Modeling of surface energy partitioning, surface temperature, and soil ...

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D06102, doi:10.1029/2003JD004089, 2004

Modeling of surface energy partitioning, surface temperature, and soil wetness in the Tibetan prairie using the Simple Biosphere Model 2 (SiB2) Zhiqiu Gao,1,2,3 Namyi Chae,1 Joon Kim,1,4 Jinkyu Hong,1,4 Taejin Choi,1 and Heechoon Lee1 Received 20 August 2003; revised 9 December 2003; accepted 15 December 2003; published 17 March 2004.

[1] This paper examines the performance of a stand-alone version of the Simple

Biosphere Model 2 (SiB2) to investigate the accuracy of the modeled surface energy components, surface effective radiative temperature, and soil wetness in a Tibetan short grass prairie from 15 July to 14 September 1998. During this monsoon period the mean canopy height and the leaf area index were 0.05 m and 0.5, respectively. The study site represented the prevailing conditions of the high Tibetan plateau with an average elevation of 4500 m. The model was initialized using in situ measurements and was driven by half-hourly atmospheric observations. The modeled values for the two months period were compared with micrometeorological field measurements made through the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME)-Tibet project. Results show that (1) when underestimating net radiation by 11%, the model overestimated sensible, latent, and soil heat fluxes by 8, 3, and 13% respectively; (2) the model generated warmer (colder) ground surface in daytime INDEX TERMS: 0315 (nighttime); and (3) soil wetness was estimated reasonably. Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 2447 Ionosphere: Modeling and forecasting; 2753 Magnetospheric Physics: Numerical modeling; 3210 Mathematical Geophysics: Modeling; 3322 Meteorology and Atmospheric Dynamics: Land/atmosphere interactions; KEYWORDS: land surface processes, SiB2, Tibetan Plateau Citation: Gao, Z., N. Chae, J. Kim, J. Hong, T. Choi, and H. Lee (2004), Modeling of surface energy partitioning, surface temperature, and soil wetness in the Tibetan prairie using the Simple Biosphere Model 2 (SiB2), J. Geophys. Res., 109, D06102, doi:10.1029/2003JD004089.

1. Introduction [2] Land surface processes affect weather and climate mainly through the surface-atmosphere exchange of energy, momentum, and CO2 across the atmospheric boundary layer [Zhang et al., 1996; Collatz et al., 2000; Bounoua et al., 2002; Defries et al., 2002; Chen et al., 2003]. Climate simulations are especially sensitive to the diurnal and seasonal variation in surface partitioning of available energy into sensible and latent heat fluxes [e.g., Rowntree, 1991; Dickinson et al., 1991]. Land surface processes are also critical in regional and mesoscale atmospheric modeling [Chen and Dudhia, 2001; Sridhar et al., 2002]. The Tibetan plateau plays an important role in the cyclic development of Asian summer monsoon [Krishnamurti and Ramanathan, 1 Global Environment Laboratory and Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea. 2 Also at Chinese Academy of Meteorological Sciences, Beijing, China. 3 Now at Department of Meteorology, Naval Postgraduate School, Monterey, California, USA. 4 Also at National Laboratory for Atmospheric Modeling Research, Yonsei University, Seoul, South Korea.

Copyright 2004 by the American Geophysical Union. 0148-0227/04/2003JD004089$09.00

1982; Chen et al., 1985; Qian et al., 2003]. The objectives of the Global Energy and Water Cycle Experiment (GEWEX) include better understanding of land surface-atmosphere interactions on a continental scale through quantitative field measurements over such crucial land surfaces. An intensive observing period (IOP) was conducted on the Tibetan plateau through GEWEX Asian Monsoon Experiment (GAME) in 1998. Many researchers [Ishikawa et al., 1999; Wang et al., 1999; Gao et al., 2000a, 2000b, 2003; Kim et al., 2000a, 2000b; Tanaka et al., 2001] have reported the direct measurements of surface energy exchange at the flux sites in that campaign. One of the striking findings was the failure of closing the surface energy budget over the Plateau at all the flux measurement stations. These sites were characterized by flat, homogeneous prairie with sparse shortgrass cover and sufficient fetch. Thus their energy budget was assumed to close with least amount of uncertainty. The encountered energy imbalance showed that the sum of the observed fluxes of sensible heat (H), latent heat (LE), and soil heat (G) amounted on average to only 70% of the observed net radiation (Rn). The cause of this energy imbalance is still uncertain and deserves further investigations [Kim et al., 2000b]. [3] The atmosphere, vegetation, and soil system are dynamically coupled through the physical processes that

