AN INTERCOMPARISON STUDY OF TWO LAND SURFACE MODELS USING A 1-D MODEL AND FIFE MEASUREMENTS

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 26: (26) Published online 29 March 26 in Wiley InterScience ( DOI: 1.12/joc.1266 AN INTERCOMPARISON STUDY OF TWO LAND SURFACE MODELS USING A 1-D MODEL AND FIFE MEASUREMENTS KIRAN ALAPATY a, and DRAGUTIN T. MIHAILOVIC b, * a Division of Atmospheric Sciences, National Science Foundation, 421 Wilson Boulevard, Suite 775, Arlington, VA 2223, USA b Faculty of Agriculture, University of Novi Sad, Novi Sad, Serbia Received 6 September 24 Revised 26 July 25 Accepted 6 August 25 ABSTRACT Two land-surface parameterization schemes ( (Interactions Soil-Biosphere-Atmosphere) and (Land-Air- Parameterization-Scheme)) of differing complexity were implemented into a 1-D atmospheric boundary layer model and evaluated utilizing the special observational data collected during the summer of 1987 from the FIFE measurements. Results from two case studies were analyzed during which boundary layer processes dominated the lower atmospheric processes. In the first case study (6 June 1987), we found that differences in the estimation of stomatal resistance in the two land-surface models (LSMs) triggered many of the differences in results obtained using the two LSMs. In the second case study (11 July 1987), only minor differences existed between respective net radiation and stomatal resistance estimations, leading to similar boundary layer structures. Diagnostic results indicate that in both case studies growth rates of and depths of atmospheric boundary layers and vertical profiles of virtual potential temperatures, water vapor mixing ratio, and horizontal winds are very similar in and. Our future studies are directed at evaluating these two schemes using a regional climate version of the WRF model. Copyright 26 Royal Meteorological Society. KEY WORDS: environmental models; land-surface scheme; atmospheric boundary layer; soil moisture; stomatal resistance 1. INTRODUCTION Land surface processes play an important role in boundary layers and influence other atmospheric processes at local- to global-scales. Ever increasing availability of computer resources and traditional land-based observations as well as satellite measurements of various land-surface parameters led to the development and refinement of land-surface models (LSMs). Thus, during the past decade many LSMs were developed and these are being refined to improve model simulations. One of the major studies undertaken to study the performance of about 3 LSMs is the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) in which several LSMs designed for use in climate modeling and weather prediction are being evaluated. Results from Phase 2 of the PILPS by Henderson-Sellers et al. (1996) indicated that while some models are consistent with observations, there remains a large range of differences among models and that many models diverged greatly from observations. Further results suggested that individual land-surface schemes capture specific aspects of the complex system with reasonable accuracy but no one scheme captures the whole system satisfactorily and consistently (Alapaty et al., 1997a). One shortcoming in that approach may be the usage of different boundary layers and radiation formulations with differing assumptions and deficiencies in each of the host models, which can contribute to differing degrees of errors in the modeled surface and boundary layer processes. One approach for evaluating LSMs * Correspondence to: Dragutin T. Mihailovic, Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovica 8, 21 Novi Sad, Serbia; guto@polj.ns.ac.yu Permanent Affiliation: University of North Carolina, USA. Copyright 26 Royal Meteorological Society

2 916 K. ALAPATY AND D. T. MIHAILOVIC would be to implement them into a single model and test them for conditions that are typically dominated by boundary layer processes. This way, one could analyze the differences resulting from using different LSMs in a single host model and their effects on other processes. For regional climate changes, air pollution simulation and other environmental studies, proper representation of surface and boundary layer processes is very crucial. This is because differences in simulated surface temperatures resulting from using different LSMs lead to differing, for example, estimates of biogenic emissions (a major precursor for ozone formation) influencing nonlinearity of atmospheric chemistry. Further, differences in the simulated depths of boundary layers affect pollutant concentrations adversely through vertical dilution by turbulent mixing. Hence, proper representation of surface and boundary layer processes is very important in meteorological, environmental and air pollution modeling studies. (Pleim and Xiu, 1994; Alapaty and Mathur, 1998). Our overall objective is to study the effects of using two LSMs of differing complexities on several meteorological parameters simulated at the surface and in the boundary layer. Our objective is accomplished by using a one-dimensional boundary layer model in which two LSMs use one-atmosphere paradigm. The advantage of using one-atmosphere paradigm is that the same physical formulations are used for all other processes to test different LSMs. This way, respective prognostic and diagnostic parameters can be studied bringing out their similarities and differences, which can improve our understanding of the behavior of LSMs tested in a numerical simulation Brief description of model 2. MODEL SETUP We used a three-dimensional windowed model developed by Alapaty et al. (1997b) to study the effects of different representations of soil-vegetation-atmosphere interactions on predicted boundary layer parameters using a turbulent kinetic energy (TKE) scheme. The windowed model has 35 vertical sigma layers and 3 3 grid cells in the horizontal layers. Essentially, this model is a one-dimensional boundary layer model in which predictions are made only at the central grid cell for all vertical layers. The eight horizontal grid cells surrounding the computational grid cell in each layer assume the same meteorological characteristics as those of the central/computational grid cell. Modular models like this one offer several advantages: they can be used efficiently for one-dimensional simulation and diagnostic studies, they can be used on any computer system, and they can easily be implemented into any two- or three-dimensional model without major modifications. To accomplish the objective of our study, we have considered two widely used LSMs of differing complexities ( and ). In this section are briefly described the governing equations, parameterization schemes and boundary layer scheme used in this study. The general forms of the dynamic and thermodynamic equations used in the model are u v T q v = u u x v u y z (u w ) f ( v g v ) (1) = u v x v v y z (v w ) + f ( u g u ) (2) = u T x v T y z (w T ) (3) = u q v x v q v y z (w q v ) (4) where u and v are the eastward and northward components of the wind (m s 1 ), respectively; z is the altitude (m); f is the Coriolis parameter (s 1 ); T is the temperature (K); and q v is the mixing ratio of water vapor (kg kg 1 ); u w, v w, w T and w q v are the kinematic turbulent fluxes for momentum (m2 s 2 ),heat

