ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 25, NO. 6, 2008, 906–921

An Overview of the Semi-arid Climate and Environment
Research Observatory over the Loess Plateau


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HUANG Jianping∗1 (), ZHANG Wu1 (), ZUO Jinqing1 (), BI Jianrong1 (1
1
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SHI Jinsen (), WANG Xin (), CHANG Zhoulin (), HUANG Zhongwei1 (), ZHANG Beidou1 (), WANG Guoyin1 (), FENG Guanghong1 (YANG Su1 (YUAN Jiuyi1 (), ZHANG Lei1 (), ZUO Hongchao1 (), WANG Shigong1 (FU Congbin2,1 (), and CHOU Jifan1 (
)
1
2






College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000

Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029(Received 30 May 2007; revised 26 June 2008)
ABSTRACT

Arid and semi-arid areas comprise about 30% of the earth’s surface. Changes in climate and climate
variability will likely have a signiﬁcant impact on these regions. The Loess Plateau over Northwest China isa special semi-arid land surface and part of a dust aerosol source. To improve understanding and capture
the direct evidence of the impact of human activity on the semi-arid climate over the Loess Plateau, theSemi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) was established in 2005.
SACOL consists of a large set of instruments and focuses on: (1) monitoring of long term tendencies in semiarid climate changes; (2) monitoring of the aerosol eﬀect on the water cycle; (3) studies of interaction between
land surface and the atmosphere; (4) improving the land surface and climate models; and (5) validationof space-borne observations. This paper presents a description of SACOL objectives, measurements, andsampling strategies. Preliminary observation results are also reviewed in this paper.
Key words: cloud, aerosol, land surface, semi-arid, Loess Plateau, SACOL
Citation: Huang, J. P., W. Zhang, J. Q. Zuo, J. R. Bi, J. S. Shi, X. Wang, Z. L. Chang, Z. W. Huang,
S. Yang, B. D. Zhang, G. Y. Wang, G. H. Feng, J. Y. Yuan, L. Zhang, H. C. Zuo, S. G. Wang, C. B. Fu,
and J. F. Chou, 2008: An overview of the semi-arid climate and environment research observatory over the
Loess Plateau. Adv. Atmos. Sci., 25(6), 906–921, doi: 10.1007/s00376-008-0906-7.

1.

Introduction

Water and energy cycles are extremely importantin understanding the mechanism of climate and environmental change in China because of the severe shortage of water resources and the rapid increase of population, which lead to a signiﬁcant anthropological impact on the climate and environment due to land-use
and greenhouse gas emissions. Anthropogenic emissions of greenhouse gases cause an increase in surface
temperatures (the “greenhouse eﬀect”) and can haveprofound eﬀects on the climate and thus on societal
welfare. Brought on by more than a factor of 5, the∗ Corresponding author: HUANG Jianping, hjp@lzu.edu.cn

increase in human population and the subsequent activities of mankind over the last century or so, greenhouse gases and aerosols produced by the combustionof fossil fuels, industrial activities, and land use havealtered the atmospheric composition so that it is not
the same as it was a century ago (Mann et al., 1999;
Crowley, 2000; Houghton et al., 2001). The resulting
increase in surface temperature over this period is estimated to be between 0.4◦ C to 0.8◦ C (Houghton etal., 2001). Aerosol particles also have a major inﬂuence on the global climate and climate change; they
can locally either intensify or moderate the eﬀects ofthe greenhouse gases through the scattering or absorp-

NO. 6HUANG ET AL.

tion of both incoming solar radiation and thermal radiation emitted from Earth’s surface. Aerosols also
act as cloud condensation nuclei (CCN) and thereby
modify the radiative properties of clouds (Twomey,1977). There is substantial uncertainty regarding themagnitude and spatial distribution of aerosol radiative forcing. Ramanathan et al. (2001) suggested thata combination of the aerosol direct and indirect eﬀect
can weaken the hydrological cycle, which could be amajor environmental issue in this century.
To better understand and quantify the humaninduced climate eﬀect, a variety of global climatic observation networks have been establishedrecently to characterize the physical and chemical properties and processes of greenhouse gases
and aerosols. These include the Baseline Surface
Radiation Network (BSRN) (http://bsrn.ethz.ch),
CEOP (Coordinated Enhanced Observing Period) (http://www.gewex.org/ceop.htm); AERONET(Aerosol Robotic Network) (Holben et al., 1998);
MPLNET (Micro-Pulse Lidar Network) (Welton et al.,2001); and so on. BSRN is a project of the World Climate Research Programme (WCRP) and the Global
Energy and Water Experiment (GEWEX) and as suchis aimed at detecting important changes in the earth’s
radiation ﬁeld at the earth’s surface that may be related to climate change. The data are of primary
importance in supporting the validation and conﬁrmation of satellite and computer model estimates ofthese quantities. CEOP was initiated as a major step
towards bringing together the research activities in
GHP (GEWEX Hydrometeorology Panel)/GEWEX(Global Energy and Water Cycle Experiment) and related projects in WCRP (CLIVAR; CLiC). CEOP has
now developed into an important element of WCRP.
It has also been endorsed by the Integrated Global
Observing Strategy Partnership (IGOS-P) as the ﬁrst
element of the IGOS-P Integrated Global Water Cycle Observations (IGWCO) Theme. As a result ofa decision at the 2005 meeting of the WCRP Joint
Scientiﬁc Committee, the oversight of CEOP is shared
between GEWEX and the WCRP Observation and
Assimilation Panel (WOAP). BSRN is aimed at detecting important changes in the earth’s radiation ﬁeld
at the earth’s surface which may be related to climatechange; AERONET is a ground-based remote sensing
aerosol network that provides a long-term, continuous and readily accessible public domain database ofaerosol optical, mircrophysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with otherdatabases (Holben et al., 1998); MPLNET is comprised of ground-based lidar systems, co-located withsun/sky photometer sites in AERONET. The com-907