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produce transport of thermal energy and water mass across the interface, so physically based modeling is an important tool for studying the coupled system [Sridhar et al., 2002]. In this study we simulated energy partitioning, surface temperature, and soil wetness over the plateau with a stand-alone version 2 of the Simple Biosphere Model (SiB2) [Sellers et al., 1996a, 1996b]. We used the micrometeorological data collected at the BJ surface flux station located in the central part of the Tibetan prairie during GAME-Tibet IOP in 1998 (http://monsoon.t.u-tokyo.ac.jp/ tibet/data/iop/pbltower/naqu.html). The SiB2 was originally designed for the use with atmospheric general circulation models (GCMs). Randall et al. [1996] compared the results of simulations using SiB2 coupled to a GCM against those of a control simulation that used a bucket surface hydrology model. They found that the SiB2 produced a warmer and drier surface and atmospheric boundary layer than the control run, resulting in increased surface H and decreased LE over the continents. The negative results of the test of SiB2 coupled with GCMs or mesoscale models could be due to (1) specific properties of the atmosphere above the surface change in response to variations in surface fluxes [Wang and Eleuterio, 2001], (2) the complication of isolating problems caused by deficiencies of SiB2 from those caused from deficiencies in other parts of the models, and (3) the lack of high-resolution spatial and temporal observations. Zhang et al. [1996] adopted an off-line SiB2 to simulate the global land surface fluxes of energy and CO2 using the European Center for Medium-Range Weather Forecasts (ECMWF) data assimilation products to prescribe the forcing variables. They found that SiB2 produced lower LE and larger H than those of ECMWF reanalysis data, partly due to large canopy resistant term explicitly formulated by SiB2 and possible precipitation differences between the SiB2 forcing and the ECMWF data. Colello et al. [1998] examined SiB2’s ability to predict changes in temperature, hydrologic state, energy partitioning, and CO2 fluxes over a tall-grass prairie throughout the growing season. When properly calibrated to the site specifics, SiB2 accurately simulated the observed responses of this ecosystem. Without calibration, simulation biases of SiB2 for H and LE were 0.95 and 1.12, respectively. [4] SiB2, a sufficiently popular operational model, is and will be continue to be significant for surface flux estimations because of its extensive use in numerical models and its applicability to a large body of climatological data from conventional observations. Furthermore, these fluxes over a large region will eventually be estimated from satellite data. So the present comparison of SiB2 and the eddy covariance method for estimation of surface fluxes will hopefully form a basis for regionalization of land surface processes and upscaling techniques. [5] Unlike most of the prior studies, the present test attempted to evaluate the performance of SiB2 for very sparse vegetation with leaf area index (LAI) of less than unity during the monsoon period in Tibet. The field measurements at BJ flux station during the GAME-Tibet IOP in 1998 provided a unique opportunity to test SiB2. The objectives of this study are to present the modeled results by SiB2 and to compare them with direct measurements of radiation components, energy budget components,

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surface temperature, and soil wetness under unique environmental conditions.