3 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D 917 (m s 1 K) and moisture (m s 1 kg kg 1 );andu g and v g are the eastward and northward geostrophic winds (m s 1 ), respectively. We have implemented the, a sophisticated soil-vegetation parameterization scheme, initially developed by Mihailovic (1996). The 1-D model already has an option to use another sophisticated land-surface model, developed by Noilhan and Planton (1989) and Jacquemin and Noilhan (199). The 1-D model is configured with two different surface and subsurface configurations according to the land-surface schemes. Both schemes are briefly described in Appendices A and B. The lower boundary layer (surface layer) is parameterized based on the similarity theory suggested by Monin and Yaglom (1971) using the nondimensional stability parameters m, h and q, for momentum, heat, and moisture, respectively. Turbulent sensible heat fluxes are computed using the relation given by S hf = u θ where u is friction velocity (m s 1 );andθ is the scale for temperature. Latent heat fluxes are computed from the land-surface schemes, where bare-ground evaporation and evaporation from transpiring canopies and the wet parts of the canopies (due to dew formation and/or rainfall interception) are estimated (Appendices AandB). In our study, we used a scheme based on TKE and its dissipation rate that is already implemented and well tested in the 1-D and a 3-D mesoscale model (MM5) by Alapaty et al. (1997b) and Alapaty and Alapaty (1999). The prognostic equations used in this scheme to explicitly calculate the TKE (E) and its dissipation rate (ε) are those suggested by Yamada and Mellor (1975). This TKE scheme is often called a one-and-ahalf-order closure scheme in which the unknown terms in the prognostic equations are parameterized in terms of local gradients of dynamic and thermodynamic parameters. The coefficient of vertical eddy diffusivity is calculated from the ratio of E and ε. Surface layer similarity profiles (Businger et al., 1971) are used for obtaining boundary conditions for the prognostic equations for E and ε, while for the mixed layer the E ε scheme is used. For further details, the reader is referred to Alapaty et al. (1994). The coefficients of eddy diffusivity for momentum and heat can be written as K m = c 2E 2 m (z/l) and K h = K m (5) ε h (z/l) where c 2 is used as a constant though it was originally formulated as a function of friction velocity and E by Detering and Etling (1985); m and h are nondimensional functions for momentum and heat; and L is the Monin-Obukhov length (m) (Businger et al., 1971). In the free atmosphere, turbulent mixing is parameterized using the formulation suggested by Blackadar (1979) in which vertical eddy diffusivities (m 2 s 1 ), K z, are functions of the Richardson number and wind shear in the vertical. This formulation can be written as 2 Rc Ri K z = K + S(kl) (6) Ro where K is the background value (1 m 2 s 1 ); S is the vertical wind shear (s 2 ); k is the von Karman constant; l is the characteristic turbulent length scale (1 m); Rc is the critical Richardson number; and Ri is the Richardson number, i.e. Ri = g θ v θ v S 2 z where g is acceleration due to gravity (m s 2 ) and θ v is virtual potential temperature (K) Numerical simulations and synoptic conditions We have performed two numerical case studies using the special measurements available from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) (Sellers et al.,

