture appears to have similar effect on the sur-face heat flux in the coupled model for a period of up


FIG. 6. Sensitivity of surface heat fluxes to the initial soil moisture for two dry points.

to, at least, 48 h. For the wet soil case, because theinitial soil moisture (0.36–39) is higher than the fieldcapacity (0.312 for the loamy sand at the wet point),the surface heat flux is only slightly changed with boththe 10% and 0.1 variations in initial soil moisture (notshown here), as expected. Thus, this perturbation in ini-tial soil moisture seems to primarily affect the surfaceenergy balance in dry and semidry areas.

Note that obtaining the initial soil moisture with anaccuracy of 10% at continental scales is beyond ourcurrent capability. First, there are no routine observa-tions of high-resolution soil moisture at large scales.Second, because the precipitation and surface radiationare not restricted (or assimilated) in the traditional four-dimensional data assimilation systems, the soil moisturefields suffer from model errors in radiation and precip-itation. For instance, the ECMWF operational globalmodel had problems related to large drifts in soil mois-ture, which, in turn, affected the precipitation forecasts(Viterbo and Courtier 1995). Thus, several major nu-merical prediction centers have suggested different ap-proaches in order to mitigate these problems (e.g., Bout-tier et al. 1993; Mitchell 1994; Smith et al. 1994).Among these methods is the idea of using observedprecipitation and surface radiation to drive an LSM inoffline mode to simulate long-term evolution of soilmoisture. This is appealing for regional model appli-cations, wherever high-resolution (spatial and temporal)

precipitation observations are available. The rationalebehind this idea is that, according to recent studies(Chen and Mitchell 1999; Calvet et al. 1998), the evo-lution of soil moisture in the surface layer and in thedeep root zone can be reasonably well captured by landsurface models, given accurate atmospheric and surfaceforcing conditions.

The future development of a soil moisture data as-similation system for the coupled MM5–LSM modelwill certainly depend on the region of intended appli-cation. If high-resolution precipitation and surface ra-diation are available, an offline soil moisture simulationsystem may be a good solution. In other regions, thelong-term solution is to explore the possibility of uti-lizing variational analysis to assimilate the surface soilmoisture with information obtained from remote sens-ing.

6. Surface layer parameterization and PBLscheme

The LSM is coupled to the MM5 model through thelowest atmospheric level, which is also referred to asthe surface layer. A surface layer parameterizationshould provide the surface (bulk) exchange coefficientsfor momentum, heat, and water vapor used to determinethe flux of these quantities between the land surface andthe atmosphere. The surface layer parameterization ba-


FIG. 7. Sensitivity of surface heat fluxes to the initial soil moisture for a semidry point.

ses its surface flux calculations on similarity theory,using a stability-dependent function (ch) and roughnesslength to determine the surface exchange coefficient forheat and moisture. This exchange coefficient is passedto the LSM from the PBL scheme, together with surfaceradiative forcing terms and precipitation rate. The LSMroutine returns to the PBL scheme the surface heat andmoisture fluxes for calculation of the boundary layerflux convergence, which contributes to atmospherictemperature and moisture tendencies. The LSM essen-tially replaces the current MM5 ground temperature pre-diction calculation that is based on the energy budgetat the ground.

Currently, in MM5, the surface exchange coefficientfor heat and moisture is formulated as

2k VaC 5 , (26)h

z za aln 2 c ln 2 cm h1 2 1 2[ ][ ]z z0m 0t

where cm and ch are expressed as in Eq. (5) of Oncleyand Dudhia (1995). However, MM5 now uses a lookuptable to fit the function instead of a polynomial fit forunstable conditions. The variable za is the height aboveground of the lowest computation level in the model(typically 35 m), k (0.4) is the von Karman constant,and Va is the wind speed at the lowest layer. The quan-tities z0m and z0t are the roughness lengths for momen-

tum and heat, respectively. Derived from Carlson andBoland (1978), z0t is defined as