bined lidar and sunphotometer measurements are able
to produce quantitative aerosol and cloud products,
such as optical depth, sky radiance, vertical structure,and extinction proﬁles.
Arid and semi-arid areas comprise about 30% of
the earth’s surface. Changes in climate and climate
variability will likely have a signiﬁcant impact on theseregions. Some ﬁeld experiments have recently been
carried out over the arid and semiarid regions of China
over the past 30 years, for example HEIFE (Hu, 1994;
Hu and Gao, 1994), IMGRASS (Lu et al., 2002, 2005),
NWC-ALIEX (Huang, 2004; Zhang et al., 2005; Baoand Lu, 2006), dust aerosol observations in Dunhuang(Iwasaka et al., 2003, 2004), surface layer turbulent
ﬂux observations over Naiman (Zhang et al., 2001) andTongyu (Liu et al., 2004), etc., in which a great dealof observational data and research achievements were
gained. However, there was little learned of the Loess
Plateau and there was no international long-term observation site for the Loess Plateau. Loess Plateau is
the largest arid and semi-arid zone in China and is also
one of the dust aerosol sources. To ﬁll this gap and improve understanding of the impact of human activity
on semi-arid climate over the Loess Plateau, the SemiArid Climate & Environment Observatory of Lanzhou
University (SACOL) was established in 2005. This site
is nearby the city of Lanzhou, at the southern bank of
the Yellow River in northwestern China. In addition,
the site is very close to the geometric center of China’s
mainland. This region includes many types of aerosol
particles of widely varying composition and sizes derived from one of the largest aerosol source regions
on the earth. SACOL makes several important measurements of wind-blown dust, urban pollution, andradiation properties. SACOL is speciﬁcally designed
to assess the dust aerosol eﬀect on local climate aswell as the global climate. Most of the instruments in
SACOL are based at the BSRN, CEOP, AERONET
and MPLNET. The main focus of SACOL is on assessing and improving the new data products being
derived from these instruments to increase our knowledge and reduce uncertainties associated with atmospheric aerosols and their radiative impacts. SACOL
also plays a critical role in testing and improving thesatellite algorithms over the Loess Plateau because of
the unusually complete datasets that are obtained tocharacterize properties of the atmosphere and surface.
Furthermore, the measurements provide details about
aerosols, such as particular height-resolved aerosol radiative and microphysical properties, some of whichcannot be retrieved from any current or anticipatedsatellite data; these data are a test bed for current
aerosol and chemical transport models. Details regarding SACOL objectives, platforms, instrumenta-

908

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

tion, measurement strategy, and a summary of early
results are given below.
2.

Scientific objectives

The SACOL site oﬀers attractive opportunities tostudy certain phenomena that are believed to be important to the drought process. These phenomena
form the focus for the SACOL. The primary objectiveof SACOL is the elucidation of drought processes insuch a way that their physical description can be accurately incorporated into global or regional climate
models. These models include parameterizations ofa variety of physical, chemical, and biological processes occurring in the coupled atmosphere-land surface, that, taken together and working in concert, aredesigned to simulate and predict the climate system,with all its relevant feedback mechanisms that dominate climate regionally as well as globally.
Recently, special attention has been dedicated tocloud interactions with desert aerosol particles (Rosenfeld et al., 2001; Bréon et al., 2002; DeMott et al.,
2003; Kawamoto and Nakajima, 2003; Huang et al.,2006a,b). However, the knowledge of the eﬀects ofAsian dust aerosols on arid climate is still very limited
due to the lack of observations. Aerosols are generally
believed to exert a cooling inﬂuence on the climate directly by scattering solar radiation and through their
indirect eﬀects on clouds. However, the semi-direct effect has the potential to oﬀset this cooling by reducing
low cloud cover and water path. Analysis of the satellite observations indicates that, on average, the water
path of dusty clouds is considerably smaller than thatfrom dust-free clouds in the same frontal systems. The
key issue may be related to the dust aerosol warming
eﬀect through the absorption of solar radiation. The
absorption or diabatic heating of Asian dust particles
can cause the evaporation of cloud droplets and reducethe cloud water path (Huang et al., 2006a,b). Due tothe large spatial and temporal extent of desert dust in
the atmosphere, the interactions of desert dust withclouds can have substantial climatic impacts. Dust
storms may have contributed to the desertiﬁcation of
Northwest China during recent decades. To better understand the eﬀect of Asian dust on regional climate,
one of the important objectives of SACOL is to focus on measurements of physical processes of aerosolclimate interactions.
Satellites, which oﬀer a global perspective withhigh spatial and temporal resolution, have become important tools for measuring the changing characteristics of the climate system and the associated radiative
heat ﬂuxes that deﬁne the climate. Improvements insatellite sensor technologies and constituent retrieval

VOL. 25

techniques have helped to advance our understandingof the climate system. Hence, improvement of the
Loess Plateau satellite remote sensing is a relevant
and important scientiﬁc objective of the SACOL. The
major scientiﬁc objectives of SACOL are to monitor
long term tendencies in semi-arid climate changes; theaerosol eﬀect on the water cycle; characterize the interaction between the land surface and the atmosphere;
form a base for the land surface in climate modelingand validation of space-borne observations.
3.

Location and climatology

SACOL is about 48 km away from the center ofLanzhou, situated at the southern bank of the Yellow River in the Gansu province in Northwest China(35.57◦N, 104.08◦E). It is situated on the China-Loess
Plateau, about 1965.8 m above sea level. The topography around the site is characterized by the Loess
Plateau consisting of plain, ridge and mound, etc, withthe elevation ranging between 1714–2089 m. The parent soil material is mainly quaternary aeolian loesswith the main soil type being sierozem. There is also
some loess soil in the terrace, ridge and mound. The
terrain where the measurements are carried out is ﬂatand covered with short glass. The dominated species
within the immediate area are stipa bungeana as well
as artemisia frigida and leymus secalinus. Figure 1
shows the typical landscape around the site. Figure
2 shows the map of SACOL’s location. The climatological information is listed in Table 1. The annual
variations of surface temperature and precipitation aredisplayed in Fig. 3. A maximum of surface temperature is found in July and a minimum in late winter.
However, a maximum of precipitation is observed inAugust.
Based on the available original dust event
records from the Yuzhong meteorological stations, themonthly means of the average observed days with surface spring dust events around the SACOL site areplotted in Fig. 4. Dust events can be classiﬁed into
three categories depending on the meteorological condition: ﬂoating-dust (FD), blowing dust (BD) anddust storm (DS). The overall 45-year (1960–2004) annual mean FD value of 18 days is about 139% greaterthan the mean BD value of 7.53 days and 1057%
greater than mean DS of 1.53 days.
4.

Measurements

SACOL was established at the Yuzhong campus ofLanzhou University in late 2005. It consists of a large
set of instruments and measure: (1) boundary layer
meteorological parameters; (2) surface radiation; (3)NO. 6HUANG ET AL.

909

Fig. 1. Multiple views of the SACOL site.
Fig. 4. Monthly variations of dust event at SACOL.

Fig. 2. Map of SACOL’s location.

