JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D21106, doi:10.1029/2004JD004767, 2004

Turbulent exchange of heat, water vapor, and momentum over a
Tibetan prairie by eddy covariance and flux variance measurements
Taejin Choi,1 Jinkyu Hong,1 Joon Kim,1 Heechoon Lee,1 Jun Asanuma,2Hirohiko Ishikawa,3 Osamu Tsukamoto,4 Gao Zhiqiu,5 Yaoming Ma,6 Kenichi Ueno,6
Jiemin Wang,7 Toshio Koike,8 and Tetsuo Yasunari9
Received 12 March 2004; revised 28 July 2004; accepted 1 September 2004; published 6 November 2004.

[1] Land-atmosphere interactions on the Tibetan Plateau are important because of theirinfluence on energy and water cycles on both regional and global scales. Flux variance andeddy covariance methods were used to measure turbulent fluxes of heat, water vapor,and momentum over a Tibetan shortgrass prairie during the Global Energy and Water
Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME) in 1998. Under
unstable conditions during the monsoon period (July–September), the observed standard
deviations of temperature and specific humidity (normalized by appropriate scaling
parameters) followed the Monin-Obukhov theory. The similarity constants for heat CT andwater vapor Cq in their dimensionless functions of stability under a free convection
limit were both 1.1, unlike the differences (i.e., CT  Cq) reported in other studies. While
the transfer efficiency of heat and water vapor exchange generally agreed with theprediction from the Monin-Obukhov theory, momentum exchange was less efficient than
predicted. In comparison with the eddy covariance data, the flux variance method (withCT = Cq = 1.1) underestimated both heat and water vapor fluxes by <5%. When the eddy
covariance data were absent, the flux variance method was used for gap filling theseasonal flux database. To estimate latent heat flux during the premonsoon period in June,
CT/Cq was approximated as rTq (where rTq is a correlation coefficient for the fluctuationsof temperature and water vapor) because of the sensitivity of Cq to changes in soil
moisture conditions. The dramatic changes in the Bowen ratio from 9.0 to 0.4 indicate theshift of energy sources for atmospheric heating over the plateau, which, in turn, resulted
INDEX TERMS:
in the shift of turbulent exchange mechanisms for heat and water vapor.
1878 Hydrology: Water/energy interactions; 0315 Atmospheric Composition and Structure: Biosphere/
atmosphere interactions; 3307 Meteorology and Atmospheric Dynamics: Boundary layer processes; 3379
Meteorology and Atmospheric Dynamics: Turbulence; KEYWORDS: Tibetan Plateau, eddy covariance, flux
variance, Monin-Obukhov theory
Citation: Choi, T., et al. (2004), Turbulent exchange of heat, water vapor, and momentum over a Tibetan prairie by eddy covarianceand flux variance measurements, J. Geophys. Res., 109, D21106, doi:10.1029/2004JD004767.

1. Introduction[2] The Tibetan Plateau is characterized by high elevation(>4000 m) and vast area (10 in latitude and 25 in
1
Department of Atmospheric Sciences, Yonsei University, Seoul,
Republic of Korea.
2
Department of Geosciences, Tsukuba University, Tsukuba, Japan.
3
Disaster Prevention Research Institute, Kyoto University, Kyoto,
Japan.
4
Faculty of Science, Okayama University, Okayama, Japan.
5Chinese Administration for Meteorological Sciences, Beijing, China.
6
School of Environment, University of Shiga Prefecture, Shiga, Japan.
7
Cold and Arid Regions Environmental and Engineering ResearchInstitute, Chinese Academy of Sciences, Lanzhou, China.
8
Department of Civil Engineering, University of Tokyo, Tokyo, Japan.
9
Hydrospheric Atmospheric Research Center, Nagoya University,
Nagoya, Japan.

Copyright 2004 by the American Geophysical Union.
0148-0227/04/2004JD004767$09.00

longitude). The plateau has been the subject of climate
research for several decades because of its topographic
characteristics and its influence on energy and water cycles
on both regional (e.g., Asian monsoon) and global (e.g., El
Niño) scales [e.g., Flohn, 1957; Yeh et al., 1957; Liu et al.,2003]. The onset of the Asian summer monsoon coincides
with the reversal of the meridional temperature gradient in
the upper troposphere south of the Tibetan Plateau, resulting
from the large temperature increases in May to June overEurasia and centered on the plateau [Li and Yanai, 1996].
[3] Much effort has been made to evaluate the strengthand distribution of atmospheric heat and moisture sources
over the plateau and its vicinity. Researchers have found
that: (1) the plateau is the major energy source by providing
sensible heat flux to the atmosphere before the onset of
monsoon [e.g., Li and Yanai, 1996]; (2) during the rainyseason, the latent heat released to the atmosphere is thedominant heat source over the eastern plateau, whereas

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sensible heat flux is comparable to latent heat flux over thewestern plateau [e.g., Chen et al., 1985]; (3) local recycling
through evaporation is important in the water budget over
the western plateau, whereas water vapor transport fromoutside is significant over the eastern plateau [e.g., Luo andYanai, 1983]; and (4) there is a consensus on the distributionof heat/moisture sources and the heating mechanism overthe plateau, but the magnitudes are conflicting. For instance,
Yeh and Gao [1979] obtained a vertically integrated meanheat source of 138 Wm2 over the western plateau in June,
whereas Chen et al. [1985] estimated it as 37 Wm2. Thislarge discrepancy was caused by the different drag coefficients used in the bulk method, resulting in different
sensible heat fluxes by a factor of 2 –3. Li et al. [2001]
reported a wide range of drag coefficients (i.e., 2.5  103
to 12  103) over the plateau despite the use of the samedata. Latent heat flux can be estimated as a residual of thesurface energy budget. However, this approach is prone toerror because significant lacks (10 – 40%) in the energy
budget closure on the plateau have been reported [e.g.,
Kim et al., 2001; Bian et al., 2002].
[4] The paucity of direct flux measurements has hindered
us from better understanding the role of the Tibetan Plateau in
the cyclic processes of energy and water and their potential
alteration associated with global change. Consequently, theGlobal Energy and Water Cycle Experiment (GEWEX)
Asian Monsoon Experiment (GAME) was organized andintensive field measurement campaigns were executed on theplateau in 1998 [e.g., Koike et al., 1999]. Since 2002, further
long-term flux measurements have been attempted throughthe Coordinated Enhanced Observing Period (CEOP, http://
monsoon.t.u-tokyo.ac.jp/ceop/). In these efforts, the eddy
covariance method has been used to quantify the seasonalchanges in surface energy partitioning.
[5] The objective of this paper is to investigate turbulent
exchange characteristics over a Tibetan prairie in terms offlux variance relations, and to document the seasonalchanges in surface energy partitioning. Both eddy covariance and flux variance methods were employed to measure
the turbulent fluxes from late May to September in 1998.
We have used flux variance when the eddy covariancemethod was not possible because of breakage of the sonicanemometer under the extreme environmental conditions on
the plateau. The validity of the use of flux variance is thus
tested against direct measurement by eddy covariance.
Basic characteristics of turbulence statistics are analyzed
to understand the mechanisms controlling the surface
exchange of energy and water. The flux data obtained fromthe two methods are combined to examine the seasonalvariation of energy partitioning associated with the monsoon. The gap-filled flux data have been documented in a
public domain database for further use in validating andcalibrating of various land surface models.

2. Materials and Method
2.1. Theoretical Considerations
2.1.1. Eddy Covariance Method[6] Vertical flux F of any scalar is based on the conservation equation. If the site is homogeneous and flat, andassuming stationarity and no sink/source for the scalar,then F can be obtained from the covariance between theD21106fluctuations of vertical wind velocity w and a mixing ratioof scalar c as below [e.g., Swinbank, 1951].
F ¼ ra w0 c0 ;ð1Þwhere ra is the air density (0.71 kg m3 at the study site),and the overbar and primes denote time averaging andfluctuations from the mean, respectively.
2.1.2. Flux Variance Method[7] On the basis of the Monin-Obukhov similarity theory,
standard deviations of any quantity, x such as w, longitudinal wind speed u, temperature T and specific humidity q
normalized by scaling parameters become universal functions of z/L as [e.g., Tillman, 1972]:
sx =x ¼ fx ð z=LÞ ¼ Cx1 ð1  Cx2 z=LÞ 1=3 ;*ð2Þwhere sx is the standard deviation of x, x* is the scaling
parameter ( w0 x00 /u*, where w0 x00 is the flux at the surface
and u* is the friction velocity), fx is the normalized
function, z is the measurement height, L is the Obukhov
length, and Cx1 and Cx2 are empirical constants. Positive(negative) sign corresponds to wind components (scalars).
As z/L approaches infinity (i.e., local free convection),
equation (2) can be simplified as [e.g., Wyngaard et al.,1971]:
sx =x* ¼ fx ð z=LÞ ¼ Cx ðz=LÞ 1=3 ;ð3Þwhere Cx(=Cx1C±1/3
x2 ) is a similarity constant. Taking T as x
and rearranging equation (3), sensible heat flux H isexpressed as [e.g., Katul et al., 1995].

H ¼ c p rasTCT

3=2 

kgz
T

1=2
;

ð4Þ

where cp is the specific heat capacity of dry air at constantpressure (=1005 Jkg1 K1), CT is a similarity constant for
heat, k is the von Kármán constant (=0.4), and g is theacceleration due to gravity.
[8] Water vapor flux is better estimated using a sitespecific similarity constant for water vapor Cq determined
under free convection limit than using values in the literature [Asanuma and Brutsaert, 1999];lE ¼ lra

rwq sq 0 0
CT sq 0 0
w T ¼ lrawT ;Cq s TrwT sTð5Þ

where rwT and rwq are the correlation coefficients for w0 and
T0, and w0 and q0, respectively.
[9] The value of CT seems universal and independent on
surface conditions, unlike that of Cq [e.g., Weaver, 1990; De
Bruin et al., 1993; Andreas et al., 1998]. The latter increases
pronouncedly as rTq decreases [Asanuma and Brutsaert,
1999]. The surface moisture condition at our study sitechanged dramatically as the summer monsoon progressed.
We therefore assume that CT does not change through theseason, whereas Cq changes with soil water availability.
[10] The term, rwT/rwq in equation (5) could be approximated by rTq when rwT/rwq < 1 (or 1/rTq when rwT/rwq > 1)

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D21106[Bink and Meesters, 1997; Katul and Hsieh, 1997]. Then,
we can rewrite equation (5) aslE ¼ lra

1 sq 0 0wT
rTq sTlE ¼ lra rTq

if rwT < rwq andð6aÞ

if rwT > rwq :ð6bÞ

sq 0 0wT
sT

Figure 1a shows the relationship between rTq (or 1/rTq) andrwT/rwq at the study site (BJ site), indicating that equations(6a) and (6b) hold. When quadrant analysis is applied toremove the likely effect of entrainment from the ABL top
on rTq, better relationship is obtained (Figure 1b).
2.1.3. Correlation Coefficients[11] The vertical transfer efficiencies for heat and water
vapor can be evaluated by their correlation coefficients, rwT
and rwq, respectively. The ratio of rwT to rwq represents therelative transfer efficiency. On the basis of the MoninObukhov similarity theory, rwx can be predicted using thecorresponding integral turbulence characteristics (ITC)[e.g., De Bruin et al., 1993]:
rwx ¼



w0 c0
z  1=3z 1=3¼ 1  Cx2
=Cw1 Cx1 1  Cw2
:
sw sx
L
L

ð7Þ

If x is scalar, the numerator is [1  Cx2(z/L)]1/3. Otherwise,[1  Cx2(z/L)]1/3 is used.
2.1.4. Drag Coefficients[12] Drag coefficients CD at the reference height (i.e., 10m) and under neutral stability (CDN10), can be evaluatedthrough the relation [e.g., Andreas et al., 1998],
CDN 10 ¼

k2½lnð10=z0 Þ 2;

ð8Þ

where z0 is the roughness length for momentum.
2.2. Site and Measurements
2.2.1. Study Site[13] The field experiment was conducted at a shortgrassprairie (BJ site) near Naqu (92.04E, 31.29N, 4580 m
above m.s.l.), Tibet in China. Soil was predominantly sandy
silt loam at the site, which was homogeneous and flat with afetch of >1 km for the prevailing wind directions. The flux
measurement was made from late May to mid-September in
1998. The rainy season started in late June. During thepremonsoon period the surface was dry and sparsely covered with short grasses. With the onset of the monsoon, thevolumetric soil water content remained >15% and theaverage grass height was about 0.05 m with grazing.
2.2.2. Flux Measurement[14] The eddy covariance measurement system consistedof a three-dimensional sonic anemometer (CSAT3, Campbell
Scientific, Inc.), a krypton hygrometer (KH20, Campbell
Scientific, Inc.), and a fine-wire thermocouple. Measurements were taken 2.85 m above the ground, and the separation between the sonic anemometer and the hygrometer was
0.15 m. The prevailing wind direction was from 135 to 225.
The sampling rate was 20 Hz and the raw data were saved on

Figure 1. Relationship between rTq (or 1/rTq) and rwT/rwq
at the study site from July to September in 1998: (a) withthe whole data set and (b) with reduced data set of thequadrant analysis.
a laptop computer connected to a data logger (CR9000,
Campbell Scientific, Inc.) for postprocessing. Half-hourly
averaged turbulence statistics were calculated and recordedon the data logger.
[15] Because of long and rough transportation to theexperimental site, the sonic anemometer was broken andwas not replaced until 14 July. During this period standard
deviations of temperature and humidity were measured
with fine wire thermocouples and a krypton hygrometer,
respectively. Because of a wide range of humidity, thepath length of krypton hygrometer was adjusted, and the
instrument was calibrated again in a low-pressure chamber
after the experiment.

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D21106considered nonstationary when NR  2 [Mahrt, 1998]. The
NR values for daytime averaged w0 T 0 and s2T in Figure 2
show that the stationarity requirement is generally met.

3. Results and Discussion

Figure 2. Variations of daytime-averaged nonstationarity
ratio NR for w0 T 0 and variance of temperature sT2 during
the monsoon period. Error bars indicate standard error ofmean.
[16] Other supporting micrometeorological measurements
made at the site included radiation components, soil heat
flux, soil temperature, soil water content, air temperatureand humidity, wind speed, and direction. All these slowresponse signals were sampled every 30 s. Average values
for 30-min periods were stored on the same data logger.
Electrical power was not available, and thus all the instruments and the data-logging system were operated through
12 VDC batteries that were recharged by solar panels [Kimet al., 2001].
2.2.3. Flux Corrections[17] The effect of coordinate rotation on fluxes was lessthan 1%. To further evaluate the contribution of lowfrequency eddies [e.g., Finnigan et al., 2003], half-hourly
averaged fluxes were compared against 2-hour averages andthe agreement was within 1%. Following Moore [1986],
fluxes were adjusted for the effects of path length averaging
and sensor separation, resulting in 5% increase in latent heat
flux. Further correction for the density variation was applied
to latent heat flux [Webb et al., 1980].
2.2.4. Stationarity Test
[18] To satisfy the stationarity assumption, the statisticalproperties of the measured time series should not changewith time. To evaluate the degree to which turbulent
statistics may violate this assumption, the nonstationarity
ratio (NR) is defined as [Mahrt, 1998]
NR

sbtw =RE;ð9Þ

where sbtw is the between-record standard deviation of
statistics (for this analysis, half-hourly
pﬃﬃﬃ raw data were divided
into four records), and RE (=swi/ J ) is the random error
estimate, where swi is the averaged within-record standard
deviation over all of the records, and J is the number ofsubrecord segments (six segments per record were used forthis study). While RE has the effect of random variability on
turbulence statistics only, sbtw may be affected by both
nonstationarity and random variability. Ideally, NR = 1 if the
turbulence statistics are stationary. In practice, data are

3.1. Turbulence Characteristics[ 19 ] The measured sw /u * is plotted with z/L in
Figure 3a. A normalized function of sw/u*(=Cw1(1 +Cw2jz/Lj)1/3) was determined, resulting in Cw1 = 1.12 andCw2 = 2.8. Also presented is the formulation of Kaimal andFinnigan [1994] (i.e., sw/u* = 1.25 (1 + 3jz/Lj)1/3), which
predicts consistently greater sw/u*. Similar results wereobtained from data collected 20 m above ground using
taller towers: one at the BJ site in 2002 and the other at Annisite in 2003 [Hong et al., 2004]. The sw/u* value of 1.12
under near-neutral conditions is within the range reported in
the literature [i.e., Kaimal and Finnigan, 1994; Pahlow etal., 2001]. Pattey et al. [2002] show that sw/u* can varydepending on the types of sonic anemometers, particularly
with one with longer path length due to loss of covariance
between u and w.
[20] The measured su/u* is plotted with z/L in Figure 3b.
Its normalized function is 3.13 (1 + 8jz/Lj)1/3. In comparison
with sw/u*, more scatters are evident with su/u*. The lattermay not totally follow the Monin-Obukhov theory and bebetter scaled with boundary layer depth hi [Panofsky andDutton, 1984; Van Den Hurk and De Bruin, 1995]. However, su/u* in Figure 3b did not scale with hi/L, where hi wasgiven as constant. The normalized function (su/u* = 2.2 (1 +3jz/Lj)1/3) of De Bruin et al. [1993] underestimates theobserved su/u*.
[21] The measured ruw is presented in Figure 4 with thecorrelation function (equation (7) with Cu1 = 3.13, Cu2 = 8,
Cw1 = 1.12, and Cw2 = 2.8). The ruw decreases in magnitudewith increasing stability because of the dominant role ofbuoyancy production over shear production. The measured
ruw appears to follow the expected pattern as a function of
z/L but with magnitudes smaller than those predicted. The
normalized function of De Bruin et al. [1993] underestimates the ruw, indicating a lower efficiency of momentum
exchange over the Tibetan Plateau than over terrains at low
altitudes.
[22] To investigate whether the cause of such smaller ruw
observed with the Campbell sonic anemometer could be
instrumental, the data obtained during the intercomparison
study with a different sonic anemometer (DAT-600, Kaijo
Denki) were also examined. The result, however, wassimilar, indicating no instrumental bias [see also Hong etal., 2004]. Högstrom [2002] and McNaughton and Brunet[2002] point out a role of inactive eddies that decreases ruw
through the enhanced su. Indeed, on the basis of spectrum
analyses of u and v under near-neutral conditions, Hong etal. [2004] show the evidence of such inactive eddies (i.e.,large spectral density at low-frequency range) at the BJ site.
[23] Figure 5 shows sT/T* and sq/q* under unstable tonear-neutral conditions. Except for the scatters under nearneutral conditions, the measured sT/T* follows well the
Monin-Obukhov theory under unstable conditions. The data
in Figure 5a fitted to a normalized function (i.e., sT/T* =CT1(1 + CT2jz/Lj)1/3) resulted in CT1 = 3.7 and CT2 = 34.5.
(To minimize the effect of scattered data, the data were

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Figure 3. Integral turbulence characteristics of (a) vertical and (b) streamwise wind component with
z/L during the monsoon period. Solid lines are the normalized functions from this study, the dash-dottedline is that by Kaimal and Finnigan [1994], and the dotted line is that by De Bruin et al. [1993].
selected when rTq > 0.9.) For comparison, the normalized
functions for heat of Kaimal and Finnigan [1994] andAndreas et al. [1998] are also presented in Figure 5. The
normalized function for sT/T* in our study is almost
identical to that of Andreas et al. [1998] over a terrain withpatchy vegetation with height of <0.6 m. However, themagnitudes of sT/T* in our study are greater than thosefrom the Kansas data [Kaimal and Finnigan, 1994].
[24] Surprisingly, the behavior of sq/q* is markedly
similar to that of heat (Figure 5b). Unlike sT/T*, thenormalized function for sq/q* of Andreas et al. [1998]
overestimates sq/q*. A normalized function for sq/q* is
not given by Kaimal and Finnigan [1994] but is suggested
to have a functional form similar to that for sT/T* (alsoshown in Figure 5b for comparison).
[25] Figure 6 shows the behavior of sT/T* and sq/q*
within the free convection limit (z/L > 0.2). The similarity
constant in a simplified function (i.e., Cx in equation (3)) is
1.1 for both CT and Cq. In the literature, CT ranges from 0.91

Figure 4. Variation of correlation coefficients for w and u
during the monsoon period. The solid line is predicted by
the corresponding ITCs in this study, and the dashed line isprovided by De Bruin et al. [1993].

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Figure 5. Integral turbulence characteristics of (a) heat and (b) water vapor near neutral and under
unstable stability during the monsoon period. Solid lines are the fitted normalized functions for sT/T* andsq/q* from this study, dashed lines are those by Kaimal and Finnigan [1994], and dash-dotted lines arethose by Andreas et al. [1998].
to 1.12 whereas Cq is greater, with a range of 1.1– 1.5 [e.g.,
Asanuma and Brutsaert, 1999; Katul and Hsieh, 1999]. On
the basis of the analysis of covariance budget equations for
w0 T 0 and w0 q0 , Katul and Hsieh [1999] showed that CT < Cq,suggesting a dissimilarity between heat and water vapor.
[26] In Figure 7, the transfer efficiency of heat to water
vapor (i.e., rwT/rwq) is plotted against z/L. Except the
scatters toward near-neutral conditions, the data converge
to unity under unstable conditions, suggesting a similarityof source/sink between heat and water vapor near the
ground surface.
[27] The measured rwT and rwq are shown in Figure 8 withequation (7) (with the predetermined constants: Cx1 = 3.7,
Cx2 = 34.5, Cw1 = 1.12, and Cw2 = 2.8). Both rwT and rwq
increase as the stability becomes more unstable. For comparison, the formulation of De Bruin et al. [1993] is also
included. Both rwT and rwq show that each scalar follows the
Monin-Obukhov theory with similar transfer efficiency.

[28] Figure 9 shows the transfer efficiencies of heat andwater vapor as compared to momentum. Heat and water
vapor are transferred more efficiently than momentum overa range of different stabilities. While the momentum
exchange is systematically less efficient, heat and water
vapor exchange shows a similar efficiency in magnitude,
following the Monin-Obukhov theory.
3.2. Drag Coefficients[29] First, z0 is estimated on the basis of logarithmic windprofiles under neutral conditions (0.05 < z/L < 0.05).
By substituting the estimated z0(=0.009 ± 0.009 m) inequation (8), CDN is estimated to be 3.1 (±0.7)  103.
On the basis of wind profile measurements at the same site,
Gao et al. [2000] reported z0 of 0.0113 m and CDN of 3.5 
103. Li et al. [2001] estimated the drag coefficients at four
stations on the plateau using wind profile measurements
over 6 years and obtained 4.4– 4.7  103. Bian et al.

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Figure 6. Integral turbulence characteristics of (a) T and (b) q under free convection (z/L > 0.2) during
the monsoon period. Solid lines are the regression lines fitted to the measurements from this study.
[2002] obtained CDN of 5.5  103 through turbulencemeasurements at Qamdo site with sparse weeds of 0.35 m
height in the southeastern Tibetan Plateau. Overall, theserecent estimates of drag coefficients are only half of those(6  8  103) reported by earlier studies [e.g., Yeh andGao, 1979].
3.3. Flux Comparison Between Eddy Covariance andFlux Variance Methods[30] Sensible heat HVAR and latent heat fluxes lEVAR fromthe flux variance method were calculated using equations(4) and (5), respectively (with CT = Cq = 1.1) and comparedagainst the available eddy covariance fluxes (HEC andlEEC) for the same period. The fluxes from the two
methods agree within 5% (SEE = 12 Wm2 and r2 = 0.88
for H; SEE = 37 Wm2 and r2 = 0.78 for lE).
[31] Because of changes in Cq with varying soil waterconditions, lEVAR was also computed with equations (6a)and (6b) using rTq and 1/rTq. Again, the flux comparison
between the two methods is similar to that with a constantFigure 7. Variation of rwT/rwq with z/L under nearneutral and unstable conditions during the monsoon period.
The dotted line shows rwT/rwq = 1.

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Figure 8. Variation of correlation coefficients for (a) w and T and (b) w and q during the monsoon
period. Solid lines are predicted by the corresponding ITCs in this study, and dashed lines for eachcorrelation are provided by De Bruin et al. [1993].
Cq, except for larger values of SEE(=74 Wm2) and lower
r2(=0.34) (Figure 10b). This is mainly due to small rTq,
resulting in 1/rTq of much larger than 1.1. It is encouraging,
however, that lE VAR using r Tq is comparable to themeasured lEEC. This result provides good grounds for
applying the flux variance method when eddy covariancemeasurement is not available.
3.4. Seasonal Variations of Energy Partitioning and
Turbulent Exchange Mechanism[32] As the summer monsoon advanced, there weresignificant changes in meteorological conditions, surface
energy partitioning and turbulent exchange characteristics.
3.4.1. Precipitation and Land Cover[33] Figure 11 shows the seasonal variations of precipitation and soil water content (0.1 m layer). Frequency andmagnitude of precipitation increased particularly in lateJune, resulting in a rapid increase in soil water content.
From the green-up stage in late June to peak growth inJuly – August, the approximate leaf area index (LAI) increased up to 0.45, with a mean canopy height of 0.05 m[Gao et al., 2004]. Following the change in vegetation
cover, color and soil water conditions at the site, theradiation balance changed dramatically.
3.4.2. Radiation Balance[34] Figure 12 shows the diurnal variation of radiation
components on two fairly clear days (12 June and 9 August1998). Prior to monsoon (12 June), the daytime outgoing
longwave radiation Rlup and incoming longwave radiation
Rldn reached up to 600 Wm2 and 300 Wm2, respectively.
The Rlup is significantly larger than those at low altitudes in

Figure 9. Transfer efficiencies of heat and water vapor tomomentum under near-neutral and unstable conditions
during the monsoon period. The solid line is the formulationfrom De Bruin et al. [1993].

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Figure 10. Comparison of measured fluxes of (a) sensible heat and (b) latent heat with estimated ones.
The open circles represent latent heat flux lECq from the flux variance method using Cq of 1.1, and thepluses represent latent heat flux lErTq from flux variance method using rTq (or 1/rTq).
similar latitudes. Smith and Shi [1992] also reported large
values of Rlup over the Tibetan Plateau. During the monsoon(9 August), Rlup decreased by 30% but Rldn increased by
20%, resulting in 60% increase in net longwave radiation.
While downward shortwave radiation Rsdn remained almost
the same, the albedo (=Rsup/Rsdn) decreased by 30% frompremonsoon (0.2) to monsoon (0.15). Consequently, the net
radiation (Rn) increased by 20%. During midday, about 25%of Rn was dissipated into soil heat flux.
3.4.3. Surface Energy Partitioning[35] Figure 13 shows the seasonal variation of the energy
partitioning in terms of the Bowen ratio (b = H/lE). With
the onset of the summer monsoon, b decreased rapidly from
9 in early June to 0.4 in July and remained low until midSeptember. Then, b began to increase with the withdrawalof the monsoon. Prior to the monsoon, sensible heat flux
from the surface was the major source of heating over theplateau, whereas the latent heat released by condensation
became the primary source with the onset of the monsoon.
3.4.4. Shift in Turbulent Exchange Mechanism[36] The seasonal variation of daytime averaged rTq
increases from near zero during the premonsoon to 0.8
during the monsoon period (Figure 14). Such a change in
rTq implies a shift in the similarity between heat and water
vapor with the seasonal march of summer monsoon.
Changes in skewness of T(SkT), q(Skq) and w(Skw) provide
further insights. Normally, positive skewness values are
expected because of narrow, warm and moist updrafts,
compared to wider, cool and dry downdrafts [Mahrt,
1991]. Throughout the season, SkT and Skw are consistently

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Figure 11. Seasonal variations of precipitation and soil
water content (SWC).

Figure 13. Seasonal variation of the Bowen ratio.

positive (0.2  1.5). However, as b rapidly decreases withenhanced lE, Skq shifts from negative to near-zero values inJune to positive values comparable to SkT during themonsoon period. Mahrt [1991] reported that rTqand Skq(measured at 100– 150 m above ground) increased withdecreasing boundary layer height because of strong surface
evaporation. During our study, hi over Amdo site (100 km
north of BJ site) also decreased from 2.5 km during
premonsoon to about 1 km during monsoon period in 1998
(http://monsoon.t.u-tokyo.ac.jp/tibet/data/iop/pbltower/). As

Figure 12. Diurnal variations of radiation budget components on 12 June (Figures 12a and 12c,
premonsoon) and 9 August 1998 (Figures 12b and 12d, monsoon). Rldn is the downward longwave
radiation, Rlup is the outgoing longwave radiation, Rn is the net radiation, Rsup is the reflected shortwave
radiation, and Rsdn is the incoming shortwave radiation.
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D21106and profile measurements of heat and water vapor in theboundary layer are needed, particularly for premonsoon
periods. Such measurements are currently in progress on
the plateau through ongoing CEOP field campaigns.
[42] Acknowledgments. We are grateful to Bert Tanner and Edward
Swiatek at Campbell Scientific, Inc., for their prompt and generous lendingof the sonic anemometer used in this study. Our thanks also go out toY. Yupin for helping with field measurements. Financial support was given
through the ‘‘Eco-Technopia 21 project’’ by the Ministry of Environment,
Korea, and the National Space Development Agency of Japan. The CEOP
data were provided within the framework of GAME/CAMP Tibet Scientificand Technological Research Project, funded by the Ministry of Education,
Culture, Sports, Science and Technology; the Japan Science and Technology Agency; the Frontier Research System for Global Change; the Japan
Aerospace Exploration Agency; the Chinese Academy of Sciences; and theChinese Academy of Meteorological Sciences.
Figure 14. Seasonal variations of daytime-averaged rTq
and skewness of T, q, and w. Error bars indicate standard
error of mean.

pointed out by De Bruin et al. [1999], the relative contributions of entrainment of warm and dry air from the boundary
layer top probably have influenced the similarity near thesurface. Local advection or the heterogeneity of sink/source
distributions of heat and water vapor also could result indissimilarity between the two scalars [e.g., Katul et al., 1995;
De Bruin et al., 1999]. Analysis of skewness under suchinfluences would be helpful to better understand the turbulent
exchange mechanisms over the Tibetan Plateau.

4. Concluding Remarks[37] Turbulent exchange characteristics in a Tibetan
shortgrass prairie were investigated in terms of flux variancerelation, and the changes in environmental conditions,surface energy partitioning, and the similarity between heat
and water vapor were presented with the seasonal march of
the summer monsoon. Main results are summarized asfollows.
[38] 1. Normalized standard deviations of heat and water
vapor follow the Monin-Obukhov similarity theory. Heat
and water vapor are similar in terms of the similarity
constant, and the flux variance method produces comparable H and lE to those of eddy covariance method.
[39] 2. Unlike the similar magnitudes of transfer efficiencyof heat and water vapor, momentum exchange is underestimated compared to the Monin-Obukhov similarity
prediction.
[40] 3. As the summer monsoon progresses, significant
changes in surface energy partitioning occur toward theenhancement of lE. The entrainment from boundary layer
top or inversion layer aloft probably affects the dissimilarity
between heat and water vapor during the premonsoon,
whereas its effect during the monsoon seems relatively
weak as evidenced by low b. More information is neededon the likely role of inactive eddies associated with turbulent exchange mechanisms over the plateau [Hong et al.,2004].
[41] 4. Further direct measurements on turbulence statistics, evaluation of local advection and heterogeneity ofsource/sink distribution through remote sensing analysis,

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J. Asanuma, Department of Geosciences, Tsukuba University, Tsukuba
305-8571, Japan. (asanuma@suiri.tsukuba.ac.jp)
T. Choi, J. Hong, J. Kim, and H. Lee, Department of Atmospheric
Sciences, Yonsei University, Seoul 120-749, Republic of Korea. (ctjin@
yonsei.ac.kr; jkhong@yonsei.ac.kr; joon-kim@yonsei.ac.kr; fluxes@atmos.
yonsei.ac.kr)
Gao Zhiqiu, Chinese Administration for Meteorological Sciences, Beijing
100081, China. (zgao@cams.cma.gov.cn)
H. Ishikawa, Disaster Prevention Research Institute, Kyoto University,
Kyoto 611-0011, Japan. (ishikawa@storm.dpri.kyoto-u.ac.jp)
T. Koike, Department of Civil Engineering, University of Tokyo, Tokyo
113-8656, Japan. (tkoike@hydra.t.u-tokyo.ac.jp)
Y. Ma and K. Ueno, School of Environment, University of Shiga
Prefecture, Hassaka-cho 2500, Hikone, Shiga 522-8533, Japan. (ymma@
ns.lzb.ac.cn; kueno@ses.ups.ac.jp)
O. Tsukamoto, Faculty of Science, Okayama University, 3-1-1 Tsushimanaka, Okayama 700-8530, Japan. (tsuka@cc.okayama-u.ac.jp)
J. Wang, Cold and Arid Regions Environmental and Engineering
Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China.
(jmwang@ns.lzb.ac.cn)
T. Yasunari, Hydrospheric Atmospheric Research Center, Nagoya
University, Nagoya 464-8601, Japan. (yasunari@hyarc.nagoya-u.ac.jp)

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