4 918 K. ALAPATY AND D. T. MIHAILOVIC 1992). In each of these case studies, we performed two numerical simulations for a period of 12 h using the and the LSMs. The starting time of simulations in the first and second cases respectively is 7 LT 6 June 1987 and 7 LT 11 July These two days are referred to as golden days in the literature because boundary layer processes dominated the lower atmospheric structure over the FIFE region. The FIFE site is located near Manhattan, KS, covering a km area where grass prairie is the predominant vegetation. Various initial conditions used in the simulations are given in Alapaty et al. (1997b). Measured surface fluxes (sensible and latent heat) are available for every 3-min time interval while vertical profiles of temperature, winds, and humidity obtained using slow ascent balloons are available for every 1 m vertical altitude intervals for various hours during each day of the observations. Initial meteorological conditions (7 LT) for the two FIFE cases have indicated the presence of the remnants of a nocturnal jet with a maximum wind speed located at about m above ground level (AGL). Up to the altitude of about m, water vapor mixing ratios showed very weak vertical gradients. Virtual potential temperatures indicated stable lapse rates. ervations at later time periods indicated that during the daytime evolution of the ABL, the nocturnal jet had dissipated, leading to decreased wind shear within the ABL. erved water vapor mixing ratio and virtual potential temperature profiles (estimated from temperature, dew point temperature, and pressure) at different periods during daytime indicated the presence of well-mixed layers in the ABL. In the July case, clouds were observed at about 16 LT causing changes in the land surface and boundary layer processes. Since the 1-D model is not configured with a cumulus cloud convection option, cloud-induced processes could not be simulated in the present model simulations. 3. RESULTS AND DISCUSSION 3.1. The 6 June 1987 case study During this first case study period (which is the first Intensive Field Campaign (IFC) of the FIFE), clear sky conditions were observed during the entire day. Thus, comparing measured surface heat fluxes with the model estimates will reveal effects of approximations in the land-surface model formulations. Temporal variation of measured (available at every 3-min intervals from 7 to 19 LT) and modeled surface fluxes is shown in Figure 1(a c). Figure 1 shows temporal variation of measured and predicted sensible heat fluxes. In general, fluxes predicted by are in very good agreement with measurements. With, a peak in the sensible heat flux is overestimated by about 3 W m 2. Also, from the late afternoon hours to the end of the simulation (19 LT), predictions are much closer to observations. Though the initial conditions and the formulation used to represent surface layer processes are the same in both the simulations, the slight differences among the predictions indicate the effects of different representations of surface and subsurface processes. In the, the onset of negative sensible heat fluxes happens an hour before than that in and the observations. Our experience (Alapaty et al., 1997b) indicated that this early reversal of sign of the sensible heat flux led to the early collapse of the atmospheric boundary layer (ABL) with several ABL schemes. However, with the TKE-based ABL schemes, such as the one used in the present simulations, results were found to be in better agreement with observations. Predicted latent heat fluxes (Figure 1) by are very close to observations while underestimated latent heat fluxes with a maximum difference of about 45 W m 2. The remaining component in the surface energy budget is the soil heat flux, i.e. heat flux due to conduction between soil layers. Temporal variation of measured (estimated as remainder of surface energy budget) and modeled soil heat flux between soil layers 1 and 2 is shown in Figure 1. Positive values indicate heat conduction from soil layer 2 to 1 (i.e. soil layer 2 is warmer than layer 1), which is typical during late night through morning hours. During daytime, typical to this type of case study, soil heat fluxes should be negative (i.e. heat is lost to soil layer 2 from layer 1). The initial soil layer temperatures and moisture in and are exactly the same while formulations in the prognostic equations for these variables are different, there exists some differences among the measurements and model estimates. However, the trend is very similar in all of them. Temporal variation of depth of ABL from the measurements (LIDAR-based) and from and (based on the predicted TKE profiles) are shown in Figure 2. Though both the and

5 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D Sensible Heat Flux (W m -2 ) Latent Heat Flux (W m -2 ) Soil Heat Flux (W m -2 ) Figure 1. Temporal variation of measured and modeled sensible heat flux, latent heat flux, and soil heat flux for the 6 June 1987 FIFE case study. All measurements are available at 3-min time intervals underestimated the ABL depths, the magnitude differences between measurements and modeled depths were within the uncertainty in the measured depths. Importantly, the growth rate of ABL starting from 8 LT in and is very similar to that in the measurements. Further, both the and show very similar ABL depths with time. Since the estimated turbulent sensible heat fluxes are slightly higher in compared to that in, its effect is reflected in the simulated depths of the ABL. Alapaty et al. (1997a) showed that stomatal resistance is one of the key parameters that influences the surface latent heat fluxes and hence the depth of the ABL. Figure 2 shows the temporal variation of modeled stomatal resistance in and. Note that we have used the same value of minimum stomatal resistance (available from FIFE measurements) in both the simulations (i.e. and ), and both and use Jarvis-type formulations to estimate stomatal resistance. However, uses a slightly different version of the Jarvistype formulation compared to that in, resulting in differences in the estimation of stomatal resistance. Since estimated stomatal resistance in is higher by about 4 s m 1 compared to that in during convective conditions in ABL, it resulted in a lower estimation of transpiration fluxes as shown in Figure 1 and hence in higher root zone soil moisture (for soil layer-2). Thus, differences in surface sensible and latent heat fluxes in and are mostly due to differences in stomatal resistance, which influences other processes and parameters.

