Sciences in Cold and Arid Regions  2016, 8 (2): 103-115   PDF    

Article Information

MaoShan Li, ZhongBo Su, YaoMing Ma, XueLong Chen, Lang Zhang, ZeYong Hu. 2016.
Characteristics of land-atmosphere energy and turbulent fluxes over the plateau steppe in central Tibetan Plateau
Sciences in Cold and Arid Regions, 8(2): 103-115

Article History

Received: July 27, 2015
Accepted: November 23, 2015
Characteristics of land-atmosphere energy and turbulent fluxes over the plateau steppe in central Tibetan Plateau
MaoShan Li1 , ZhongBo Su2, YaoMing Ma3, XueLong Chen2, Lang Zhang3, ZeYong Hu1     
1. Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
2. Faculty of Geo-Information Science and Earth Observation of the University of Twente, Enschede, The Netherlands;
3. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Abstract: The land-atmosphere energy and turbulence exchange is key to understanding land surface processes on the Tibetan Plateau(TP).Using observed data for Aug.4 to Dec.3,2012 from the Bujiao observation point(BJ) of the Nagqu Plateau Climate and Environment Station(NPCE-BJ),different characteristics of the energy flux during the Asian summer monsoon(ASM) season and post-monsoon period were analyzed.This study outlines the impact of the ASM on energy fluxes in the central TP.It also demonstrates that the surface energy closure rate during the ASM season is higher than that of the post-monsoon period.Footprint modeling shows the distribution of data quality assessments(QA) and quality controls(QC) surrounding the observation point.The measured turbulent flux data at the NPCE-BJ site were highly representative of the target land-use type.The target surface contributed more to the fluxes under unstable conditions than under stable conditions.The main wind directions(180°and 210°) with the highest data density showed flux contributions reaching 100%,even under stable conditions.The lowest flux contributions were found in sectors with low data density, e.g.,90.4% in the 360°sector under stable conditions during the ASM season.Lastly,a surface energy water balance(SEWAB) model was used to gap-fill any absent or corrected turbulence data.The potential simulation error was also explored in this study.The Nash-Sutcliffe model efficiency coefficients(NSEs) of the observed fluxes with the SEWAB model runs were 0.78 for sensible heat flux and 0.63 for latent heat flux during the ASM season,but unrealistic values of-0.9 for latent heat flux during the post-monsoon period.
Key words: turbulent energy flux     Asian summer monsoon     gap-filling     surface energy water balance model     central Tibetan Plateau    

1 Introduction

The exchange of energy,momentum, and mass between l and and atmosphere is key to underst and ing l and surface processes,which in turn affect atmospheric circulation and climate. The Tibetan Plateau(TP)is the world's highest and largest alpine region,with altitudes of >4,000 m a.s.l. spanning 25° of longitude. Owing to its high altitude and its consequent enormous effect on the regional and global energy and water cycles,the TP has been the focus of climate study since the mid-1970s(Flohn,1957; Yeh et al., 1957). A series of atmospheric science expeditions have been carried out on the TP,including the First Tibetan Plateau Meteorological Science Experiment(1979),Tibetan Plateau Surface Heat Resource Observations(Ji et al., 1986),the Second Tibetan Plateau Meteorological Science Experiment(TIPEX)(Xu et al., 2002), and the Coordinated and Enhanced Observation Period(CEOP)of the Asian-Australian Monsoon Project in Tibet(CAMP-Tibet)(Koike et al., 1999; Wang et al., 1999; Ma et al., 2005; Ma et al., 2006). Recently‚ work by the Japan International Cooperation Agency(JICA)supported observations made on the TP and its surrounding areas. We have studied the effect of dynamic and heat transport from the TP on the climates of China,East Asia, and indeed the whole world using all these datasets. Our results indicated that sensible heat flux transfers prevail during the pre-monsoon periods,but during the ASM(Asian summer monsoon)seasons,latent heat flux predominates(excluding the western TP)(Chen et al., 1985). There were clear energy imbalances during the ASM seasons(Bian et al., 2002; Yang et al., 2004).