1z 5 , (27)0t

ku* 111 2K za l

where Ka is the molecular diffusivity (2.4 3 1025 m2

s21) and u* is the friction velocity. Note that the for-mulation includes the molecular sublayer through theparameter zl (50.01 m), and this was previously onlyapplied to moisture in MM5. The LSM’s use of a skintemperature, which is defined at the surface not at z0m,as opposed to the previously used slab-layer groundtemperature, makes this formulation more consistentwith Monin–Obukhov similarity theory. The effect ofthe molecular sublayer is to act as a resistance to fluxes,and it is equivalent in this sense to the use of smallerthermal roughness lengths as proposed by several au-thors. For example, F. Chen et al. (1997) tested twoapproaches to specify the thermal roughness length: 1)assume the roughness length for heat is a fixed ratio ofthe roughness length for momentum, and 2) relate thisratio to the roughness Reynolds number as proposed byZilitinkevich (1995). Their 1D column model sensitivitytests suggested that the Zilitinkevich approach can im-prove the surface heat flux and skin temperature sim-ulations. A long-term test with the NCEP mesoscale Eta


FIG. 8. (a) Comparison of surface exchange coefficients calculated using three functions: 1) MM5ns, without usingthe sublayer parameterization; 2) MM5sb, with sublayer parameterization as in Eq. (27); and 3) the Zilitinkevichscheme. (b) Comparison of latent heat fluxes of these three schemes and the FIFE observations.

Model indicated that this approach can also reduce fore-cast precipitation bias.

Figure 8 shows a comparison of surface exchangecoefficients calculated by three methods. Without a sub-layer parameterization [i.e., z0m 5 z0t in Eq. (26)] thesurface exchange coefficients are overestimated and candouble the values compared to using a sublayer param-eterization [Eq. (27)]. Verified against the area-averagedlatent heat flux obtained from the FIFE observations(Betts and Ball 1998), the simulation without the sub-layer parameterization tends to overestimate the surfaceheat fluxes. Although the surface exchange coefficientsusing the Zilitinkevich scheme with parameter C 5 0.1are higher than that obtained using Eq. (27), the latentheat fluxes calculated by both methods are very closeand the MM5 sublayer scheme produces slightly betterresults during the day.

The land surface model interacts with MM5 throughits boundary layer scheme. Currently, the LSM has beencoupled with the nonlocal PBL scheme based on Troenand Mahrt (1986) and that has been implemented in theNCEP Medium-Range Forecast (MRF) model by Hongand Pan (1996). Compared with other nonlocal or high-order closure schemes, this PBL scheme has a similarperformance but is more efficient.

7. Summary

This paper has addressed a number of issues relatedto the implementation of an advanced land surface–hy-drology model in the Penn State–NCAR MM5 modelingsystem. The overall philosophy is to select a relativelysimple and sound land surface model, so that it can beefficiently executed for real-time atmospheric and hy-drologic applications at different scales. One conceptadopted here is that the LSM should be able to providenot only reasonable diurnal variations of surface heatfluxes (which is, of course, the primary function of anLSM), but also correct seasonal evolutions of soil mois-ture in the context of a long-term data assimilation sys-

tem. This, in turn, will ensure the accurate partitioningof available surface energy into latent and sensible heat-ing. The modified OSULSM (Chen et al. 1996) has beentested against field observations obtained for variousclimate and vegetation/soil conditions. This LSM per-forms adequately in the NCEP mesoscale forecast EtaModel, and a similar LSM is also used in the NCEPglobal data assimilation system and NCEP–NCAR rean-al ysis system. Considering the issue of the compatibilityof the soil moisture dynamic range, which represents adifficulty in utilizing one LSM’s soil moisture to ini-tialize another LSM that may have a different soil mois-ture climatology, employing this LSM should facilitatethe soil moisture initialization for the MM5 model. Thisis one of the major reasons that this LSM was chosenfor implementation in the MM5 model.

To meet the increasing demand for employing high-resolution mesoscale models (e.g., 1-km horizontalspacing), several high-resolution fields characterizingthe land surface conditions are utilized in the coupledMM5–LSM model. For instance, the 1-km resolutionland use and soil texture maps will help identify veg-etation/water/soil characteristics at finescales, and cap-ture the dynamics of the associated land surface forcing.This is important for inducing mesoscale atmosphericcirculations and modifying the development of the PBLand associated cloud and precipitation processes, asdemonstrated by many studies (Avissar and Pielke 1989;Chen and Avissar 1994a,b, etc.) Note that these landsurface forcings seem to not only influence the localcirculations but also to modify large-scale precipitationprocesses, even in some situations that are dominatedby strong synoptic forcing during warm seasons (Bel-jaars et al. 1996; Paegle et al. 1996). A monthly varyingclimatology 0.158 3 0.158 green vegetation fraction isalso introduced in the MM5–LSM coupled model torepresent the annual control of vegetation on the par-titioning of total vegetation among bare soil direct evap-oration in the surface layer and canopy uptake of waterin deep root zones. Given the important role of the land


use and soil texture in determining secondary parame-ters in the LSM, and the green vegetation fraction tothe seasonal evolution of evaporation and soil moisture,it is necessary to evaluate these surface fields in orderto further improve their characterizations in coupledmodels.