2. Materials and Methods 2.1. Field Measurements [6] The field experiment was conducted at BJ site near Naqu (91
900E, 31
370N, 4580 m above sea level) during the GAME-Tibet IOP from late May to mid September in 1998. The BJ site was located in a flat prairie with sufficient fetches to all directions except north, where the station camp was established with low hills behind. Vegetation cover was short grasses with canopy height of less than 0.05 m and LAI of less than 0.5. Soil at the site was predominantly sandy silt loam. Details on the instruments and various data processing techniques are provided at the Web site: http://monsoon.t.u-tokyo.ac.jp/tibet/data/iop/pbltower/doc/ naqu-fx.txt. [7] Fluxes of sensible heat (H) and latent heat (LE) were measured by eddy covariance using a triaxial sonic anemometer (CSAT3, Campbell Scientific Inc.), a krypton hygrometer (KH20, Campbell Scientific Inc.) and a finewire thermocouple. These instruments were mounted at 2.85 m above ground facing prevailing wind directions. Sampling rate was 20 Hz, and the raw data were collected for postdata processing. Appropriate corrections were made for nonzero mean vertical velocity, flux loss owing to sensor separation (0.15 m) between sonic anemometer and hygrometer, and density variation owing to simultaneous transfer of H and LE [Webb et al., 1980]. Detailed information can be found in the work of Kim et al. [2000a] and T. Choi et al. (Measurements of turbulent exchange of heat and water vapor over the Tibetan plateau using variance and eddy covariance methods, submitted to Boundary-Layer Meteorology, 2004, hereinafter referred to as Choi et al., submitted manuscript, 2004). Because of the breakage of the sonic anemometer, eddy covariance data are not available from May through mid July. Choi et al. (submitted manuscript, 2004) estimated H and LE on the basis of flux variance technique, but the data are limited to selected daytime conditions. Hence the SiB2 simulation was made only from 15 July to 14 September, when the direct eddy covariance data are available. [8] Net radiation was measured at 1.5 m above ground with CNR1 radiometer (Kipp & Zonen, Holland), which measures incoming short-wave/long-wave and upwelling short-wave/long-wave radiation separately. The thermal effects owing to sensor temperature were taken into account in calculating long-wave radiation components. The surface skin temperature was computed from the measure outgoing long-wave radiation with a downward facing radiometer of CNR-1. The Stefan-Boltzmann law was used with the infrared emissivity of 0.9 [Garratt, 1992, pp. 292]. The volumetric water content of the surface soil layer (0 – 0.1 m) was measured by two soil moisture reflectometers (CS615, Campbell Scientific, Inc.). Soil heat flux was measured with two heat transducers (HFT3, Campbell Scientific, Inc.) buried 0.05 m below the ground. The heat storage above the transducers was calculated from the time variation of soil temperatures (at two depths and six locations) with their soil water contents. The forcing data for SiB2 were obtained from slow-response meteorological sensors deployed at the

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study site. These measurements included: radiation components, profiles of wind speed, air temperature and humidity, precipitation, soil temperatures, and soil moisture contents. All the instruments were calibrated before and after the experiment and the changes in calibration coefficients were taken into consideration in data processing. More detailed information on instrumentation and data processing can be found in the work of Kim et al. [2000a].

Table 1. Modified Morphological and Aerodynamic Parameters Used in SiB2 Parameter Z2 Z1 V Dr s6 LT N z0 D C1 C2

2.2. Flux Computations [9] Eddy fluxes of sensible heat and latent heat were computed as [e.g., Kaimal and Finnigan, 1994]: ð1Þ

LE ¼ Lrw0 q0 ;

ð2Þ

where r, Cp, and L are the density of air (kg m3), the specific heat of air (J kg1 K1), and latent heat of vaporization (J kg1), respectively. Terms w0, T0, and q0 are the fluctuations of vertical wind component (m s1), air temperature (K), and specific humidity, respectively. [10] With no other sources/sinks of heat in the soil at our site, the surface soil heat flux (G0) was calculated as: ð3Þ

where G1 is the soil heat flux measured at 0.05 m below the soil surface (W m2); Cs is the volumetric heat capacity of soil (2.42  106 J m3 K1) [Stull, 1988]; Dz0 = 0.05 m; and Ts is the mean temperature of 0 – 0.5 m soil layer, calculated from the temperatures measured at 0.015 and 0.04 m depth. [11] Net radiation (Rn) can be obtained by Rn ¼ DSR þ DLR  OSR  OLR;