6 92 K. ALAPATY AND D. T. MIHAILOVIC 16 3 Top of the Boundary Layer AGL (m) Stomotal Resistance (s m -1 ) Figure 2. Temporal variation of measured and modeled boundary layer, and modeled stomatal resistance for the 6 June 1987 FIFE case study Of interest to meteorological and air quality simulation modelers is a better prediction of near-surface winds, temperature, and moisture. This is because these parameters can affect the estimation of surface turbulent fluxes of thermodynamic parameters and chemical species, biogenic emission rates, and chemical reactions. We compare surface observations of temperature, water vapor mixing ratio, u- and v-winds (eastward and northward components of horizontal wind) with corresponding modeled values from and. Since the lowest level in the model is located at about 1.9 m above ground level (AGL), predicted winds, temperature, and water vapor mixing can be directly compared to the measurements made at 1 m AGL. Figure 3 shows the temporal variation of near-surface air temperature from the measurements and from and. Both the schemes result in very similar temperatures and are similar to those in observations. Similarly, the water vapor mixing ratio (Figure 3) also shows some minor differences, but in general, they both follow observations. Trends in the predicted zonal winds (Figure 3) in both simulations follow the observations closely until 14 LT after which observations and modeled zonal winds deviate by about a maximum of 3 m/s. Note that the zonal winds are very weak both in observations as well as in simulations, ranging from 1 to 4 m s 1. Strong meridional winds (Figure 3(d)) as observed are well simulated by and. In general, differences between and are very insignificant. We now compare observed and predicted thermodynamic and dynamic profiles. Figure 4(a d) show the vertical variation in virtual potential temperature (θ v ) obtained from observations at different hours and corresponding model simulations using and. Initial conditions (7 LT) indicate that the depth of the stable boundary layer could be about 3 m AGL (Figure 4). Also, the θ v profile between 3 m and 14 m indicates the presence of the previous day s ABL, the residual boundary layer. At 1 LT (Figure 4), both and have very similar θ v profiles and by 13 LT (Figure 4) both schemes show very similar results except that both are potentially cooler than observations by about K. Though simulated surface sensible hat fluxes are slightly higher (by about 1 5 W m 2 ) than in observations, depth of the ABL (inferred from the altitude of inversion) is shallow in and compared to that in observations (inferred from observed θ v and from Figure 4). Cooler temperatures in and are probably due to unaccounted advection processes happening at the FIFE site. Similar cooler temperatures (by about 2 K) and shallower ABL depths in and are persistent even at 16 LT (Figure 4(d)) compared to respective observations. Modeled and observed vertical variations of water vapor mixing ratio at different times are presented in Figure 5(a d). At 7 LT (Figure 5; initial conditions), the effects of the stable boundary layer s stratification on mixing ratio are shown. At an altitude of about 1 m, a sharp vertical gradient indicates the presence of very dry air in the free atmosphere and is indicative of the depth of the previous day s ABL.

7 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D Near-Surface Air Temperature (K) Near-Surface Mixing Ratio (g/kg) Eastward Wind Velocity (m s -1 ) Northward Wind Velocity (m s -1 ) (d) Figure 3. Temporal variation of measured and modeled near-surface (1.5 m AGL) air temperature, water vapor mixing ratio, eastward horizontal wind velocity, and (d) northward horizontal wind velocity for the 6 June 1987 FIFE case study At 1 LT (Figure 5), like in θ v profiles, modeled and observed mixing ratios are very similar to each other. At 13 LT (Figure 5), both and indicate drier and shallow ABLs compared to the observations. Though surface latent and sensible heat fluxes at 13 LT simulated by are relatively lower than those in, dilution of fluxes through a relatively shallow ABL in leads to relatively humid ABLs compared to (Figure 5). At 16 LT (Figure 5(d)), both and have very similar mixing ratios and are comparable to the observations. Though observations indicate a well-mixed profile for the water vapor mixing ratio (at 13 and 16 LT), both schemes are unable to produce such a well-mixed ABL. Predictions of the horizontal winds in a one-dimensional model can be very sensitive to the prescribed geostrophic wind profiles. In all our simulations, we prescribed geostrophic wind profiles that were used by Alapaty et al. (1997b). Since horizontal pressure gradients are not readily available, we used a qualitative procedure to estimate temporally evolving geostrophic winds. Vertical variation in the u-wind (eastward component) obtained from observations and model simulations are shown in Figure 6(a d). Presence of a nocturnal jet at 7 LT is evident from the u-wind profile between the surface and the 1 m altitude; the core of the jet is located at about 4 m AGL. At 1 LT (Figure 6), observations still indicate the

8 922 K. ALAPATY AND D. T. MIHAILOVIC 2 (7 LT) 2 (1 LT) Virtual Potential Temperature (K) Virtual Potential Temperature (K) 2 (13 LT) 2 (16 LT) Virtual Potential Temperature (K) (d) Virtual Potential Temperature (K) Figure 4. Vertical variation of measured and modeled virtual potential temperature at 7, 1, 13, and (d) 16 LT for the 6 June 1987 FIFE case study presence of a nocturnal jet while in and it is weakened due to strong vertical mixing. At 13 LT (Figure 6), observations indicate a strong vertical u-wind shear within the ABL while and indicate a relatively well-mixed ABL. Best agreements between, and observations are found at 16 LT (Figure 6(d)). Vertical variation in the v-wind (northward component) obtained from observations and model simulations is shown in Figure 7(a d). The presence of the nocturnal jet is somewhat evident (7 LT) in the lower altitudes of the atmosphere. Interestingly, observed v-winds do not show the signs of remnants of the nocturnal jet at 1 LT unlike in the u-winds (Figure 6). In general, observed v-winds are more uniform with altitude and corresponding simulations with and are in general agreement with observations. To summarize the differences between and simulations, most of the differences in prognostic variables (Temperature, mixing ratio, and horizontal winds) are very minor and compare with observations very well as seen in the respective figures. The diagnostic parameters also show similar signatures. However, the relative magnitudes of subprocesses (surface evaporation verses transpiration) are different in and. These differences can be attributed mostly to differences in the formulations estimating the stomatal resistance.