Temperature (°C)

25 (a)
20
15
10
5
0
-5
-10
-15

4.1
1

2

3

4

5

Precipitation (mm)150 (b)

6 7 8
Month

9 10 11 12

6 7 8
Month

9 10 11 12

120
90
60
30
0

surface ﬂuxes; (4) soil heat and water moisture; (5)ambient air quality; (6) aerosol optical properties; (7)temperature and water vapor proﬁles; (8) sky conditions. SACOL is a participating site in the Coordinated Enhanced Observing Period (CEOP) which was
initiated as a major step towards bringing together
the research activities in GHP/GEWEX and related
projects in WCRP (CLIVAR; CLiC). CEOP has now
developed into an important element of WCRP. It has
also been endorsed by the Integrated Global Observing Strategy Partnership (IGOS-P) as the ﬁrst elementof the IGOS-P Integrated Global Water Cycle Observations (IGWCO) Theme. An AERONET-Cimel sun
photometer for spectral, radiative, and aerosol optical properties (Holben et al., 1998) has been measured
and archived at SACOL. The Micro-Pulse Lidar Network (MPLnet) lidar (Welton et al., 2001) is also operational at SACOL. It provides real-time images ofthe vertical proﬁle of backscatter. Many additional
measurements made at SACOL are shown in Table 2.
The details about the instruments are introduced asfollowing:

1

2

3

4

5

Fig. 3. Monthly mean and standard deviation of meteorological parameters at SACOL for (a) surface temperature; and (b) precipitation.

Boundary layer meteorological measurements system

Boundary layer meteorological measurements (Fig.
5a) include wind speed (014A-L, Met One), air temperature and relative humidity (HMP45C-L, Vaisalla)
at 1, 2, 4, 8, 12, 16 and 32 m, and wind direction(034B-L, Met One) at 8 m, with signals logged toa data logger (CR23X, Campbell) and recorded at
half-hour intervals. The measurement of skin temperature is made with a Precision Infrared Thermocouple Sensor (IRTS-P, Apogee). Barometric pressure
is measured using a CS105 Barometric Pressure Sensor (Vaisala) over a 600 to 1060 hPa range. Precipitation is measured with a tipping bucket Rain Gage
(TE525MM-L, Texas Electronics) in 0.1 mm increments.

910

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

VOL. 25

Table 1. Surface and climate parameters.
Parameter

Description

Surface TypeLandscapeClimateAnnual precipitation
Annual mean evaporation
Annual mean air temperature
Annual mean highest temperature
Annual mean lowest temperature

Loess tableland, ridge, hillock and gully
crop and semi-desert
continental monsoon zone
381.8 mm
1528.5 mm
6.7◦ C
13.7◦ C0.8◦ C

Table 2. Instruments and parameters measured from the semi-arid climate and environment of Lanzhou University.
Description

Model

Manufacturer

Wind speed sensor
Wind direction sensor
Humidity and temperature probe
Surface temperature sensor
Barometric pressure sensor
Tipping bucket rain gage
Pyranometer (SW ﬂux)
Normal incidence pyrheliometer (DIRECT)
Pyrheliometer
UV radiometer (UV-A & UV-B)
Pyrgeometer (LW ﬂux)
UV-Multi-ﬁlter rotating shadow-band radiometer
Multi-ﬁlter rotating shadow-band radiometer
3-D Sonic anemometer
Opened path infrared CO2 & H2 O analyzer
Large Aperture Scintillometer
Water content reﬂectometer
Soil temperature proﬁleSoil Heat ﬂux plateSO2 analyzer
NO/NO2 /NOx analyzer
Ozone analyzer
CO analyzer
PM10 Ambient Particulate Monitor
Non Methane Hydrocarbons Analyzer
Micro-Pulse Lidar
CAMLTM Micro lidar
Sun photometer
Integrating Nephelometer (3-wavelength)
Integrating Nephelometer (450, 550, 700 nm)
Aerodynamic Particle Sizer Spectrometer(0.5 to 20 µm aerodynamic sizing,
0.37 to 20 µm optical detection)
Aethalometer (aerosol light absorption)
Dual Band for Temperature,
Water Vapor & Liquid Water ProﬁlesTotal Sky Imager

014A L
034B LHMP45C-L
IRTS-P
CS105
TE525MM-L
CM21
MODEL NIP
CH 1
UV-S-AB-T
CG4
UVMFR-7
MFR-7
CSAT3
LI7500LAS
CS616-L
STP01-L50
HFP01SC-L50
ML9850B
ML9841B
ML9810B
ML9830B
RP 1400a
PCF NMHC 527
MPL-4
CE370-2
CE318 (AERONET)TSI 3563
M9003

Met OneMet OneVaisalaApogeeVaisalaR. M Young
Kipp & Zonen
EppleyKipp & Zonen
Kipp & Zonen
Kipp & Zonen
YankeeYankeeCampbell
Li-CorKipp & Zonen
CampbellHukseﬂux
Hukseﬂux
EcotechEcotechEcotechEcotech
R&P
PCF
Sigma Space
Cimel & CNRS
Cimel
TSI
Ecotech

TSI 3321

TSI

AE-31
TP/WVP-3000

Magee
Radiometrics
Corporation
YankeeTSI 880

NO. 6911

HUANG ET AL.

Fig. 5. Instruments for (a) Boundary layer meteorological measurements; (b)–(e) Surface radiation monitoring; (f) and (g) Surface ﬂuxes
monitoring; (h) Soil parameters monitoring; (i)–(k) Ambient air analyzers; (l)–(r) Aerosol optical properties measurements; (s) Temperature
and water vapor proﬁles; and (t) Sky conditions measurement.