Description

Unitsa Value

height of canopy top m 0.05 height of canopy bottom m 0.005 canopy cover fraction 0.45 root depth m 0.3 High-temperature stress factor, respiration K 288 2 2 total leaf-area index m m 0.5 canopy greenness fraction 0.43 canopy roughness length m 0.001 canopy zero plane displacement m 0.035 1 bulk boundary layer resistance coefficient (s m ) 42 ground to canopy air-space resistance coefficient 80

a

H ¼ rCp w0 T0

G0 ¼ G1 þ Cs Dz0 @Ts =@t;

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ð4Þ

where DSR and DLR are downward short- and long-wave radiation and OSR and OLR are upwelling reflected shortwave radiation and long-wave radiation emitted by surface, respectively. 2.3. SiB2 Model 2.3.1. Overview [12] The SiB2 is a land surface parameterization scheme where a set of physically based formulations and parameters describe the turbulent transfer of heat and mass in vegetation canopy. Sellers et al. [1996a] described the detailed structure of SiB2, which has 11 prognostic physical state variables and calculates the energy budget components. The radiation model in SiB2 is the two-stream approximation for the canopy-ground system. The formulas to calculate H and LE were given by Sellers et al. [1996a, Table 4]. Mention G using force-restore method here. 2.3.2. Parameter Setting [13] The SiB2 requires as input the soil and vegetation types. The vegetation on the Tibetan prairie was dominated by C3 short-grass species. Therefore the category was defined as ‘‘agricaulture/C3 grassland’’ in short vegetation (biome type 9) [Sellers et al., 1996b, Table 2]. We obtained time-invariant vegetation parameters from Sellers

Note that ‘-’ denotes unitless.

et al. [1996b, Table 5]. Here the values of total leaf area index, canopy greenness fraction, and vegetation cover fraction were based on our field measurements at the BJ site for the entire experimental period (Table 1). The mixture of sandy loam soil, pebbles, and roots characterized the surface soil over the prairie on the plateau. We therefore selected the soil type ‘‘sandy loam’’ from Sellers et al. [1996b, Table 4]. [14] On the basis of our measurements, top and bottom heights of the short-grass canopy were estimated on average to be 0.05 and 0.005 m, respectively. Accordingly, the default values of the related parameters for the biome type 9 in SiB2 were replaced. Subsequent changes in the associated aerodynamic parameters in SiB2 were taken into account by recomputing canopy roughness length, zero plane displacement, and the resistance coefficients for bulk boundary layer and ground to canopy airspace (Table 1). The depth of total soil layer was 1.5 m of with three soil sublayers defined as surface layer (D1 = 0.02 m), root zone (D1 + D2 = 0.15 m), and recharge zone (D3 = 1.0 m). The depth of root zone was modified from the default value of 1 – 0.15 m on the basis of the field measurements of soil and plant samples, resulting in D3 of 1.0 m for the recharge zone. Some parameters of SiB2 are very sensitive. Our sensitivity study shows that onedegree change of the optimal temperature of canopy photosynthesis, for example, could significantly change stomatal conductance and thus produce a significantly different latent heat flux. We changed the original value (328 K) of high-temperature half-inhibition factor (s6) for short vegetation (biome type 9) from Sellers et al. [1996b, Table 5] to be 288 K because the temperature in the region never reach 328 K for our research period. 2.3.3. Forcing Data [15] The SiB2 model requires six meteorological forcing variables: downward short-wave radiation, downward long-wave radiation, vapor pressure, air temperature, wind speed, and precipitation. These forcing data were provided from the in situ measurements at the BJ flux station on a half-hourly basis. The simulation period was from 15 July to 14 September, which included monsoon and postmonsoon periods in 1998 (Figure 1). As the summer monsoon progressed, all the forcing data did not show significant seasonal variations until the postmonsoon period in September. The maximum (and mean) values of downward short-wave radiation (DSR) and downward long-wave radiation (DLR) were 1192 (and 233) Wm2