9 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D (7 LT) (1 LT) Water Vapor Mixing Ratio (g Kg -1 ) Water Vapor Mixing Ratio (g Kg -1 ) 2 2 (13 LT) (16 LT) Water Vapor Mixing Ratio (g Kg -1 ) (d) Water Vapor Mixing Ratio (g Kg -1 ) Figure 5. Vertical variation of measured and modeled water vapor mixing ratio at 7, 1, 13, and (d) 16 LT for the 6 June 1987 FIFE case study 3.2. The 11 July 1987 case study During this day, the second IFC of the FIFE, clear sky conditions were observed until afternoon. Cloudcamera observations indicated overcast sky conditions after 1 LT. Because the current version of our model does not consider processes related to cloud formation and its effects on atmospheric radiation and surface processes, model predictions after 1 LT may not be comparable to observations. The temporal variations in the surface fluxes estimated by the model and from the observations are shown in Figure 8(a c). Figure 8 shows the temporal variation in the measured and estimated sensible heat fluxes from using the two land-surface schemes. In general, both and follow observations with a noticeable difference during evening hours consistent with the results from the earlier case study. Figure 8 compares measured surface latent heat fluxes with those obtained using and. Unlike in the earlier case, slightly overestimates latent heat fluxes by about 5 35 W m 2 until noon. Note that the differences in latent heat fluxes between the and is much smaller as compared to that in the earlier case study. After 1 LT, estimated latent heat fluxes deviate greatly from the measurements owing to the exclusion of cloud effects in the model. Figure 8 shows the temporal variation of observed and modeled soil heat flux. Even though the initial soil layer temperatures and moisture in and

10 924 K. ALAPATY AND D. T. MIHAILOVIC 2 2 (7 LT) (1 LT) Eastward Wind Velocity (m s -1 ) Eastward Wind Velocity (m s -1 ) 2 2 (13 LT) (16 LT) Eastward Wind Velocity (m s -1 ) (d) Eastward Wind Velocity (m s -1 ) Figure 6. Vertical variation of measured and modeled eastward horizontal wind velocity at 7, 1, 13, and (d) 16 LT for the 6 June 1987 FIFE case study are exactly the same while formulations in the prognostic equations for these variables are different, the two schemes adjust soil layers temperatures quite rapidly (in less than 5 min of simulation time), leading to differing solutions during the initial spin-up time. However, the trend is very similar in all of them. We now present a temporal variation of the depth of the ABL, near-surface (lowest layer) temperature, and stomatal resistance in Figure 9(a c), respectively. The growth rate of the ABL is well captured in as well as in and is comparable to that in observations (Figure 9). Also, near-surface air temperatures in and are very close to each other and observations. The maximum difference between estimated stomatal resistances (Figure 9), in this case study, is only about 15 s m 1 while it is about 5 s m 1 in the earlier case study. This result is consistent with the formulation of transpiration fluxes in and. It is interesting to note that even though the stomatal resistance formulation used in and are very similar, their estimation differed significantly between two cases. Figure 1 shows the vertical variation in observed virtual potential temperature, θ v, (initial conditions). Stable lapse rates are present at this time (7 LT) with an indication of the ABL depth being about 3 4 m. Model simulations using and and observations of θ v at 9 LT are shown in Figure 1. In and, the modeled mean θ v of the ABL is warmer compared to observations by about.5 K. However, at 12 LT θ v in and indicate potentially cooler ABLs and shallow ABL depths (Figure 1) compared to observations.

11 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D (7 LT) 2 (1 LT) Northward Wind Velocity (m s -1 ) Northward Wind Velocity (m s -1 ) 2 (13 LT) 2 (16 LT) Northward Wind Velocity (m s -1 ) (d) Northward Wind Velocity (m s -1 ) Figure 7. Vertical variation of measured and modeled northward horizontal wind velocity at 7, 1, 13, and (d) 16 LT for the 6 June 1987 FIFE case study This trend continues even at 1 LT (Figure 1(d)). The presence of superadiabatic lapse rates, a typical feature during the daytime near the surface, is evident starting from 9 LT in observations. This feature is well simulated by and at 12 and 1 LT. The stronger vertical gradient (inversion) present at higher altitudes indicates the approximate altitude of the top of the mixed layer in each θ v profile. The θ v profile at 1 LT (Figure 1(d)) contains the influence of dynamic and thermodynamic processes related to the reported cloud activity. Also, a stable-type of ABL has evolved in the observed θ v owing to cloud processes, which cannot be simulated with the current model version. Vertical profiles of observed water vapor mixing ratio (q v ) at different hours are shown in Figure 11(a d). At 12 and 1 LT, q v profiles indicate a uniform distribution within the mixed layer with large gradients in the surface layer. erved and modeled eastward wind velocity profiles at different times are shown in Figure 12(a d). Initial conditions (Figure 12) indicate the presence of remnants of a nocturnal jet in the lower altitudes in the initial conditions (7 LT). The presence of vertical wind shear in the ABL is an indication of weaker vertical mixing. Owing to the presence of cloud processes at 1 LT (Figure 12(d)), both and could not simulate strong vertical wind shear as observed. erved and modeled northward wind velocity profiles at different times are shown in Figure 13(a d). Initial conditions (Figure 13) also indicate the

12 926 K. ALAPATY AND D. T. MIHAILOVIC 2 Sensible Heat Flux (W m -2 ) Latent Heat Flux (W m -2 ) Soil Heat Flux (W m -2 ) Figure 8. Temporal variation of measured and modeled sensible heat flux, latent heat flux, and soil heat flux for the 11 July 1987 FIFE case study. All measurements are available at 3-min time intervals presence of remnants of a nocturnal jet in the lower altitudes in the v-wind filed. Compared to u-winds, vertical wind shear in the ABL is weaker in observations. In general, insignificant difference is present in and simulations of horizontal winds. 4. CONCLUDING REMARKS In this paper, we considered and discussed the intercomparison of two land surface schemes using a 1-D ABL model and FIFE measurements for 6 June and 11 July We used the model developed by Alapaty et al. (1997b) to study the effects of two different representations of soil-vegetation-atmosphere interactions on predicted boundary layer parameters using a TKE scheme. To accomplish the objective of our study, we have considered two widely used LSMs of differing complexities, i.e. (Interactions Soil-Biosphere- Atmosphere) and (Land-Air-Parameterization-Scheme) of differing complexity. There were some general features that were associated with each of the schemes in both the case studies. These are (1) simulated direct evaporation fluxes that are higher in while they are significantly lower in, (2) transpiration fluxes are lower in both the simulations in compared to those in the,