In this coupled MM5–LSM system, the secondaryvegetation and soil properties such as albedo, minimumstomatal resistance, and soil thermal/hydraulic conduc-tivity are determined by the spatial distribution of veg-etation and soil types. Some of these parameters aresimply compiled from the literature, and some of themare calculated from widely used formulations. The avail-able water capacity, which is the difference between thefield capacity and wilting point, is expanded to take intoaccount the surface heterogeneity in soil moisture fields.In the future, increasingly improved remote sensingtechniques can help the real-time specification of theseasonal evolution of some vegetation and soil param-eters such as albedo, LAI, roughness length, etc., in thecoupled model.

The soil thermal and hydraulic conductivities, beingcritical for heat and soil water transfer within the soillayers, are very sensitive to soil moisture changes. Thiswill certainly affect the soil water movement and runoffprocesses. Furthermore, the partitioning of surface ra-diation forcing into latent and sensible heat fluxes arealso significantly influenced by the initial soil moisturefields, especially in arid and semiarid climatic regions.Thus, although the current coupled MM5–LSM can uti-lize the soil moisture analyzed/forecasted by the NCEPglobal and regional operational assimilation/forecastsystems, it is necessary to establish an appropriate soilmoisture data assimilation system to improve the soilmoisture initialization at finescales. Such a system canbe based on an offline LSM and use the observed pre-cipitation and surface radiation as forcing conditions.

The issue of coupling the LSM to the surface layer(i.e., the lowest model level in the MM5) cannot beoverlooked. It involves the formulations for calculatingthe surface exchange coefficients and, in particular, thetreatment of the roughness length for heat/moisture. Theformulation, which includes the molecular sublayer, canreduce the surface exchange coefficient for the unstableregime and hence mitigate the overestimation of surfaceheat fluxes. Although the surface exchange coefficientscalculated from the MM5 molecular sublayer formu-lation are somewhat lower than those from the Zili-tinkevich scheme tested by F. Chen et al. (1997), thesurface heat fluxes obtained from these two formulationsare very similar and agree with the FIFE observations.

This paper documents a significant extension to thewidely used community MM5 modeling system, whichwill extend its applicability to long-term simulations byallowing soil moisture variations, as well as improveshort-range forecasts by allowing for initial land surfaceinhomogeneities that were previously neglected. By re-leasing this LSM to a broad user community, it is ex-

pected that its usage in scientific studies will greatlyincrease, and more expertise will be available for ver-ifying it in detail, as well as for suggesting and makingfuture improvements to the LSM and its coupling toMM5. Furthermore, since the MM5 system now has inplace the linkages for one LSM, it is expected that de-veloping options for other LSMs will be easier.

Acknowledgments. This research was funded by theU.S. Army Test and Evaluation Command through anInteragency Agreement with the National Science Foun-dation, the special funds from the National ScienceFoundation that have been designated for the U.S.Weather Research Program at NCAR, and DOE/ARMGrant DE-AI02-97ER62359. We express our appreci-ation to Dr. Thomas Warner for his support and sug-gestions. Many have helped to make the OSULSM partof MM5’s version 3 release in the summer of 1999.Yong-Run Guo provided a means of including vegeta-tion, soil, and vegetation fraction among the standardmodeling system datasets. Kevin Manning has helpedwith the extraction of soil moisture and temperature andother information from the NNRP and EDAS archiveddata, and with manipulating the Level II rainfall data.David Gill has adapted programs to interpolate new sur-face datasets onto the model grid. Wei Wang has facil-itated use of the LSM in the standard version 3 releaseavailable to MM5 users. Lynn Berry has prepared someof the figures presented in this paper.

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