Previous studies have shown that,pre-ASM,the plateau surface is the major energy source providing sensible heat flux to the atmosphere(Li and Yanai, 1996). During the rainy season,the latent heat released to the atmosphere is the dominant heat source over the eastern TP,but sensible heat flux values are comparable to latent heat flux values over the western TP(Chen et al., 1985). Over the past 50 years,scientists have focused closely on research into energy and turbulent fluxes over the TP. The First Tibetan Plateau Meteorological Science Experiment(1979)obtained some important results: during dry periods,the sensible heat flux is the major energy source delivering heat to the atmosphere,whereas latent heat flux values are greater than sensible heat flux values in wet periods(Zhang et al., 1988).

Many observations of the TP ground surface(Ji et al., 1986)have improved our underst and ing of l and -atmosphere interaction. In summer,the plateau surface is a heat source, and in dry periods the sensible heat flux is the major heat source; during the rainy season,the latent heat flux becomes as important as the sensible heat flux(Qian et al., 1997). Research shows that the ASM begins in June,is strongest in July, and retreats in October on the TP(Xu and Gao, 1962). The Bowen ratio(Bo)changes clearly with seasons on the TP; it is constant during the daytime before Sept. 27(ASM season),then changes with time. We therefore studied the monsoonal effect on energy fluxes for the ASM season and post-monsoon period separately,following Wang et al.(2005). Although many energy unbalance studies have been performed,none of them definitively determined the reasons for energy unbalance. Li et al.(2015)produced a meta-analysis of flux measurements with a more climatological focus,which revealed a significant dependence of the energy balance components on the altitude and on the l and -use types in the TP. Different stages of vegetation development and l and cover density of grassl and s can also lead to temporal variation of surface characteristics at a single site. The turbulent fluxes at an individual location are also influenced by these differences. Therefore,the first objective of this study was investigation of the flux and energy balance non-closure difference with changing time using observation data from the Nagqu Plateau Climate and Environment Station(NPCE-BJ)located in the center of the TP.

Footprint climatology analysis was applied to determine the contribution of target l and use to total flux(Göckede et al., 2004,2006; Rebmann et al., 2005). High mountains,lakes,ice, and grassl and s are distributed over the whole TP. Accordingly,the second objective of the present study was to investigate the presentation of the data processing and quality control of the flux data.

During the long monitoring period,data gaps occurred due to various reasons. Therefore,this study needed a gap-filling method to address the missing data,so we used a 1-D soil-vegetation-atmosphere transfer scheme(surface energy water balance,or SEWAB)developed by Mengelkamp et al.(1999,2001)to simulate the surface energy balance at the NPCE-BJ area,followed by statistical analysis to associate the biases and correlations between model outputs and observations.

2 Site description and experimental set-up

The Bujiao observation point(BJ)of the Nagqu Plateau Climate and Environment Station(NPCE-BJ)is located in the Nagqu area of the central TP. Its surface is essentially flat and open. Prior to the ASM period,the surface is very dry and coveredby dry grass; with the onset of the ASM,the surface becomes wetter and the grass starts to grow rapidly. The NPCE-BJ station consists of an eddy covariance(EC)tower,an automatic weather station(AWS)for measuring radiation and wind speed, and a planet boundary layer(PBL)tower with a soil measurement system to enable the detection of all surface energy balance components. A l and -use map of a 4km×4km area,with the location of the EC tower indicated as a black circle in the middle of the map,is given in Figure 1. The positions of the AWS and PBL tower near the EC tower are marked with a red cross.

Figure 1 Land-use map at the NPCE-BJ site

A turbulent flux measurement sonic anemometer(CSAT3,Campbell Scientific,Inc.)was set up 3 m above the ground at the NPCE-BJ site(31.37°N,91.9°E; 4,509 m a.s.1.). The precision of the measured horizontal wind speed was better than 0.04 m/s,while that of the vertical wind speed was better than 0.02 m/s. The density of water vapor(H2O) and the concentration of carbon dioxide(CO2)were obtained using an LI-7500 open-path gas analyzer(Li-COR Biosciences)for H2O and CO2 concentrations. This instrument responds quickly to the environment, and has been reliably used over long periods in the field. The data logging system used was the CR5000 control system(Campbell Scientific,Inc.). The measurement frequency of the EC system used was 10 Hz, and the flux data are output every 30 minutes. All EC data were processed and quality-controlled using the TK3.1 software package and produced turbulence datasets. Net radiation(Rnet)short-wave components were measured with a CM21 device(Kipp & Zonen,B.V.), and long-wave components with an Eppley precision infrared radiometer(PIR)(Eppley Laboratory,Inc.)at a height of 1.40 m above the surface. Subsurface soil heat flux was recorded at 0.05 m and 0.10 m depths with an HFP01 heat flux plate(Hukseflux Thermal Sensors); the soil temperature at 0.05,0.10,0.20,0.40,0.80 and 1.60 m depths was measured with a TR219-L probe(Tri-Tronics), and the soil moisture at 0.05,0.10,0.20,0.40,0.80 and 1.60 m depths was measured using a CS616-L water content reflectometer(Campbell Scientific,Inc.). Precipitation was measured using a Geonor model T-200B precipitation weighing gauge(Geonor,Inc.),which allows the measurement of both liquid and solid precipitation.

3 Turbulent data processing 3.1 TK3.1 processing

This research used observation data from Aug. 4 to Dec. 3,2012 at the NPCE-BJ site. The EC flux values of the NPCE-BJ turbulence data were processed and quality-controlled using the TK3.1 software package,an update of TK3(Mauder and Foken, 2011). The processing steps and flux corrections listed below were applied to the EC raw data following Mauder et al.(2006).

Calculation of averages,variances, and covariances for using averaged 30-min intervals and taking into consideration the time delays between different sensors, and excluding physically invalid values and spikes(Vickers and Mahrt, 1997).

Cross-wind correction of the sonic temperature.

Planar fit coordinate rotation(Wilczak et al., 2001).

Correction of spectral loss due to path-length averaging,spatial separation of the sensors, and the dynamic frequency effect of signals(Moore,1986).

Conversion of buoyancy into sensible heat flux(Schotanus et al., 1983; Liu et al., 2001).

Correction of density fluctuations(WPL correction)to determine fluxes in the scalar quantities of H2O and CO2(Webb et al., 1980; Fuehrer and Friehe, 2002; Liebethal and Foken, 2003,2004).

The impact of these processing and flux-correction steps on flux estimates and CEB(energy balance closure) has been discussed in Mauder et al.(2006). Previous quality tests implemented using TK3 consisted of a stationarity test and a test of the fulfilment of integral turbulence characteristics(ITC)for each turbulent flux(Foken and Wichura, 1996; Foken et al., 2004). According to Rebmann et al.(2005),the final quality flag(1-5)is assigned to a specific half-hourly turbulent flux value by combining the quality flags for stationarity and ITC. Classes 1-2 can be used for fundamental research and classes 3-4 for general use,such as continuously-running systems. Turbulent flux values marked with a class 5 quality flag should be rejected. This information is also provided in the NPCE-BJ turbulence database(Section 2)along with the footprint modeling results(Section 4.3 below).

3.2 Data selection

The following data selection criteria were applied to the results shown in Section 4 below. The data quality tests used is described in Section 3.1. Turbulent flux data within the following parameters were considered in thisanalysis:

TK3.1 quality flags <3(Section 3.1); and

Flux contributions from the target l and -use type >90%(Section 3.4 below).

3.3 Energy balance closure(CEB)

A possible residuum of the surface energy balance(Qres)was evaluated at all the measuring sites in the turbulence network where additional radiation and soil measurements were carried out and turbulent fluxes of sensible heat(QH), and latent heat(QE)were measured. At the surface,the net radiation(QS),is transformed into QH and QE and into the ground heat flux(QG),thus:

$ - {Q_S} = {Q_H} + {Q_E} + {Q_G} + {Q_{res}} $ (1)

The heat storage in the upper soil layer is included in the value of QG and was calculated according to the method of Yang and Wang(2008). Other storage terms(e.g.,plants,air) and photosynthesis can be neglected as they are usually very small for low-level vegetation(Foken,2008).

Several studies have suggested a significant imbalance(10%-40%)in the TP's energy budget(Kim et al., 2000; Bian et al., 2002). Sensible and latent heat fluxes were therefore corrected following Twine et al.(2000)as suggested by Foken(2008): The energy balance residuum(Qres)was distributed among the turbulent fluxes,while Bo was held steady; the energy balance correction method(EBC)was then employed to yield the corrected energy balance sensible(QH,EBC) and latent(QE,EBC)heat fluxes. In order to avoid unreasonably large corrections and artificial spikes,the correction was not applied when Bo was negative or if at least one of the turbulent fluxes failed to exceed the specified measurement of accuracy,which was assumed to be 10 W/m2 in absolute terms. Instead,such values were excluded from further analysis. After filtering and energy balance correction were complete,missing values accounted for up to 42% of the QH,EBC and QE,EBC records. Most of these gaps,however,occurred during the night,when the fluxes were known to be low. Nonetheless,gap-filling was necessary to obtain unbiased mean diurnal or longer-period flux values. The influence of gap-filling on the overall results is discussed in Section 4.3 below.

3.4 Footprint modeling

In order to evaluate the spatial representation of the measured turbulent flux data in the context of underlying l and -use distribution,a footprint model was applied to the NPCE-BJ EC flux site. Following the approach of Göckede et al.(2004,2006),the flux data-quality flagging scheme of Rebmann et al.(2005)was combined with the Thomson(1987)3-D Lagrangian stochastic trajectory Langevin-type model(Wilson and Sawford, 1996). The parameterization followed the flow statistics and the effect of stability on the profiles used in Rannik et al.(2003). The approach of Göckede et al.(2004,2006)allows the determination of footprint climatology in relation to l and use(spatial representation) and the spatial distribution of quality flags(spatial quality structure),as well as the percentage calculation of particular target l and -use-type flux contributions to the total flux measured for each 30-min turbulent flux period. Recently,this flux data-quality evaluation approach was adapted for use in CarboEurope network sites by Rebmann et al.(2005) and Göckede et al.(2008).

Table 1 shows the flux contribution from the target l and -use type at the NPCE-BJ station for different wind direction sectors and atmospheric conditions. Flux contributions from the target surface clearly increased as conditions moved from stable to unstable. The main wind directions(180° and 210°)with the highest data density(not shown)show flux contributions reaching 100%,even under stable conditions. The lowest flux contributions were found in sectors with low data density,e.g.,90.4% in the 360° sector under stable conditions during the ASM season. Overall,the measured turbulent flux data at the NPCE-BJ site were highly representative of the target l and -use type.

Table 1 Flux contributions from the "grass" target land-use type at the NPCE-BJ station from different wind direction sectors, and stability classes during monsoon season (upper) and post-monsoon (lower)
Season Stability Flux contribution from different wind direction sectors
30° 60° 90° 120° 150° 180° 210° 240° 270° 300° 330° 360°
Monsoon Average flux contribution from target land-use type "grass" (%)
Stable 94.4 99.0 99.4 98.9 99.0 100 100 99.9 97.6 99.7 100 90.4
Neutral 98.4 100 100 100 100 100 100 100 99.9 100 100 92.4
Unstable 99.0 100 100 100 100 100 100 100<