13 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D Top of the Boundary Layer AGL (m) Near-Surface Air Temperature (K) Stomotal Resistance (s m -1 ) Figure 9. Temporal variation of measured and modeled boundary layer depth, near-surface (1.5 m AGL) air temperature, and modeled stomatal resistance for the 11 July 1987 FIFE case study and (3) a time lag in the reversal of the sign in the surface sensible heat flux is present in during evening hours. Estimated stomatal resistance was found to differ because of feedback and differences in the implementation of the Jarvis-type formulation. It was found that most of the differences in prognostic variables (temperature,mixing ratio,and horizontal winds) are very minor and compare well with observations. However, some of the estimated surface parameters that have dominant control on the evolution of the ABL differed in each of the LSMs. This study does, however, define an expected level of uncertainty associated with these land-atmosphere interaction schemes. There is no definite conclusion about the superiority of any of the considered schemes. We will establish that conclusion in a future regional climate study using a 3-D model such as the WRF model. ACKNOWLEDGEMENTS The research work described in this paper has been funded by the University at Albany under a contractual agreement CR and by the Serbian Ministry for Science and Environmental Protection under the project No. ON The authors would like to thank Dr S.T. Rao for supporting this research project. They are also appreciative of Miss Slavica Malinovic and Miss Zorica Podrascanin for their support in the technical preparation of the manuscript.

14 928 K. ALAPATY AND D. T. MIHAILOVIC 2 (7 LT) 1 (9 LT) Virtual Potential Temperature (K) Virtual Potential Temperature (K) 14 (12 LT) 2 (1 LT) Virtual Potential Temperature (K) (d) Virtual Potential Temperature (K) Figure 1. Vertical variation of measured and modeled virtual potential temperature at 7, 9, 12, and (d) 1 LT for the 11 July 1987 FIFE case study APPENDIX A The structure of the Land-Air-Parameterization-Scheme () The net radiation absorbed by the canopy and soil is assumed to be partitioned into sensible heat, latent heat, and storage terms, as T f R nf = λe f + H f + C f (A1) T g R ng = λe g + H g + C g (A2) where R nf,r ng are net radiation (W m 2 ); λ is latent heat of vaporization (J kg 1 ); λe f, λe g are evapotranspiration flux (W m 2 ); H f, H g are sensible heat flux (W m 2 ); C f, C g are heat capacity (J K 1 m 2 ); and T f, T g are surface temperature (K). The subscripts f and g refer to the upper-level canopy and soil, respectively. The deep soil temperature (K), T d, is calculated from the equation (Mihailovic et al., 1999) R ng = λe g + H g + 365π C g 2 T d (A3)

15 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D (7 LT) 2 (9 LT) Water Vapor Mixing Ratio (g Kg -1 ) Water Vapor Mixing Ratio (g Kg -1 ) 2 (12 LT) 2 (1 LT) Water Vapor Mixing Ratio (g Kg -1 ) (d) Water Vapor Mixing Ratio (g Kg -1 ) Figure 11. Vertical variation of measured and modeled water vapor mixing ratio at 7, 9, 12, and (d) 1 LT for the 11 July 1987 FIFE case study The fluxes of sensible and latent heat from the canopy and ground are represented by electrical analog models in which the fluxes are proportional to potential differences (in temperature or vapor pressure) and inversely proportional to resistances, which are equivalent to the inverse integrals of conductances over a specified length scale. For example, an aerodynamic resistance is calculated by integrating the inverse of a turbulent transfer coefficient between the reference points. The heat fluxes in (Mihailovic and Kallos, 1997; Pielke, 22; Mihailovic et al., 24) may be written as: λe f = ρc p γ [e (T f ) e a ] ( Wf r b + 1 W f r b + r c ) where ρ, c p are the density and specific heat of air (kg m 3,Jkg 1 K 1 ); γ is the psychrometric constant (mb K 1 ); e (T f ) is saturated vapor pressure at temperature T f (mb); e a is canopy air space vapor pressure (mb); W f is canopy wetness fraction; r b is bulk canopy boundary layer resistance (s m 1 );andr c is bulk canopy stomatal resistance (s m 1 ). (A4) λe g = ρc p γ α s e ( Tg ) ea r surf + r d (A5)