4.2

Surface radiation monitoring system

The surface radiation monitoring system consistsof upward and downward pyranometers (CM21, Kipp
& Zonen) (Fig. 5b) for incoming and outgoing shortwave radiation; and upward and downward pyrgeometers (CG4, Kipp & Zonen) (Fig. 5c) for incoming
and outgoing longwave radiation. In addition, spectral
measurements of total, diﬀuse and direct solar irradiances are made with a set of two multi-ﬁlter rotating
shadow-band radiometers [UVMFR-7 (Fig. 5d) andMFR-7 (Fig. 5e)] with seven channels in the UV (300,
305.5, 311.4, 317.6, 325.4, 332.4 and 368 nm) and thevisible (415, 500, 615, 673, 870, and 940 nm), respec-tively. From those measurements, the properties ofclouds (Min, 1996) and the atmosphere (Harrison etal., 1994; Harrison and Michalsky, 1994; Alexandrov
et al., 1999) can be derived, such as the column abundances of ozone and water vapor as well as aerosol andother atmospheric constituents.
4.3

Surface fluxes monitoring system

4.3.1

Eddy covariance system

The ﬂuxes of momentum, CO2 , latent (H2 O) andsensible heat are measured at 3.0 m with a threeaxis Sonic Anemometer (CSAT3, Campbell) (Fig. 5f)
pointed into the prevailing wind direction, and an

912

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

VOL. 25

opened path infrared CO2 and H2 O analyzer(LI7500 LI-COR) (Fig. 5f). These signals are logged
to a data logger (CR5000, Campbell) at 10 Hz. It is
important to estimate the turbulent ﬂuxes between thesurface and the atmosphere to understand the mechanism of the water and energy cycles over the semi-arid
region of the Loess Plateau. All the necessary procedures for corrections and quality control of the turbulent ﬂuxes are applied during post-ﬁeld data processing, such as coordinating the rotation after the planarﬁts (PF) (Wilczak et al., 2001), frequency response
corrections (Moore, 1986; Massman, 2000, 2001), sonictemperature correction (Schotanus et al., 1983), WPL
correction (Webb et al., 1980), and quality control after Foken et al. (2004).

land use regimes and the dynamics of soil moisture,
ﬁve experimental sites have been set for measuring
soil and vegetation parameters. The sites include two
grasslands (fenced grassland and grazing grassland)
and three croplands. Each site is 1 m2 and each isadjacent to one another. When the fenced grassland
was fenced in October 2005, both grasslands had been
restored from cropping for 20 years and the dominantgrass at the site was Stipa. bungeana. Three croplands
were cropped with Solanum tuberosum L., Setaria italica Beauv, and Semen Sesami Nigrum in rotation, andin fallow every two or three years (See Table 3). Soilsamples are taken to 100 cm at seven depths (0–10,
10–20, 20–30, 30–40, 40–60, 60–80 and 80–100 cm)
and the following parameters are measured:

4.3.2• Chemical parameters: SOC (soil organic carbon) is
measured by using the oil bath K2 CrO7 titration
method after digestion, and total nitrogen (Kjeldahl method) and pH[1:1(w/w) distilled water] arealso measured (Chinese Editorial Committee of Soil
Analysis, 1996).

Large Aperture Scintillometer (LAS)

The observations of the surface sensible heat ﬂux,
representative of areas of several square kilometers,
have been regularly performed with a Large Aperture Scintillometer (LAS, Kipp & Zonen) (Fig. 5g)
at SACOL. This measurement is suitable for use in
the energy balance and water cycle study, air pollution and regional weather forecasting, and for inertial sub-range scale turbulence measurements. Compared to the traditional “point’ measurements, LAS
measured larger-scale (horizontal length 0.25–4.5 km,
close to the satellite pixel scale) sensible heat ﬂuxes;
it is also suitable for the validation of the study ofsatellite/aircraft remote sensing.
4.4

Soil and vegetation parameters monitoring system

4.4.1

Soil heat ﬂuxes, moisture and temperature measurements

To complete the surface energy balance, soil heat
ﬂuxes (HFP01SC-L, Hukseﬂux) are measured at 5 and10 cm depth. Soil moisture is measured with a watercontent reﬂectometer (CS616-L, Campbell) at 5, 10,
20, 40 and 80 cm depths; and soil temperature with asoil temperature proﬁle (STP01-L, Hukseﬂux) sensor
at 2, 5, 10, 20 and 50 cm depths (see Fig. 5h).
4.4.2

Soil chemical, physical and biological parameters measurement

To better understand the carbon exchange in
the atmosphere-vegetation-soil system under diﬀerent• Physical parameters: Soil particle size is measured using the Automatic Granularity Laser(MALVERN, Germany) while soil bulk density
measurement is carried out with the soil core
method (Chinese Editorial Committee of Soil Analysis, 1996). A time-domain reﬂectometer (TDR) is
used to monitor the dynamics of the moisture insoil.
• Biological parameters: Soil respiration is monitoredwith the LI-COR 6400-9 (LI-COR, Lincoln, Neb.,
USA; Janssens et al., 2000) while temperature andsoil moisture are measured simultaneously to ﬁnd
out the relationship between soil respiration andthese factors. To monitor the soil moisture and soil
respiration dynamics, both parameters are measured once a month.
4.4.3

Vegetation parameters measurements

Quadrants are set for the measurement of vegetation parameters over the 5 sites mentioned above.
Within each quadrant, all species are identiﬁed andcounted and the heights of plant and vegetation cover
are measured. Above-ground biomass measurement
is taken with the harvest method (Ren, 1998). In
the grassland, the below-ground biomass (0–100 cm)

Table 3. The rotation pattern in past four years.

Cropland I
Cropland II
Cropland III

2003

2004

2005

2006

fallowsesamefallow

milletpea
sesamefallow
fallow
milletpotatopea
fallow

NO. 6is measured by hand washing after the soil cores aretaken with an auger (9.3 cm diameter) (Ren, 1998),
while in croplands, it is measured with the harvest
method. Carbon and Nitrogen content in plant components are measured using the Thermo CHNS-O analyzer (Flash EA 1112). The heat value of the plant
samples is measured with a Micro-computer calorimeter (WGR-1).
4.5

Ambient air quality monitoring system

SACOL is equipped with the ML9800 (SeeFig.
5i) ambient air analyzers (Monitor Labs)
which measure the concentrations of carbon monoxide (CO) (ML9830B), ozone (O3 ) (ML9810B), sulfur dioxide (SO2 ) (ML9850B), and oxides of nitrogen (NO/NO2 /NOx ) (ML9841B), present in the atmosphere. SACOL is also equipped with instruments thatallow scientists to study the properties of tiny atmospheric particles called particulate matter (PM) andthat of non-methane hydrocarbons (NMHC) which effects photochemical reactions in the troposphere. The
concentration of PM10 present in the boundary layer ismonitored with a Beta Gauge Particulate Monitor (F701, Durag-Verewa) (see Fig. 5j), and that of NMHC
with a Total Non Methane Hydrocarbons Analyzer(TNMH 462, DANI) (See Fig. 5k).
4.6
4.6.1

Aerosol optical properties monitoring system
Column aerosol optical depth

The observations of aerosol optical depth (AOD)
are made using an automatic sun tracking photometer (CE318N-VPS8, Cimel) (Fig. 5l) with 8 ﬁltersin the visible and near infrared at wavelengths of
440, 670, 870, 870,870, 936 and 1020 nm. The sunphotometer works by measuring the intensity of thesurface-reaching solar radiation at the speciﬁed wavelength bands and then converting it to optical depth
by knowing the corresponding intensities at the topof the atmosphere (TOA). The TOA intensity values
are periodically obtained using the Langley plot calibration technique. SACOL also has a CE318N-EBS9
with 9 measurement channels at 340, 380, 440, 500,
675, 870, 936, 1020 and 1610 nm. The instrument
calibration is carried out once per year.
4.6.2

913

HUANG ET AL.

Aerosol vertical distribution

Aerosol vertical distribution (extinction coeﬃcient)is measured using a Micro-Pulse Lidar system (MPL-4,
Sigma Space) (Fig. 5m). The MPL has one measurement channel that records backscatter signals up to
20+ km. The primary quantity from this signal is thelowest detected cloud base in meters. Additional quan-tities, possible through post-processing of the raw signal return, include a relative backscatter proﬁle at 527
nm. From the relative backscatter proﬁle other data
products are possible including multiple cloud decks,
cloud and layer boundaries, as well as aerosol extinction and backscatter proﬁles. After the new units are
deployed cloud phase information will also be available.
Aerosol vertical distribution is also measured by a
CAMLTM Micro Lidar (CE 370-2, CIMEL & CNRS)(Fig. 5n). It is a portable, eye safe, and unattended
backscattering lidar that features the ability to proﬁle atmospheric cloud and aerosol structures and to
retrieve aerosol optical and dynamic properties.
4.6.3

Aerosol size distribution

Aerodynamic diameter as well as the relative lightscattering intensity of aerosol particles, which are between 0.5 and 20 µm in diameter, are measured using an Aerodynamic Particle Sizer (APS-3321, TSI)
(Fig. 5o), which uses a sophisticated time-of ﬂight
technique. The APS also measures the light-scattering
intensity in the equivalent optical size range of 0.37 to
20 µm using a light-scattering technique. By providing paired data for each particle, the information of
the makeup of an aerosol may be derived.
4.6.4

Aerosol scattering coeﬃcient

The observations of the aerosol scattering coeﬃcient are performed using a TSI Integrating Nephelometer (Model 3563) (Fig. 5p) and three ECOTECH
Integrating Nephelometers (M9003). The TSI Integrating Nephelometer has three wavelengths (450, 550
and 700 nm) (Fig. 5q), and the ECOTECH Integrating Nephelometer has only one wavelength (450, 550
or 700 nm). These two kinds of nephelometers sharethe same principle. They are all designed speciﬁcallyfor studies of direct radiative forcing of the Earth’s climate by aerosol particles or studies of ground-based orairborne atmospheric visual air quality.
4.6.5 Aerosol absorption coeﬃcient
The aerosol light-absorption coeﬃcient is measured
at seven diﬀerent wavelengths (370, 470, 520, 590, 660,
880 and 950 nm) by an aethalometer (model AE-31,
Magee Sci.) (Fig. 5r), which measures the attenuation of the light beam transmitted through the aerosols
deposited continuously on a quartz ﬁber ﬁlter paper.
The diﬀerence in light transmission through the particle laden sample spot and a particle-free reference spotof the ﬁlter is used to compute the absorption coeﬃcient. The primary propose of this instrument is tomeasure the aerosol black carbon (BC) concentrationin the atmosphere.

914
4.7

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

VOL. 25

Temperature and water vapor profiles

The continuous measurements of temperature andwater vapor proﬁles are carried out with a Radiometrics Proﬁling Radiometer (TP/WVP-3000, Radiometrics) (Fig. 5s), which produces vertical proﬁles fromthe surface to 10 km in height by observing 5 frequency
channels from 22 to 30 GHz and 7 channels from 51
to 59 GHz. The TP/WVP-3000 also produces lowresolution liquid water proﬁles and cloud base temperatures using a zenith-pointed infrared thermometer(IRT).
4.8

Sky conditions measurements

The observations of daytime sky conditions are performed using a TSI total sky imager (Model 880) (Fig.
5t), which is an automatic, full-color sky imaging system that provides real-time processing and display of
daytime sky conditions. An onboard processor computes both fractional cloud cover and sunshine duration, storing the results and presenting data to users
via an easy-to-use web browser interface. It captures
images into standard JPEG ﬁles that are analyzed into
fractional cloud cover.
5.

Fig. 6. Interannual variations of monthly mean downward and upward longwave (LW) radiations, downward
and upward shortwave (SW) radiations, and net radiation for the period of May 2006 to May 2008.

Observation results

A brief summary of the key measurements obtainedat SACOL from May 2006 through May 2008 is presented in this section. A more complete description of
the measurements will be presented in other papers.
Figure 6 depicts the interannual variation ofmonthly mean downward and upward longwave (LW)
radiation, downward and upward shortwave (SW) radiation, and net radiation for the period of May 2006
to May 2008. The upward longwave and downward
shortwave radiation have the strongest seasonal andinterannual variability, especially for summer time.
Figure 7 shows the diurnal cycle and the variabilityof downward and upward short wave and long wave
ﬂuxes, respectively. The distribution shape of thedownward and upward short wave ﬂuxes are symmetric with a maximum at 1300 LST (Fig. 7a). The upward LW ﬂux shows a similar pattern to the downward
SW ﬂux and follows the expected warming and coolingof surface temperatures. However, the large variabilityof the downward SW and upward LW ﬂuxes are likely
associated with the variability of clouds and aerosols.
Figure 8 shows the diurnal cycle of net radiation for
four seasons from May 2006 to May 2008. The largest
net radiation occurs at noon during the summer andspring seasons, which are 35% and 100% larger than
the autumn and winter seasons, respectively.
Figure 9 shows the comparison of the MODIS/CERES retrieved short wave radiative properties with

Fig. 7. Diurnal cycle and standard deviation of (a) upward and downward shortwave (SW) radiation, and (b)upward and downward longwave (LW) radiation for theperiod of May 2006 to May 2008.

SACOL’s ground-base observations. The MODIS/CERES radiative data are from CERES SSF MODIS
Edition 1B data on board Aqua. The most recentSingle Scanner Footprint (SSF) dataset combines theradiation budget data from CERES with cloud property retrievals from an imager on the same platform toprovide a vastly improved characterization of the in-NO. 6HUANG ET AL.

Fig. 8. Diurnal cycle of seasonal mean net radiation for
period the of May 2006 to May 2008.

915

Fig. 10. Diurnal cycle of seasonal mean aerosol optical
thickness for the period of May 2006 to May 2008.

Fig. 11. Diurnal cycle of seasonal mean Angstrom Exponents for the period of May 2006 to May 2008.

Fig. 9. Comparisons of MODIS Aqua retrieved (a)downward and (b) net shortwave radiative properties
with ground-based observations.

stantaneous state of the atmosphere (Chambers et al.,2002). The satellite-based downward and net SW
ﬂuxes overestimate the instruments by an average of
9.84 W m−2 (or 1.25%) and 26.05 W m−2 (or 4.29%).
Because the downward ﬂux depends primarily on theatmospheric gas and aerosol proﬁles and less on thesurface albedo or satellite calibration, the bias ofdownward SW ﬂux is smaller than the net SW ﬂux.
The reason for the larger bias of net SW ﬂux may
be due to the occurrence of convective clouds with avariety of vertical structures that cause a deviationfrom the plane-parallel radiative transfer assumed in
the parameterization. On the other hand, the CERES
narrowband-broadband calibration may not be optimal for this area.
The diurnal cycles of seasonal mean AOD at 550
nm are presented in Fig. 10, which are observed byusing a Sun-Photometer (CE-318) at SACOL for a period of one year from August 2006 to July 2007. The
annual averaged AOD is 0.33 and seasonal means are
0.418, 0.356, 0.315, 0.242 for spring, summer, autumn
and winter, respectively. The largest averaged valuesof AOD are found in spring season due to the dust
weather events. The diurnal cycle shows a gradual increase in the morning, reaching the maximum around1100 LST, and then begins to decrease later in the af-

916

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

Fig. 12. Diurnal cycle of seasonal mean concentration of
black carbon for the period of May 2006 to May 2008.

ternoon during spring. The diurnal variation of thewinter season is similar to the spring. However, there
are two strong AOD peaks in summer, the ﬁrst one appears at about 1000 LST (Beijing Time) and the second comes around 1400 LST. The variability of AOD
is the largest in the summer. The autumn AOD issigniﬁcantly lower than other seasons and both AOD
averaged value and variability are the smallest in autumn.
Figure 11 shows the diurnal cycle of seasonal mean

VOL. 25

Angstrom Exponents α at SACOL for the same period as in Fig. 10. The Angstrom Exponent α gradually digresses from summer, autumn, winter, to spring.
The annual averaged Angstrom Exponent α is 0.93 andseasonal mean are 1.17, 1.03, 0.98, 0.53 for summer,
autumn, winter, and spring, respectively. It suggeststhat the ﬁne particles contribute a much larger aerosol
fraction in summer, while the coarse particles are themain parts in spring. The mean α value of springis 140% less than it is in summer. This diﬀerence isrelated to summer rainfall and spring dust weather
events. In spring, the diurnal variation of α presents astable high value during the daytime. However, there
are two α peaks in the summer, one appears at 1200
LST and the other appears at 1500 LST. The variability in summer is also larger than any other season. In
autumn, α increases in the morning reaching the maximum at 1200 LST, then it begins to decrease gradually.
In the winter, the value of α varies between 0.9 and
1.0.
Figure 12 shows the diurnal cycles of the seasonalmean concentration of Black Carbon (BC) for four seasons at SACOL for the period of April 2007 to March
2008. The structure of BC’s diurnal cycle is the significant double-peak pattern for all seasons. The doublepeak appears in the morning and evening, respectively.

Fig. 13. Frequency distributions of hourly averaged concentration of black carbon for the four
seasons for the period of May 2006 to May 2008.

NO. 6HUANG ET AL.

Dp (µm)10

1
0.5

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
2007
2008
10-3

10-2

10-1

0

10+1

10+2

10+3

Fig. 14. Seasonal average aerosol number size distribution at SACOL as deduced from APS (Aerodynamic Particle Sizer) measurements (0.542  Dp 10 µm) for period of June 2007 to May 2008. Dp denotes particle diameter.

Fig. 15. Seasonal average aerosol number size distribution at SACOL as deduced from APS (Aerodynamic Particle Sizer) measurements (0.542  Dp  10µm) for theperiod of June 2007 to May 2008. Dp denotes particle
diameter, and N particle numbers.

The concentration of BC shows a gradual digression
from winter, autumn, summer, to spring. The ﬁrst
peak of BC appears about one hour later in autumn
and winter than other seasons, which may be controlled both by the characteristics of the atmosphereboundary layer and anthropogenic inﬂuences.
Figure 13 shows the frequency distributions of917

hourly averaged concentration of BC for all seasonsat SACOL for the period of April 2007 to March 2008.
Based on log-normal statistics, the background concentration of Black Carbon in this area was estimatedto be approximately 850 ng m−3 , which is lower thanthe value in Wenjiang (2850 ng m−3 ) and higher than
the results from Mt. Waliguan (65 ng m−3 ). By using
the same method, the background concentrations in
diﬀerent seasons are 700 ng m−3 (spring) (Fig. 13a),
750 ng m−3 (summer) (Fig. 13b), 800 ng m−3 (autumn) (Fig. 13c), and 1750 ng m−3 (winter) (Fig.
13d), respectively. It can be found that the background concentration in winter is nearly twice theother seasons, due to increased coal consumption forheating. Although it is the rainy season in this region,
the summer concentration of BC is still much higher
than Mt. Waliguan.
The size-separated number concentrations in therange of 0.5 µm Dp 10 µm (Dp denotes particle
diameter) has been observed since June 2007 at the
SACOL by using an Aerodynamic Particle Sizer (TSIAPS Model 3321). The particle size measurements
permit the determination of a fraction of ﬁne mode
(0.5 µm Dp  1 µm) particles and coarse mode (1µm Dp 20 µm) particles with a 5-minute time resolution. The instrument is based on the quantiﬁcationof the 90◦ scattering of light by aerosol particles. Figure 14 shows the daily averaged aerosol number size
distribution for the period of June 2007 to May 2008.
The reasons for dismissing data were due to instrument
failures or rainy conditions. The averaged diurnal
size distributions is 921.46 m−3 (winter), 178.26 m−3(spring), 222.05 m−3 (summer) and 509.39 m−3 (autumn) for ﬁne mode particle (0.5 µm Dp  1 µm)
and 30.34 m−3 (winter), 10.28 m−3 (spring), 6.32 m−3(summer) and 12.36 m−3 (autumn) for coarse modeparticle (1 µm Dp 10 µm), respectively.
The APS continuous measurement conducted at
SACOL also permits the ability to obtain information
concerning the seasonal averaged size distribution forthe ﬁne mode and coarse mode. The seasonal averaged
size distributions for the period of June 2007 to May
2008 are shown in Fig. 15, which are characterized by
the highest number concentration in winter and thelowest in summer, with a typical shape of backgroundareas. The number concentration relative maximum
was evident at the Dp ∼0.7 µm (5th APS channel), indicating the possible shifting between ﬁne and coarse
mode. This high value of particles in winter may berelated the biomass burning, which can produce large
amounts of ﬁne mode particle. The aerosol size number concentration in spring is relatively lower than theother seasons for the coarse mode particle at SACOL.
Figure 16 shows the diurnal cycle of surface latent

918

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

Fig. 16. Diurnal cycle of seasonal mean surface ﬂuxes
for (a) latent heat and (b) sensible heat ﬂuxes for theperiod of May 2006 to May 2008.

Figure 17 displays the diurnal cycle of seasonalmean CO2 uptake at SACOL for the four seasons. The
CO2 ﬂuxes in Fig. 17 are normalized by the averageof each 30 min. The processing procedures applied
to CO2 dataset are the same as latent and sensible
heat ﬂux, except the air density ﬂuctuation eﬀect correction which is not applied to CO2 . In winter andspring, the CO2 ﬂuxes are less than 0.05 mg m−2 s−1(in the ﬁgures a negative value indicates a net ﬂux into
the vegetation, i.e., a sink) and there are no obvious
diurnal variations due to the bare surface and weak terrestrial carbon exchange. In summer and autumn, theCO2 ﬂuxes shows signiﬁcant diurnal variability witha negative maximum at 1000 LST and positive values
during the nighttime.
Figure 18 shows the temperature, relative humidity and liquid water contours to 10 km height retrieved
from a radiometric proﬁler with rain eﬀect mitigation. Surface temperature, relative humidity, pressure,cloud base temperature, rain ﬂag, and column integrated vapor and liquid are shown (counterclockwise)in the 24-h time series plots. Figure 19 shows normalized relative backscatter intensity as functions of local
time and altitude collected by the SACOL MPL on
26 June 2007. The aerosol back scatter was observed
between 1 and 3 km with a cloud layer from 5 to 8
km during night and early morning, and this aerosol
layer persisted the whole day. A mixed aerosol-cloudlayer between 3 and 5 km was also observed during
the morning hours. Figures 18 and 19 suggest thatthe microwave radiometer and lidar are useful in determining the vertical structure of cloud and aerosol
layers.
6.

Fig. 17. Diurnal cycle of seasonal mean CO2 ﬂux for theperiod of May 2006 to May 2008.

heat and sensible heat ﬂux at SACOL. In general, it
shows that latent heat ﬂux is high with a maximum
around noon (1100 LST to 1600 LST) for summer (Fig.
16a). The averaged sensible heat ﬂux is positive during the daytime, i.e. sensible heat ﬂux is transported
away from the surface (Fig. 16b). However, thereis negative sensible heat ﬂux at during the nighttime(Fig. 16b).

VOL. 25

Concluding remarks

The observational facilities of SACOL represent aunique resource for climate research and should allow
the community to focus its eﬀort on the high priority
problem of understanding semi-arid climates. SACOL
employs a multiplatform approach, with state-of-theart instrumentation deployed at the surface to provide a comprehensive description of atmosphere andland surface thermodynamic state, particulate composition, aerosol, cloud and radiative properties. This
integrated dataset is being used to improve the understanding of aerosol-climate interaction, and to test andimprove climate model and satellite remote sensing
techniques. Results of the analyses performed to datehave been summarized in this paper and are reported
in more detail elsewhere in other papers. SACOL’s
rich data can be accessed online through the SACOL
home page at http://climate.lzu.edu.cn.

NO. 6919

HUANG ET AL.

Fig. 18. Temperature, relative humidity and liquid water contours to 10 km height
retrieved from a radiometric proﬁler with rain eﬀect mitigation. Surface temperature, relative humidity, and pressure, cloud base temperature, rain ﬂag, and column
integrated vapor and liquid are shown (counterclockwise) in the 24-h time series plots.

Fig. 19. Micro-Pulse Lidar normalized relative backscatter intensity for 26 June 2007.
Acknowledgements. SACOL was sponsored by
Lanzhou University through 985 Program; the National
Basic Research Program of China under Grant No.
2006CB400501, and the National Natural Science Foundation of China under Grant Nos. 40633017 and 40725015.
We are grateful for the excellent support provided by thepeople at the College of Atmospheric Sciences, Lanzhou
University. We also thank, Drs. Daren Lü, Jianmin Xu,
Ronghui Huang, Guoxiong Wu, Si-Chee Tsay, Zhanqing
Li, Qiang Fu, Qilong Min, and Qiang Ji, for their usefulsuggestions.

REFERENCES
Alexandrov, M. D., A. A. Lacis, B. E. Carlson, and
B. Cairns, 1999: Remote sensing of atmosphericaerosols, nitrogen dioxide, and ozone by means ofmulti-ﬁlter rotating shadow-band radiometer. Remote Sensing of Clouds and Atmosphere, J. Russell,and C. Serio, Eds., Proc. SPIE 3867, 156–170.
Bao, Y., and S. Lu, 2006: Review of land-atmosphereinteraction research in Arid and Semi-arid regions.
Journal of Desert Research, 3, 134–140. (in Chinese)
Breon, F.-M., D. Tanre, and S. Generoso, 2002: Aerosol

920

AN OVERVIEW OF THE SACOL OVER THE LOESS PLATEAU

eﬀect on cloud droplet size monitored from satellite.
Science, 295, 834–838.
Chambers, L. H., B. Lin, and D. F. Young, 2002: Examination of new CERES data for evidence of tropicaliris feedback. J. Climate, 15, 3719–3726.
Chinese Editorial Committee of Soil Analysis, 1996: Soil
Physical and Chemical Analysis and Description of
Soil Profile. China State Standards Press, 266pp. (inChinese)
Crowley, T. J., 2000: Causes of climate change over the
past 1000 years. Science, 289, 270–277.
DeMott, P. J., K. Sassen, M. Poellot, D. Baumgardner, D. C. Rogers, S. Brooks, A. J. Prenni, and S.
M. Kreidenweis, 2003: African dust aerosols as atmospheric ice nuclei. Geophys. Res. Lett., 30, 1732,
doi:10.1029/2003GL017410.
Foken, T., M. Göckede, M. Mauder, L. Mahrt, B. D.
Amiro, and J. W. Munger, 2004: Post-ﬁeld data
quality control. Handbook of Micrometeorology: A
Guide for Surface Flux Measurements, Lee et al.,
Eds., Kluwer, 181–208.
Harrison, L., and J. Michalsky, 1994: Objective algorithms for the retrieval of optical depths fromground-based measurements. Appl. Opt., 33, 5126–
5132.
Harrison, L., J. Michalsky, and J. Berndt, 1994: Automated multiﬁlter rotating shadow-band radiometer:
An instrument for optical depth and radiation measurements. Appl. Opt., 33, 5118–5125.
Holben, B. N., and Coauthors, 1998: AERONET—A
federated instrument network and data archive foraerosol characterization. Remote Sens. Environ., 66,
1–16.
Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P.
J. van der Linden, and D. Xiaosu, 2001: Climate
Change 2001: The Scientific Basis. Cambridge University Press, 944pp.
Hu, Y., 1994: Research advance about the energy budget and transportation of water vapor in the HEIFE
area. Advance in Earth Sciences, 9, 30–34. (in Chinese)
Hu, Y., and Y. Gao, 1994: Some new understandings ofprocesses at the land surface in arid area from theHEIFE. Acta Meteorologica Sinica, 52, 285–296. (inChinese)Huang, J. P., B. Lin, P. Minnis, T. Wang, X. Wang, Y.
Hu, Y. Yi, and J. R. Ayers, 2006a: Satellite-based
assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia. Geophys.
Res. Lett., 33, doi: 10.1029/2006GL026561.
Huang, J. P., P. Minnis, B. Lin, T. Wang, Y. Yi, Y.
Hu, S. Sun-Mack, and K. Ayers, 2006b: Possible
inﬂuences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS
and CERES. Geophys. Res. Lett., 33, L06824, doi:
10.1029/2005GL024724.
Huang, R., 2004: Advances of the project of the formation
mechanism and p theory of severe climatic disasters
in China. China Basic Science, 4, 6–16. (in Chinese)VOL. 25

Iwasaka, Y., and Coauthors, 2003: Nature of atmosphericaerosols over the desert areas in the Asian continent:
chemical state and number concentration of particles
measured at DunHuang, China. Water, Air, and Soil
Pollution: Focus, 3, 129–145.
Iwasaka, Y., and Coauthors, 2004: Pool of the dust particles over the Asian continent, balloon-borne optical particle counter and ground-based lidar measurements at Dunhuang, China. Environmental Monitoring and Assessment, 92, 5–24.
Janssens, I. A., A. S. Kowalski, B. Longdoz, and R. Ceulemans, 2000: Assessing forest soil CO2 eﬄux: An in
situ comparison of four techniques. Tree Physiology,
20, 23–32.
Kawamoto, K., and T. Nakajima, 2003: Seasonal variation of cloud particle size from AVHRR remote sensing. Geophys. Res. Lett., 30, 1810–1813.
Liu, H., W. Dong, C. Fu, and L. Shi, 2004: The longterm ﬁeld experiment on aridiﬁcation and the ordered human activity in semi-arid area at Tongyu,
Northeast China. Climatic and Environmental Research, 9, 352–378. (in Chinese)
Lu, D., and Coauthors, 2002: Composite study on
Inner Mongolia semiarid grassland soil-vegetationatmosphere interaction (IMGRASS). Earth Science
Frontiers, 9, 52–63. (in Chinese)
Lu, D., Z. Chen, J. Chen, G. Wang, J. Ji, H. Chen, and Z.
Liu, 2005: Study on soil-vegetation-atmosphere interaction in Inner Mongolia semiarid grassland. Acta
Meteorologica Sinica, 63, 33–55. (in Chinese)Mann, M. E., R. S. Bradley, and M. K. Hughes, 1999:
Northern hemisphere temperatures during the pastmillennium: inferences, uncertainties, and limitations. Geophys. Res. Lett., 26, 759–762.
Massman, W. J., 2000: A simple method for estimating
frequency response corrections for eddy covariancesystems. Agricultural Forest Meteorology, 104, 185–
198.
Massman, W. J., 2001: Reply to comment by Rannik on“A simple method for estimating frequency response
corrections for eddy covariance systems”. Agricultural Forest Meteorology, 107, 247–251.
Min, Q. 1996: Cloud properties derived from surface
MFRSR measurements and comparison with GOES
results at the ARM SGP site. Geophys. Res. Lett.,
23, 1641–1644.
Moore, C. J., 1986: Frequency response corrections foreddy correlation systems. Bound.-Layer Meteorol.,37, 17–36.
Ramanathan, V., P. J. Crutzen, J. T. Kiehl, and D.
Rosenfeld, 2001: Aerosols, climate, and the hydrological cycle. Science, 294, 2119–2124.
Ren, J. Z., 1998: Research Method of Prataculturae Science. Chinese Agriculture Press, 441pp. (in Chinese)
Rosenfeld, D., Y. Rudich, and R. Lahav, 2001: Desert
dust suppressing precipitation: A possible desertiﬁcation feedback loop. Proceedings of the National
Academy of Sciences, 98, 5975–5980.
Schotanus, P., F. T. M. Nieuwstadt, and H. A. R. De-

NO. 6HUANG ET AL.

Bruin, 1983: Temperature measurement with a sonicanemometer and its application to heat and moistureﬂuctuations. Bound.-Layer Meteor., 26, 81–93.
Twomey, S., 1977: Inﬂuence of pollution on shortwave
albedo of clouds. J. Atmos. Sci., 34, 1149–1152.
Webb, E. K., Pearman, G. I., and R. Leuning, 1980: Correction of ﬂux measurements for density eﬀects dueto heat and water vapour transfer. Quart. J. Roy.
Meteor. Soc., 106, 85–100.
Welton, E. J., J. R. Campbell, J. D. Spinhirne, andV. S. Scott, 2001: Global monitoring of clouds andaerosols using a network of micropulse lidar systems.

921

Vol. 4153, Proc. Lidar Remote Sensing for Industry and Environmental Monitoring, Sendai, Japan,
SPIE, 151–158.
Wilczak, J. M., S. P. Oncley, and S. A. Stage, 2001: Sonic
anemometer tilt correction algorithms. Bound.-Layer
Meteor., 99, 127–150.
Zhang, H. S., J. Y. Chen, and S.-U. Park, 2001: Turbulence structure in the unstable condition over various
surfaces. Bound.-Layer Meteor., 100, 243–261.
Zhang, Q., and Coauthors, 2005: NWC-ALIEX and its
advances. Advance in Earth Sciences, 20, 60–74. (inChinese)