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Figure 1. Temporal variation of the atmospheric forcing quantities: short-wave downward radiation, long-wave downward radiation measured at 1.5 m height, vapor pressure, air temperature and absolute horizontal wind speed measured at 3.5 m height, and precipitation at a half-hourly basis. and 388 (and 324) Wm2, respectively. The mean values of vapor pressure, air temperature, and wind speed at 3.5 m were 8.3 hpa, 282.2 K, and 2.7 ms1, respectively, during the simulation period. All the forcing data showed large diurnal variations, particularly with rain events that were of convective nature during the monsoon season. In our comparison analysis, data from these rainy periods were excluded. [16] The aerodynamics of SiB2 is very sensitive to the input wind and reference height [Sellers et al., 1996b]. Our reference height was determined to be 3.5 m height because at which vapor pressure, air temperature and wind speed were collected by slow response instruments as mentioned above. We used the data collected by slow instruments rather than those collected by fast response instruments because we considered the extensive use of SiB2 in GCMs or mesoscale models and its applicability in a large body of climatological data from conventional observations. 2.3.4. Initialization [17] Table 2 lists the initial conditions of the parameters used for the simulation of SiB2. Initial values of the four temperatures (for canopy, canopy air space, ground surface, and deep soil) and three soil wetness (at soil surface layer, root zone, and recharge zone) were directly determined from our field observations at the BJ station. Our sensitivity tests showed that the simulation results were not sensitive to the initialization of the aforementioned temperatures. However, water balance of SiB2 was very sensitive on a longterm basis to soil wetness at root and recharging zones, whereas soil wetness at the surface layer converged rapidly

to the measured values (within a few weeks) regardless of the initial value. 2.4. Statistical Analysis of the Simulation Results [18] The modeled results (Mi) from SiB2 were compared against the in situ field measurements (Oi), on the basis of three statistical analysis [e.g., Colello et al., 1998] as follows: Bias ¼

n X Mi  Oi

n

i¼1

SEE ¼

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP un u ðMi  Oi Þ2 ti¼1 n2

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX u n u ðMi  Oi Þ2 u u i¼1 ; NSEE ¼ u n u X t ðOi Þ2

ð5Þ

ð6Þ

ð7Þ

i¼1

Table 2. Initial Condition used in SiB2 Initial Parameter

Initial Value

Canopy temperature Ground surface temperature Deep soil temperature Canopy air space temperature Soil wetness Soil wetness at root zone Soil wetness at recharge zone

282 K 282 K 284 K 282 K 0.35 0.35 0.35

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Figure 2. Comparison of net radiation (Rn) modeled using SiB2 against direct measurements.

where bias is a slope in the 1:1 linear regression; n is the total number of data points (n = 1704); SEE is the standard error of the estimate; and NSEE is a normalized SEE, denoting an estimate of relative uncertainty.

3. Results and Discussion [19] When radiation and energy balance components were less than 100 Wm2, the measurement error became meaningful and these components became insignificant, so our analyses in this section do not include the results in these cases. Our analyses do not include the results for rain periods as mentioned above. [20] Actually, when we tried to compare the modeled results of energy components with direct measurements, energy imbalance was found in direct measurements, while the energy balance is always held in model. In previous research, only those field measurements with good energybalance closure were chosen for comparison research. For example, Sridhar et al. [2002] used the criterion 10 W m2 < Rn-LE-H-G < 10 Wm2 to filter the field measurements for comparison of the modeled results using the National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrolic Research Lab- Oregon State University (NOAH-OSU) land model [Ek et al., 2001] with the Oklahoma Atmospheric Surface-Layer Instruments System (OASIS) measurements. We could not use their method because the energy imbalance was significant over our site. Energy imbalance was also encountered at other sites during this period over the Tibetan Plateau [Tanaka et al., 2001; Bian et al., 2002]. Under this situation, in this section we present the modeled

results and compare them with the direct measurements in order to quantify the difference between the modeled results and direct measurements. 3.1. Radiation Balance Components [21] Net radiation (hereinafter referred to as Rn) is derived from the four components of radiation budget: incoming and outgoing components of short- and long-wave radiation. The two downward components were given as forcing data (Figure 1), whereas SiB2 simulated the two upward components. Consequently, any errors in outgoing short-wave radiation (hereinafter referred to as OSR) and outgoing long-wave radiation (hereinafter referred to as OLR) would result in proportional errors in Rn simulation. We plotted the modeled results of Rn, OLR, and OSR in Figures 2 – 4, respectively, where we divided the simulation period (63 days in 1998) into three 21-day periods in order to clarify figures. Figures 2 – 4 show that model closely captured the peak and the diurnal pattern of radiation components. [22] Scatterplots of the modeled Rn, OLR, and OSR against direct measurements are given in Figure 5. Figures 2 and 5a show that on average, Rn was underestimated by 11% with the squared correlation coefficient of 1.0. Figures 3 and 5b show that OLR was overestimated by 6%, and the squared correlation coefficient was 0.95. In SiB2, OLR was calculated via Stefan-Boltzmann law, where the default value of emissivity is unity. Figures 4 and 5c show that OSR was overestimated by 4%, and the squared correlation coefficient reached 1.0. Because OSR mainly depends on the reflectance of soil and canopy, we remained the default values of reflectance of soil and

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Figure 3. Comparison of upward long-wave radiation (OLR) modeled using SiB2 against direct measurements.

Figure 4. Comparison of upward short-wave radiation (OSR) modeled using SiB2 against direct measurements. 6 of 11

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Figure 5. Scatterplots of net radiation (Rn), upward long-wave radiation (OLR), and upward shortwave radiation (OSR) against direct measurements. canopy in order to do the absolute minimum tuning in our testing. 3.2. Energy Balance Components [23] Figures 6 – 8 show the time series of the sensible heat (H), latent heat (LE), and soil heat (G0) fluxes, respectively, obtained by direct measurements and modeled by SiB2. It is obvious that SiB2 generated consistent temporal variations of H, LE, and G0. The squared correlation coefficients are 0.88, 0.85, and 0.93. Scatterplots of the modeled H, LE, and G0 against direct measurements are given in Figure 9, which shows that SiB2 overestimated H, LE, and G0 by 8, 3, and 13%, respectively. 3.3. Surface Effective Radiative Temperature [24] Figure 10 shows the time series of the surface effective radiative temperature (Teff) obtained from direct measurements and modeled by SiB2. It is apparent that SiB2 overestimated Teff in daytime and underestimated Teff

in nighttime. Scatterplot of the modeled Teff against direct measurements is given in Figure 11, which shows that the squared correlation coefficient reached 0.95. 3.4. Soil Wetness [25] Figure 12a shows the time series of the soil wetness obtained by direct measurements in 0 – 0.1 m surface layer and those modeled by SiB2 in three layers: soil surface layer (0– 0.02 m), root zone (0.02 –0.30 m), and recharge zone (0.30– 1.0 m); Figure 12b shows the time series of the precipitation. We found that (1) both directly measured and modeled results responded to precipitation sensitively with the most striking case happened in the night of day of year (DOY) 210, when a thunderstorm made the greatest sudden change of wetness; (2) SiB2 overestimated soil wetness in surface layer and root zone during and after convective rain as in the periods of DOY 211– 218 and 240– 248; (3) the measured results were in close agreement with the modeled results for root zone; (4) soil wetness modeled for surface

Figure 6. Comparison of sensible heat flux (H) modeled using SiB2 against direct measurements. 7 of 11

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Figure 7. Comparison of latent heat flux (LE) modeled using SiB2 against direct measurements.

Figure 8. Comparison of soil heat flux (G0) modeled using SiB2 against direct measurements. 8 of 11

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Figure 9. Scatterplots of sensible (H) latent (LE) and soil (G0) heat fluxes modeled using SiB2 against direct measurements.

soil layer gradually decreased owing to evaporation from bare soil surface and the transpiration of canopy in clear periods of DOY 247 – 257; and (5) the averaged wetness modeled by SiB2 were 0.41, 0.50, and 0.49 for soil surface layer, root zone and recharge zone, respectively, while the measured mean wetness was 0.41 (with 0.44 porosity). 3.5. Statistical Analysis of the Simulation Results [26] Table 3 presents the three statistical indices bias, SEE, and NSEE for the whole modeled period. 3.6. Surface Flux Partitioning [27] If we let Re denote the residual energy, i.e., Rn  H  LE  G0 = Re for the direct measurements, Table 4

lists the energy components on average for the period from 14 July to 14 September 1998. According to both direct measurements and modeled results, the latent heat was the largest consumer of incoming energy in the monsoon season, as it is well known, which has a great influence not only on energy distribution but also on water conditions.

4. Summary and Conclusions [28] The GEWEX/GAME program is being implemented to understand the role of the Asian monsoon in the global energy and water cycle and to improve the simulation and seasonal prediction of Asian monsoon patterns and regional

Figure 10. Comparison of surface effective radiative temperature (Teff) modeled using SiB2 against direct measurements. 9 of 11

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Table 3. Computed Bias, Standard Error of the Estimate (SEE), and Normalized Standard Error of the Estimate (NSEE) of Net Radiation (Rn), Sensible (H), Latent (LE) Heat Fluxes, Soil Heat Flux (G0), Surface Effective Radiative Temperature (Teff), and Soil Wetness (W) at BJ Flux Site in the Tibetan Prairie From 15 July to 14 September During GAME-IOP 1998 Rn H LE G0 Teff W

Bias

SEE

NSEE

22 Wm2 5 Wm2 2 Wm2 7 Wm2 2.0 K 0.03

43 Wm2 19 Wm2 49 Wm2 42 Wm2 5.1 K 0.08

0.11 0.30 0.33 0.35 0.01 0.15

Figure 11. Scatterplots of surface effective radiative temperature (Teff) modeled using SiB2 against direct measurements.

water resources. (http://www.ihas.nagoya-u.ac.jp/game/). The present research tried to meet the needs of this program. [29] In this paper we have reported our modeled results for energy budget components, surface effective radiative temperature and soil wetness in a Tibetan prairie from 15 July to 14 September 1998. Daily patterns of net radiation (Rn), sensible heat flux (H) and latent heat flux (LE), soil heat flux (G0), surface effective radiative temperature (Teff), and soil wetness (W) modeled using SiB2 are consistent to the direct measurements. Like direct measurements, the modeled daily patterns of sensible heat flux

(H) and latent heat flux (LE), and soil heat flux (G0) followed that of net radiation (Rn). [30] Our results show that, compared with the direct measurements, SiB2 generated warmer (colder) ground surface in daytime (nighttime). However, SiB2 generated a reasonable estimate of soil wetness. [31] We compared the SiB2 and the eddy covariance techniques for estimation of sensible and latent heat fluxes using the data collected during the above time period. We found that the SiB2 overestimated on average the sensible and latent heat fluxes by 8 and 3%, respectively. SiB2 overestimated soil heat flux by 13%. The inconsistence of model outputs and the direct measurements implied that

Figure 12. Comparison of soil wetness at three layers, modeled using SiB2 against direct measurements. 10 of 11

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Table 4. Average Energy Components Measured and Modeled at the Naqu Site in the Tibetan Prairie From 15 July to 14 September 1998 Rn, Wm2 H, Wm2 LE, Wm2 G0, Wm2 Re, Wm2

Measurements

Modeled Values

164 30 81 16 37

142 35 83 24 0

further efforts are still needed for both experimental and model research. [32] Acknowledgments. The postdoctoral fellowship of Zhiqiu Gao was supported by the Ministry of Science and Technology (NRL Program). The GAME-IOP field experiment was supported by the Ministry of Environment (G-7 Project) of Korea and by National Space Development Agency of Japan. The National Natural Science Foundation of China partly supported this study through the project of Regionalization of Air-Land Exchange Parameters over Tibetan Plateau. Our sincere thanks go out to all the GAME-Tibet members for their dedication and encouragement. We are very grateful to anonymous reviewers for their careful review and valuable comments, which led to substantial improvement of this manuscript.

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N. Chae, T. Choi, J. Hong, J. Kim, and H. Lee, Global Environment Laboratory and Department of Atmospheric Sciences, Yonsei University, Seoul 120-749, South Korea. Z. Gao, Department of Meteorology, Naval Postgraduate School, 58439 Dyer, Room 263, 306 Park Ave., Monterey, CA 93943, USA. (zgao@nps. navy.mil)

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