16 93 K. ALAPATY AND D. T. MIHAILOVIC 2 2 (7 LT) (9 LT) Eastward Wind Velocity (m s -1 ) Eastward Wind Velocity (m s -1 ) 2 (12 LT) 2 (1 LT) Eastward Wind Velocity (m s -1 ) (d) Eastward Wind Velocity (m s -1 ) Figure 12. Vertical variation of measured and modeled eastward horizontal wind velocity at 7, 9, 12, and (d) 1 LT for the 11 July 1987 FIFE case study where α s is a factor to correct for soil dryness (Mihailovic et al., 1995), e ( Tg ) is saturated vapor pressure at temperature T g (mb), r surf is soil surface resistance (s m 1 ),andr d is aerodynamic resistance between soil surface and canopy air space (s m 1 ). H f = 2 (T f T a ) ρc p H g = r b ( Tg T a ) r d ρc p (A6) (A7) where T a is canopy air space temperature (K). The latent and sensible heat fluxes from the soil and canopy combine to give the total surface fluxes, which are transferred from the canopy air space to the reference height, z r, and are given by λe f + λe g = (e a e r ) ρc p r a γ (A8)

17 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D (7 LT) 2 (9 LT) Northward Wind Velocity (m s -1 ) Northward Wind Velocity (m s -1 ) 2 2 (1 LT 1 (12 LT) Northward Wind Velocity (m s -1 ) (d) Northward Wind Velocity (m s -1 ) Figure 13. Vertical variation of measured and modeled northward horizontal wind velocity at 7, 9, 12, and (d) 1 LT for the 11 July 1987 FIFE case study H f + H g = (T a T r ) ρc p r a (A9) where e r is vapor pressure at the reference height (mb), r a is aerodynamic resistance (s m 1 ),andt r is air temperature at the reference height (K). The prognostic equations for the water stored on the canopy (m), w f,is w f = P f E wf ρ w (A1) where ρ w is the density of liquid water (kg m 3 ); P f is the amount of water retained on the canopy (m s 1 ); and E wf is the evaporation of water from the wetted fraction of canopy (kg m 2 s 1 ). The parameterization of the volumetric soil water content is based on the concept of the three-layer model, i.e. ϑ 1 = 1 [ P 1 F 1,2 1 ] ( ) Eg + E tf,1 R R 1 d 1 ρ w (A11)

18 932 K. ALAPATY AND D. T. MIHAILOVIC ϑ 2 ϑ 3 = 1 [ F 1,2 F 2,3 E ] tf,1 R 1 d 2 ρ w = 1 d 3 [ F2,3 F 3 R 3 ] (A12) (A13) where ϑ i is the volumetric soil moisture content in the ith layer (m 3 m 3 ); P 1 is the infiltration rate of precipitation into the upper soil moisture store (m s 1 ), d i is the thickness of the ith soil layer; F i,i+1 is the water flux between i and i + 1 soil layer store (m s 1 ); F 3 is the gravitational drainage flux from recharge soil moisture stores (m s 1 ); E tf,1 and E tf,2 are the canopy extraction of soil moisture by transpiration from the first and the second soil layer, respectively (kg m 2 s 1 ); R is the surface runoff and R 1 is the subsurface runoff from the ith soil layer (m 3 m 3 ). APPENDIX B The structure of the interactions soil-biosphere-atmosphere () scheme There are five prognostic equations for deep soil temperature (K),T d, deep volumetric soil water content (m 3 m 3 ), ϑ 2, surface soil/vegetation temperature (K), T s, top volumetric soil water content (m 3 m 3 ), ϑ g, and interception water storage (kg m 2 s 1 ), w r, T s T s ϑ g ϑ 2 w r = C T (R n H λe) 2π τ (T s T d ) (B1) = 1 τ (T s T d ) (B2) = C 1 ( ) C 2 ( ) Pg E g ϑg ϑ geq ; ϑg (B3) ρ w d 1 τ = 1 ρ w d 2 ( Pg E g E tr ) C 3 d 2 τ max [., (ϑ 2 ϑ fc )] ; ϑ 2 ϑ sat = σ c P r (E v E tr ) R r ; w r w rmax (B5) (B4) where R n is net radiation at the surface (W m 2 ); H and λe are sensible and latent heat fluxes (W m 2 ); C T is the inverse of thermal capacity of a particular type of soil (K J 1 m 2 );andτ is the number of seconds in a day; ϑ g and ϑ 2 are volumetric soil water contents for the two soil layers (m 3 m 3 ); C 1 and C 2 are the dimensionless functions of soil type and its water retention characteristics; C 3 is the coefficient for gravitational drainage; d 1 and d 2 are thicknesses of the two soil layers (m); P g is the flux of rain water reaching the soil surface after interception and runoff (kg m 2 s 1 ); E g is the evaporation at the soil surface (kg m 2 s 1 ); ϑ geq is the volumetric soil water content when gravity balances the capillary forces (m 3 m 3 ); ϑ fc is the field capacity volumetric soil water content (m 3 m 3 ); ϑ fc is the saturated volumetric soil water content; and E tr is the transpiration rate (kg m 2 s 1 ); σ c is the vegetation cover in fractional units; P r is the precipitation rate at the top of the vegetation (kg m 2 s 1 ); E v is the evaporation rate from the wet parts of the canopy (kg m 2 s 1 ); R r is the runoff rate from canopy interception reservoir (kg m 2 s 1 );and w rmax is the maximum water intercepted by the canopy. The turbulent fluxes are calculated by means of the classical aerodynamic formulae. For sensible heat flux H = ρc p C H V r (T s T r ) (B6) where V r is the wind speed at the reference height (m s 1 );andc H is the drag coefficient depending on the thermal stability of the atmosphere. The water vapor flux (kg m 2 s 1 ), E, is the sum of the evaporation from

19 AN INTERCOMPARISON OF THE TWO LAND SURFACE MODELS USING 1-D 933 the soil surface (i.e. E g ) and the vegetation (i.e. E v ): λe = (λe g + λe v ) E g = ρc p C H V r (1 σ c ) [ ] h u q sat (T s ) q r [ ] E v = ρc p C H V r h u qsat (T s ) q r (B7) (B8) (B9) where q sat is the saturated specific humidity at the reference height (g kg 1 ) at the temperature T s ; q r is the atmospheric specific humidity at the reference height (g kg 1 );andh u is the relative humidity at the ground surface that is related to the superficial soil moisture (Mahfouf and Noilhan, 1991). Direct evaporation from the wet fraction of the foliage covered by intercepted water (kg m 2 s 1 ), E r, as well as the transpiration E r of the remaining part of the leaves are calculated as E g = σ c (w r /w rmax ) 2/3 r a [ qsat (T s ) q r ] E tr = σ c 1 (w r /w rmax ) 2/3 r a + r surf [ qsat (T s ) q r ] (B1) (B11) All other details about parameterization in this scheme can be found in Noilhan and Planton (1989), Jacquemin and Noilhan (199), Mahfouf and Noilhan (1996) and Boone et al. (1999). REFERENCES Alapaty K, Mathur R Effects of atmospheric boundary layer mixing representations on vertical diffusion of passive and reactive tracers. Meteorology and Atmospheric Physics 69: Alapaty K, Alapaty M Development and testing of an E-ε scheme using the MM5. In The 9th Penn State/NCAR MM5 Users Workshop, Boulder, Colorado, June Alapaty K, Raman S, Madala RV Investigation of the role of the boundary layer processes in an active monsoon using a mesoscale model. Boundary-Layer Meteorology 67: Alapaty K, Raman S, Niyogi DS. 1997a. Uncertainty in the specification of surface characteristics: a study of prediction errors in the boundary layer. Boundary-Layer Meteorology 82(3): 473. Alapaty K, Pleim J, Raman S, Niyogi DS, Byun DW. 1997b. Simulation of atmospheric boundary layer processes using local- and nonolocal-closure schemes. Journal of Applied Meteorology 36: Blackadar AK Modeling pollutant transfer during daytime convection. Preprints, 4th Symposium on Turbulence, Diffusion and Air Pollution. American Meteorological Society: Reno, NV; Boone A, Calvet J-C, Noilhan J The inclusion of a third soil layer in a land surface scheme using the force-restore method. Journal of Applied Meteorology 38: Businger JA, Wyngaard JC, Izumi Y, Bradley EF Flux-profile relationship in the atmospheric surface layer. Journal of the Atmospheric Sciences 28: Detering HW, Etling D Application of E-ε turbulence model to the atmospheric boundary layer. Boundary-Layer Meteorology 33: Henderson-Sellers A, McGuffie K, Pitman AJ The project for intercomparison of land-surface parameterization schemes (PILPS): 1992 to Climate Dynamics 12: Jacquemin B, Noilhan J Sensitivity study and validation of land surface parameterization using the HAPEX-MOBILHY data set. Boundary-Layer Meteorology 52: Mahfouf J-F, Noilhan J Comparative study of various formulations of evaporation from bare soil using in situ data. Journal of Applied Meteorology 9: Mahfouf J-F, Noilhan J Inclusion of gravitational drainage in a land surface scheme based on the force-restore method. Journal of Applied Meteorology 35: Mihailovic DT Description of a land-air parameterization scheme (). Global Planetary Change 13: Mihailovic DT, Kallos G A sensitivity study of a coupled soil-vegetation boundary-layer scheme for use in atmospheric modeling. Boundary-Layer Meteorology 82: Mihailovic DT, Rajkovic B, Lalic B, Dekic LJ Schemes for parameterizing evaporation from a non-plant-covered surface and their impact on partitioning the surface energy in land-air exchange parameterization. Journal of Applied Meteorology 34: Mihailovic DT, Kallos G, Arsenic ID, Lalic B, Rajkovic B, Papadopoulos A Sensitivity of soil surface temperature in a forcerestore equation to heat fluxes and deep soil temperature. International Journal of Climatology 19: Mihailovic DT, Alapaty K, Lalic B, Arsenic I, Rajkovic B, Malinovic S. 24. Turbulent transfer coefficients and calculation of air temperature inside the tall grass canopies in land-atmosphere schemes for environmental modeling. Journal of Applied Meteorology 43: Monin AS, Yaglom AM Statistical Fluid Mechanics, vol. I. MIT Press: Cambridge, Massachusetts,

20 934 K. ALAPATY AND D. T. MIHAILOVIC Noilhan J, Planton S A simple parameterization of land surface processes for meteorological models. Monthly Weather Review 117: Pielke RA Sr. 22. Mesoscale Meteorological Modeling, 2nd edn. Academic Press: New York, 676. Pleim JE, Xiu A Development and testing of a surface flux planetary boundary layer model with explicit soil moisture parameterization for applications in mesoscale models. Journal of Applied Meteorology 34: Sellers PJ, Hall FG, Asrar G, Strebel DE, Murphy RE An overview of the first international satellite land surface climatology project (ISLSCP) field experiment (FIFE). Journal of Geophysical Research 97: Yamada T, Mellor CL A hierarchy of turbulence closure models for planetary boundary layers. Journal of the Atmospheric Sciences 31: