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Journal of Geographical Sciences >

2015 , Vol. 25 >Issue 12: 1411 - 1422

DOI: https://doi.org/10.1007/s11442-015-1242-3

Research Articles

The implication of mass elevation effect of the Tibetan Plateau for altitudinal belts

  • YAO Yonghui 1 ,
  • XU Mei 2, * ,
  • ZHANG Baiping , 1, 3
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*Corresponding author: Zhang Baiping (1963-), Professor, E-mail:

Received date: 2015-04-30

  Accepted date: 2015-05-27

  Online published: 2015-12-31

Supported by

National Natural Science Foundation of China, No.41571099.No.41001278

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

The heating effect (or mass elevation effect, MEE) of the Tibetan Plateau (TP) is intense due to its massive body. Some studies have been undertaken on its role as the heat source in summer and its implications for Asian climate, but little has been known of the implications of its MEE for the distribution of mountain altitudinal belts (MABs). Using air temperature data observed and remotely sensed data, MAB/treeline data, and ASTER GDEM data, this paper compares the height of MABs and alpine treelines in the main TP and the surrounding mountains/lowland and explains the difference from the point of view of MEE. The results demonstrate: 1) at same elevation, air temperature and the length of growing season gradually increase from the eastern edge to the interior TP, e.g., at 4500 m (corresponding to the mean altitude of the TP), the monthly mean temperature is 3.58°C higher (April) to 6.63°C higher (June) in the interior plateau than in the Sichuan Basin; the 10°C isotherm for the warmest month goes upward from the edge to the interior of the plateau, at 4000 m in the Qilian Mts. and the eastern edges of the plateau, and up to 4600-5000 m in Lhasa and Zuogong; the warmth index at an altitude of 4500 m can be up to 15°C·month in the interior TP, but much lower at the eastern edges. 2) MABs and treeline follow a similar trend of rising inwards: dark-coniferous forest is 1000-1500 m higher and alpine steppe is about 700-900 m higher in the interior TP than at the eastern edges.

Key words： Tibetan Plateau; mass elevation effect; mountain altitudinal belt; treeline; the warmth index; the 10℃ isotherm in the warmest month

Cite this article

YAO Yonghui , XU Mei , ZHANG Baiping . The implication of mass elevation effect of the Tibetan Plateau for altitudinal belts[J]. Journal of Geographical Sciences, 2015 , 25(12) : 1411 -1422 . DOI: 10.1007/s11442-015-1242-3

1 Introduction

More than one hundred years ago, De Quervain (1904) proposed the concept of Massenerhebungseffekt (Mass elevation effect, briefly MEE) to account for the observed tendencies in temperature-related parameters, such as treeline and snowline, to occur at higher elevations in the central Alps compared to their outer regions. This phenomenon has also been discovered and reported in other places around the world (Leuschner, 1996; Holtmeier, 2003; Flenley, 2007; Barry, 2008). Due to MEE, growing season is relatively longer and warmer at any given elevation in the central mountain ranges. These favorable conditions make treeline rise for about 400 m higher in the central Alps compared to the outer ranges (Holtmeier, 2003). Grubb (1971) also stated that the upper limit of lowland rain forest is at about 700-900 m and that of lower montane rain forest at about 1200-1600 m on small, isolated mountains and outlying ridges of major ranges, whereas approximately 1200-1500 m and 1800-2300 m, respectively, on the main ridges of major ranges. Similar phenomena were observed on the TP, Andes and other large mountains and plateaus. In the southeastern TP, alpine treelines climb up to approximately 4600-4700 m (Troll 1973; Zheng and Li, 1990) and even higher (4900 m) on a few sunny slopes (Miehe et al., 2007), which represent the highest treeline in the Northern Hemisphere. The highest snowline in the Northern Hemisphere also distributes on the TP, at about 6000 m, but in the southwestern TP (Shi et al., 1992; Han et al., 2011).
The heating effect of the TP was identified as early as the 1950s. Flohn (1957) and Ye (1957) separately found that the TP was a summertime atmospheric heat source. Since then, it has been related to Eurasian weather and climate and even to the atmospheric general circulation (Ye, 1982; Ye and Wu, 1998; Yanai and Wu, 2006; Ye and Chang, 1974; Chen et al., 1985; Wu et al., 1997; Zhao et al., 2013). Flohn (1953) first proposed that elevated plateau surfaces, such as those of Tibet and the Altiplano in South America, are warmer in summer than the adjacent free air as a result of the altitudinal increase in solar radiation and the substantial longwave radiation at higher elevation. Barry (2008) noted that sensible heat transferred from the surface and the latent heat of condensation due to precipitation from orographically induced cumulus cloud development contributes to the heating effect in the mountain atmosphere. Ye (1982) calculated the sensible heat and the latent heat of the TP. Over the drier western part of the TP, the sensible heat flux is significant and the total daily sensible energy transfer from the plateau surface to the atmosphere reaches 220 Wm-2 in June. East of longitude 85°E, the latent and sensible heat fluxes are nearly identical (90 and 100 Wm-2, respectively). The maximum heating rates in June for the layer between 600 and 150 mb amount to +1.8°C day-1 from sensible heat, +1.4°C day-1 from latent heat, and radiative cooling of -1.5°C day-1, giving a net heating of +1.7°C day-1. Various estimates suggest that the heating is about 2°C day-1 over the eastern half of the Plateau (Chen et al., 1985). Moreover, due to the heating effect of the TP, it is an important negative vorticity source of the summer atmospheric movement (Wu et al., 2005).
Such substantial heating must have large effects not only on the climate of the TP but also the ecological patterns of the plateau, especially the spatial pattern of mountain altitude belts (Zheng and Li, 1990; Liu et al., 2003). Yao and Zhang (2013a, 2013b, 2013c, 2014) studied quantitatively the mass elevation effect of the plateau by comparing monthly mean air temperature differences at given elevations of 4000, 4500, 5000, 5500 and 6000 m between the main plateau, the Qilian Mts. in the northeastern corner of the plateau and the Sichuan Basin to the east of the plateau, and discussed the implications of mass elevation effect for treelines by considering the 10°C isotherm for the warmest month and warm index of 15°C·months. However, the implication of mass elevation effect for the whole ecological pattern of the plateau has been rarely involved. This paper intends to explore the implication of mass elevation effect for altitudinal belts with observed and estimated air temperature data, DEM and altitudinal belt data.

2 Study area

The study area is located between latitudes 25°-40°N and longitudes 75°-105°E (Figure 1), including the entire TP and adjacent areas. The plateau covers an area of nearly 2.5 million km2, mostly between 4000 and 6000 m above sea level (asl). The Himalayan, Hengduan and Kunlun Mountains are situated on the southern, eastern and northern borders of the plateau, respectively. The Gangdisê and Tanggula Mountains lie in the interior TP and divide the main plateau into three parts (i.e. the southern, central and northern main plateau). The Qaidam Basin, located in the northeast of the TP, is approximately only 3000 m asl and separates the Qilian Mts. from the main plateau.
Figure 1 Sketch map of the Tibetan Plateau and treeline and mountain altitudinal belt sites

3 Data and data sources

3.1 Air temperature data

Two kinds of air temperature data are used in this paper. Observed air temperature data were downloaded from the China Meteorological Information Center (http://cdc.cma.gov.cn/ index.jsp). MODIS land surface temperature (LST) data were processed with meteorological data for 2001-2007 from 137 stations and the ASTER GDEM data (Yao and Zhang, 2013). The MODIS LST data were the Terra Monthly Land Surface Temperature/Emissivity (MOD11C3) product at 0.05° geographic Climate Modeling Grid (CMG) spatial resolution, downloaded from the Land Processes Distributed Active Archive Center (https://lpdaac. usgs.gov/lpdaac/products/modis_products_table). Geographical weighted regression (GWR) methods were used to estimate air temperature, and the root mean square error (RMSE) for each month ranges from 1.13°C for August to 1.53°C for March. These data such estimated are spatially continuous and contain more detailed air temperature information than the observed but rather scattered data.

3.2 Mountain altitudinal belt (MAB) and treeline data

Zhang et al. (2008, 2009) collected 544 spectra of mountain altitudinal belts, 594 sites for treeline data, and 148 sites for snowline data from the published literature for the global and integrated them into a digital mountain altitudinal belt information system. Of them, a total of 267 treeline and 39 MAB data are used in this paper.

4 Methods

Montane dark coniferous forest, alpine shrub meadow, alpine meadow, and treelines are taken into account along three profiles (the Mt. Erlang-Nyainqentanglha profile: along Mt. Erlang-Mt. Gaoshi-Nyainqentanglha Mts.; the Mt. Guangguang-Tuoba Beishan profile: along Mt. Guangguan-Mt. Siguliang-Tuoba Beishan, and the Mt. Wutai-Amugang profile: along Mt. Wutai-Tanggula Mts. -Amugang), to study the distribution principles of the limits of MABs, and to calculate the mass elevation effect as temperature difference at the same elevation between the interior TP and the outer edges. Lastly, the mean temperature and the 10°C isotherm for the warmest month and the warm index of 15°C·months are used to study the implications of mass elevation effect for the MABs.
Firstly, according to the estimated air temperature data and ASTER GDEM, air temperatures and altitudes at the sites of MABs are extracted. Secondly, for the east edges or the neighboring areas, the extracted air temperatures were adjusted to the higher altitudes by Equation (1):
where is the lapse rate, T is the air temperature at a height h and Tadjusted is the adjusted air temperature at an elevation H. There are several lapse rate data available in the western Sichuan Basin, between 0.42 and 0.60°C/100 m-1 (Liu, 1992; Wu, 1996; Zheng et al., 1986; Xie 2006), and the lapse rates measured in Mt. Emei (0.55°C/100 m-1 for July and 0.51°C/ 100 m-1 for January) is relatively creditable, for Mt. Emei is located at the southwestern edge of the Sichuan Basin and at the easternmost edge of the Hengduan Mountains Therefore, the lapse rates of Mt. Emei were selected for the air temperature adjustment calculations in this paper (Table 1).
We then projected the elevation at which occurs the 10°C isotherm for the warmest month mean temperature and the warmth index (WI) at 4500 m (which corresponds to the mean elevation of the main plateau) based on the estimated monthly mean air temperatures and the ASTER GDEM data. The 10°C isotherms were extracted from the estimated air temperature data, and the corresponding altitudes were obtained from the ASTER GDEM data using ArcGIS. WI at the mean elevation of the main plateau (4500 m asl) is calculated as follows:
where t is the monthly mean air temperature at 4500 m asl and WI is the sum of (t-5) for months in which t exceeds 5°C (Kira, 1948; Ohsawa, 1990). Previous studies have shown that the warmest month 10°C isotherm and 15°C·month warmth index exhibit the best overall occurrence of forest upper limits (Troll, 1973; Ohsawa, 1990). These climatic indexes could be used to explore the potential altitude of treelines and the correlation between mass elevation effect and the MABs/treeline position.

5 Results

5.1 MABs and treelines rise gradually from the easternmost to the interior plateau

(1) Montane dark coniferous forest
Along the Mt. Erlang-Nyainqentanglha profile, montane dark coniferous forest rises westwards: at about 2200-2900 m in the westernmost Sichuan Province (such as Mt. Emei and Mt. Erlang), at about 3000-4000 m at the eastern edges of the Plateau (such as Mt. Gongga, Mt. Zheduo and Mt. Gaoshi), up to 3000-4300 m in the Hengduan Mts. (such as Mt. Shaluli and Mt. Ningjing), and even up to 3200-4500 m in the interior TP (Figure 2a). The same trend is observed along the Mt. Guangguang-Tuoba Beishan profile: about 2400-3700 m of the eastern edges of the Plateau (such as Beichuan, Mt. Guangguang, and Mt. Siguliang), and about 3200-4400 m westward to the interior TP (such as Changdu) (Figure 2b).
Figure 2 Mountain altitudinal belts along three profiles (a. the Mt. Erlang-Nyainqentanglha profile; b. the Mt. Guangguang-Tuoba Beishan profile; c. the Mt. Wutai-Amugang profile)
(2) Alpine shrub meadow and alpine meadow belts
Alpine meadow is at about 3700-4500 m in the eastern edges of the Plateau and rises to about 4400-5400 m in the interior TP. For example, along the Mt. Erlang-Nyainqentanglha profile, it is at 4000-4500 m at the eastern edges of the plateau (such as at Mt. Gaoshi) and about 4400-5400 m in the interior TP (such as at Bomi and Nyainqentanglha Mts.) (Figure 2a). Along the Mt. Guangguang-Tuoba Beishan profile, it is at about 3700-4200 m at the eastern edge of the plateau (such as at Mt. Guangguang and Mt. Siguliang) and 4500-5400 m in the interior TP (westward to Changdu) (Figure 2b). Along the Mt. Wutai-Amugang profile, it is at 3700-4400 m at the eastern edges of the Plateau, 4100-4800 m westward to the Qumalai county, 4400-5000 m in the Tangula Mts., and 5000-5400 m westward further to the interior TP (such as at Amugang) (Figure 2c).
(3) Treelines
Treelines also rise from the easternmost to the interior TP. Treelines are typically below 3700 m along the eastern edge of the TP. For example, 3200 m in the Qilian Mts., about 3250 m on Mt. Erlang, 3700 m on Minshan and Mt. Gongga. They ascend to about 4000 m east of the Maqin-Daofu-Jiulong line, and to 4600-4700 m westward in Zuogong and Lhasa, even up to 4900 m on some sunny slopes (Figure 1). Normally, treelines are about 1000-1500 m higher in the interior Plateau than in the eastern edges and adjacent lowlands.
In short, MABs and treelines all gradually ascend from the easternmost to the interior TP. This is the so-called “mass elevation effect (MEE)” (De Quervain, 1904; Grubb, 1971; Holtmeier, 2003), namely, growing season is longer and warmer at any given elevation in the interior mountains than in the outer mountain ranges. This makes treelines rise by about 400 m in the central Alps compared to the outer ranges (Barry, 2008; Holtmeier, 2003). As for the TP, MEE is much stronger due to its supersize body.

5.2 Mass elevation effect of the TP

(1) Gradual increase of air temperatures at the same elevation from the easternmost to the interior plateau
The air temperatures at the MABs sites on the eastern edges are adjusted to the altitudes of the sites on the interior TP by Equation (1) (shown in Table 2). Along the Mt. Erlang- Nyainqentanglha profile, monthly mean temperature is below -12°C for January and 2°C for July at 5292 m on the east of Mt. Gaoshi, and it is above -11°C and 4°C, respectively, at the same elevation in the interior TP. The same trend can be seen along the Mt. Guangguang- Tuoba Beishan profile and along the Mt. Wutai-Amugang profile (Table 2). That is to say, air temperatures at same elevations increase gradually from the easternmost to the interior plateau.
(2) Mass elevation effect of the Tibetan Plateau
Differences in monthly air temperature between the interior TP and the eastern edges of the plateau at same altitude are calculated as the measurement of mass elevation effect. Leshan, Pingwu and Lintao are three stations located at the eastern edges of the plateau or neighboring lowland, and their temperatures are adjusted to the altitudes of the stations in the interior TP with Equation (1) based on the lapse rates of Mt. Emei (Table 1). The results are shown in Table 3. At the altitude of Lhasa station (3648.9 m), the minimum and maximum differences between Lhasa and Leshan are 7.3°C (July) and 11°C (June); at the altitude of Zuogong station (3780 m), the minimum and maximum differences between Zuogong and Leshan are 4.2°C (November) and 8.8°C (June). Similarly, at the altitude of Anduo station (4800 m), the minimum and maximum differences between Anduo and Pingwu are 2.0°C (November) and 6.8°C (June); at the altitude of Seda station (3893.9 m), the minimum and maximum differences between Seda and Pingwu are 0.5°C (November) and 4.0°C (June). At the altitude of Wudaoliang station (4612.2 m), the minimum and maximum differences between Wudaoliang and Lintao are 1.5°C (March) and 4.2°C (January).
Table 1 Reported lapse rates of the Mt. Emei (°C/100 m)
Month Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec.
Lapse rate 0.51 0.53 0.56 0.57 0.60 0.60 0.55 0.56 0.54 0.53 0.55 0.49
Table 2 Temperatures and the adjusted temperatures in January and in July near the locations of MABs along the three W-E profiles
Profiles MAB sites Longi-
tude		 Lati-
tude		 Altit-
ude
(m)		 Air tem-
perature
in Jan.
(°C)		 Air tem-
perature
in July
(°C)		 Adjusted
height
(m)		 Adjusted
air tem-
perature
in Jan.
(°C)		 Adjusted
air tem-
perature
in Jul.
(°C)
Mt.Erlang-
Nyainqentanglha		 Langkazi-Yangzhuoyong 90.50 29.70 5292 -10.23 6.58 5292 -10.2 6.6
Bomi-Yigong 94.62 30.17 5096 -10.08 8.12 -11.1 7.0
Zhuka-Dongdala shady slope 98.64 29.70 4496 -5.64 10.12 -9.7 5.7
Zhuka-Dongdala sunny slope 98.56 29.75 4050 -4.23 11.36 -10.6 4.5
East slope of Mt. Ningjing 99.00 29.83 3455 0.46 14.98 -8.9 4.9
South range of Shaluli 99.73 29.75 4483 -4.75 10.35 -8.9 5.9
Mt. Gaoshi 101.00 30.03 3226 -1.91 13.37 -12.5 2.0
Mt. Zheduo 101.80 30.10 4218 -7.37 9.46 -12.5 3.6
South slope of Mt.Gongga 102.07 29.39 2077 -2.45 15.90 -12.8 -1.8
West slope of Mt. Erlang 102.56 30.12 3199 -5.07 13.12 -18.8 1.6
East slope of Mt. Emei 103.45 29.58 473 4.96 26.65 -21.5 0.1
Mt.Guang-
guang-
Tuoba
Beishan		 Southern Qiangtang Plateau 90.00 31.33 4943 -12.24 8.44 4943 -12.24 8.44
Naqu-Nierong 92.17 31.63 4636 -10.18 9.96 -11.75 8.28
Shady slope of Biru-Baqing 94.59 31.50 4417 -8.79 9.85 -11.48 6.95
Lancang River: Angqu-Shangka 96.84 31.45 3605 -3.47 13.73 -10.29 6.37
Lancang River: Changdu 97.17 31.45 3533 -5.84 12.78 -13.03 5.03
Lancang River: Zhaqu-Weng
Dagang		 97.21 31.52 3675 -2.31 13.89 -8.78 6.92
East slope of Beishanin Tuoba 97.97 31.53 4321 -9.02 9.22 -12.19 5.80
Xinlong of Mt. Daxueshan 100.31 30.94 3189 -4.21 12.62 -13.15 2.98
Songlinkou-Daofu of Mt. Daxue 101.12 30.98 2934 -2.18 15.82 -12.42 4.77
Big/small Jinchuan 102.06 31.48 2540 -0.02 17.60 -12.28 4.38
Mt. Siguliang 102.90 31.10 3950 -10.54 11.90 -15.60 6.44
Mt. Balang 103.17 31.07 4047 -7.63 7.89 -12.20 2.96
South slope of Dabanzhao 103.05 31.67 3794 -11.93 11.16 -17.79 4.84
Mt. Guangguang 103.61 31.01 745 5.61 25.53 -15.80 2.44
Small Zhaizigou 103.80 31.35 3392 -5.10 13.64 -13.01 5.11
Mt.Wutai- Amugang Shady slope of Memar
Tso Xishan		 81.83 34.45 5090 -14.31 8.30 5742 -17.64 4.71
Sunny slope of Memar
Tso Xishan		 82.38 34.28 5282 -14.24 7.62 -16.59 5.09
East slope of Amugang 85.50 33.50 5742 -17.99 5.15 -17.99 5.15
Tanggula Mts. 89.92 33.20 5018 -13.86 8.50 -17.55 4.52
Qumalai County 95.78 34.13 4149 -11.30 11.10 -19.42 2.34
Shady slope of Hutou Shan 103.22 34.07 2471 -9.23 17.00 -25.92 -0.99
Xuebaoding 103.62 32.88 3723 -9.32 10.22 -19.62 -0.88
Shady slope of Mt. Tutai 103.70 33.07 3622 -11.47 11.16 -22.28 -0.50
Table 3 Temperature and temperature differences of typical observation stations between the main plateau and surrounding areas (°C)
Stations Lat. Long. Elev.(m) Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec.
Lhasa 29.7 91.1 3648.9 0.6 3.0 6.3 8.7 12.7 16.1 16.5 16.1 14.2 9.7 4.0 0.8
Zuogong 29.7 97.8 3780 -3.9 -1.9 1.5 5.0 8.7 13.0 13.3 12.8 10.9 6.2 0.0 -3.4
Leshan 29.6 103.8 424.2 7.4 10.7 14.3 19.0 22.4 24.4 26.9 26.0 22.8 18.3 14.2 8.7
TLhasa-Leshan 3648.9 9.7 9.4 10.1 8.2 9.6 11.0 7.3 8.2 8.9 8.4 7.5 7.8
TZuogong-Leshan 3780 5.9 5.2 6.0 5.1 6.4 8.8 4.8 5.6 6.2 5.6 4.2 4.3
Anduo 32.4 91.1 4800 -12.3 -10.4 -6.4 -2.1 2.0 6.0 8.4 8.2 5.3 -1.2 -8.5 -11.3
Seda 32.3 100.3 3893.9 -9.6 -6.7 -3.0 1.6 4.9 8.7 10.7 10.1 7.3 1.7 -5.2 -8.4
Pingwu 32.4 104.5 893.2 4.7 7.9 11.7 16.2 19.8 22.7 24.8 23.3 19.6 15.4 10.9 5.5
TAnduo-Pingwu 4800 2.9 2.4 3.8 4.0 5.6 6.8 5.1 6.7 6.8 4.1 2.0 2.4
TSeda-Pingwu 3893.9 0.9 1.3 2.1 2.5 3.1 4.0 2.4 3.6 3.9 2.2 0.5 0.8
Wudaoliang 35.2 93.1 4612.2 -15.3 -13.0 -9.6 -4.5 -0.8 3.1 6.7 6.2 2.7 -4.3 -11.0 -14.1
Lintao 35.4 103.9 1893.8 -5.7 -0.7 4.2 9.5 13.7 17.1 19.3 18.7 13.8 8.3 1.7 -4.5
TWudaoliang-Lintao 4612.2 4.2 2.1 1.5 1.5 1.8 2.3 2.3 2.7 3.5 1.8 2.2 3.7
Yao and Zhang (2014) calculated the air temperature differences at the altitude of 4500 m between the main plateau and the adjacent lowlands or at the eastern edges. The temperature difference between the southern plateau and the Sichuan Basin is 5.25°C for the coldest month (January) and 4.86°C for the warmest month (July); the minimum and maximum differences are 3.58°C (April) and 6.63°C (June), respectively (Table 4). In short, monthly mean air temperature in the main plateau is approximately 2-7°C higher than in the surrounding mountains and adjacent lowland areas. This analysis verifies that the main plateau is warmer than its surroundings and adjacent lowland areas at the plateau surface elevation and gives rise to so-called “mass elevation effect” of the plateau.
Table 4 Monthly temperatures and temperature differences (ΔT) between the main plateau and the surrounding/adjacent lowland areas at an altitude of 4500 m (°C)
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec.
Main Plateau -10.19 -8.16 -4.73 -0.1 3.77 7.83 9.94 9.59 6.9 0.13 -6.3 -9.14
Hengduan Mts. -7.07 -5.28 -2.27 0.83 5.21 8.2 10.08 9.93 7.2 2.12 -3.84 -6.52
Southern main TP -7.55 -6.08 -2.85 0.83 4.42 8.5 10.22 9.89 7.5 2 -3.6 -6.4
Central main TP -10.8 -8.26 -4.41 0.21 4.02 8.45 10.56 10.11 7.39 0.49 -6.22 -9.56
Northern main TP -13.48 -11.17 -7.58 -1.37 3.01 6.95 9.44 9.00 6.16 -2.41 -9.55 -12.31
Qilian Mts. -17.41 -15.58 -12.08 -4.19 2.11 6.13 8.94 7.93 4.4 -5.44 -13.08 -17.16
Sichuan Basin -12.8 -10.29 -7.16 -2.75 -0.41 1.87 5.36 4.15 1.54 -2.23 -7.63 -10.94
ΔTHengduan-Sichuan 5.73 5.01 4.89 3.58 5.62 6.33 4.72 5.78 5.66 4.35 3.79 4.42
ΔTSouthern TP-Sichuan 5.25 4.21 4.31 3.58 4.83 6.63 4.86 5.74 5.96 4.23 4.03 4.54
ΔTCentral TP-Qilian 6.61 7.32 7.67 4.4 1.91 2.32 1.62 2.18 2.99 5.93 6.86 7.60
ΔTNorthern TP-Qilian 3.93 4.41 4.5 2.82 0.9 0.82 0.5 1.07 1.76 3.03 3.53 4.85

Note: cited from Yao and Zhang (2014).

5.3 Implication of mass elevation effect for the MABs/treelines

On both global and continental scales, temperature is, to a great extent, the final factor for determining treeline altitude and the MABs (Holtmeier and Broll, 2005). It has been also proved that the warmest month 10°C isotherm and the warmth index of 15°C·month coincide quite well with alpine treelines, especially in temperate areas (Troll, 1973; Ohsawa, 1990). The warmest month 10°C isotherm is below 4000 m in the Qilian Mts. and the eastern edges of the plateau, and ascends to 4000-4600 m in the Hengduan Mts. It goes up to 4600-4700 m westward in Zuogong and Lhasa and even up to 5000-6000 m westward in the areas of Ga’er-Gêrzê (Figure 3). Warmth index of 15°C·month occurs above the altitude of 4500 m in the Hengduan Mts. and the southern and central parts of the plateau.
The analysis above shows that air temperature is warmer in the interior than at the eastern edges of the plateau at given elevation due to its mass elevation effect. That explains why treelines are 1000-1500 m higher in the interior than in its outer slopes and surrounding areas of the TP.
Figure 3 Spatial distribution of the 10°C isotherm for the warmest month on the Tibetan Plateau

6 Discussion and conclusions

6.1 Discussion

(1) Precipitation is also significant for the distribution of treelines and MABs
Although the warmest month 10°C isotherm is at the highest elevation of 5000-6000 m in Ga’er-Gêrzê of the southwestern TP and the highest warmth index occurs in the central TP, the highest treeline in the Northern Hemisphere does not occur in the central or the southwestern TP but in the southeastern plateau. This is because tree growth requires a certain amount of annual precipitation, at least 500 mm in plain areas and 300 mm in some high mountains (Hou, 1982). In the southwest, north and heartland of the TP, the annual precipitation is only about 50-300 mm (Liao, 1990; Zheng and Li, 1990; Zhang et al., 2002; Wang et al., 2011). It amounts to 500-1000 mm in the southeastern part of the plateau. As a result, the highest treeline in the Northern Hemisphere occurs in the southeastern TP.
(2) Temperature lapse rate on the TP needs in-depth study
When studying mountain climates, temperature lapse rate is typically a necessary parameter (Rolland, 2003). However, there are few reports regarding the seasonal variation in lapse rates on TP. In this study, lapse rates measured on Mt. Emei were used for temperature adjustment calculations in the Sichuan Basin according to the reported references (Table 1). Strictly speaking, the lapse rate of Mt. Emei cannot be representative for the whole TP. Lapse rates on the TP may be smaller than that of Mt. Emei due to its MEE, and temperature lapse rates are usually steeper on isolated mountains near the sea than within extensive mountain ranges that provide their own heating (Hastenrath, 1968; Flenley, 1995). It has also been noted that lapse rates exhibit considerable variability in relation to the climatic zone, season (Hastenrath, 1968), air mass types (Yoshino, 1966) and local topography (Flenley, 2007; Barry, 2008). Thus monthly mean lapse rates on the TP and their spatiotemporal variation deserve a closer examination in the future.
(3) Comparative studies of mass elevation effect and its implications for MABs are necessary
Any massive mountains or plateaus must have great MEE. But the magnitude of MEE varies from mountain to mountain, and its implication differs for MABs and treelines in different regions. The highest snowline and treeline occur on the TP for the Northern Hemisphere, and in the central Andes for the Southern Hemisphere. The TP and the central Andes must have the greatest MEE. So, comparative studies of mass elevation effect and its implications for MABs will help disclose the mechanism of interactions between mountain complex ecological patterns and mass elevation effect.

6.2 Conclusions

(1) Due to MEE, air temperature is higher in the interior TP than in eastern edges at given elevations. At the average height (4500 m) of the TP, air temperatures are 3.58 (in April)-6.63°C (in June) higher in the interior TP than in the Sichuan Basin. The 10°C isotherm of the warmest month goes upward from the eastern border to the interior of the plateau, e.g., 4000 m in the Qilian Mts. and up to 4600-5000 m in Lhasa and Zuogong. Warmth index at an altitude of 4500 m in the interior TP can be up to over 15°C·month, but lower than 15°C·month at the same elevation at the eastern edges of the TP.
(2) Air temperature, MABs and treelines follow a similar trend of rising inwards due to MEE. Dark coniferous forest is 1000-1500 m higher, alpine steppe belts 700-900 m higher, and treelines 1000-1500 m higher in the interior plateau than at the eastern edges.

The authors have declared that no competing interests exist.

References

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1
Barry R G, 2008. Mountain Weather and Climate. Boulder, USA: Cambridge University Press.Mountain environments are reaching the world environmental agenda of concern. The first edition of this book provided a well organized set of principles on how weather and climate processes operate in mountain environments; it was and remains the major reference on the subject. This second edition remains in the original format but adds new material, including updates and increased bibliography and stressing the importance of the temporal dimension of mountain climates and the potential sensitivity of these environments to global change processes.

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Chen Longxun, Reiter E R, Feng Zhiqiang, 1985. The atmospheric heat-source over the Tibetan Plateau: May-August 1979.Monthly Weather Review, 113: 1771-1790.Abstract Estimates of the time and space variability of the atmospheric heat source over Tibet are presented for the summer of 1979. These estimates rely on new data from the People's Republic of China allowing a better assessment of the surface heat fluxes, and on new satellite data from Nimbus-7 giving the radiation balance at the top of the atmosphere. Our estimates of the atmospheric heat source turned out to be considerably smaller than those provided earlier in the literature, mainly because of different assumptions of the drag coefficient. The atmospheric heat source over Tibet is mainly modulated by the release of latent heat. Over the southeastern and southwestern plateau regions the heat source appears to be in phase with the precipitation yield of the Indian summer monsoon, whereas central Tibet reveals an out-of-phase behavior. Over western Tibet there appears to be hardly any net import of moisture from outside the region, whereas the maintenance of the hydrological cycle over eastern Tibet requires moisture flux convergence from outside the region of up to 40% of the mean rainfall, in agreement with what is known about the surface hydrology of Tibet.

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3
De Quervain A, 1904. Die Hebung der atmosphärischen Isothermen in den Schweizer Alpen und ihre Beziehung zu den Höhengrenzen.Gerlands Beiträge zur Geophysik, 6: 481-533

4
Flenley J R, 1995. Cloud forest, the Massenerhebung effect, and ultraviolet insolation.Ecological Studies, 110: 150-155.As usually defined, the Massenerhebung or mountain mass elevation effect means the occurrence of physiognomically and sometimes floristically similar vegetation types at higher altitudes on large mountain masses than on small isolated peaks, especially those in or near the sea. Although the effect was first reported in the European Alps (Schroeter 1908) and in North America (where it is known as the Merriam Effect; Martin 1963), it is best known in the tropics. Perhaps its clearest expression is the occurrence of tropical mountain cloud forest (TMCF) (upper montane rain forest) at lower altitudes on isolated peaks than on the main mountain masses, which are taken as the norm (Figure 1).

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Flohn H, 1951. Some remarks on the annual trend of weather in the Scottish highlands.Quarterly Journal of the Royal Meteorological Society, 77(334): 674-675.No abstract is available for this article.

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Flohn H, 1957. Large-scale aspects of the “summer monsoon” in South and East Asia.Journal of the Meteorological Society of Japan, 75: 180-186.

7
Grubb P J, 1971. Interpretation of Massenerhebung effect on tropical mountains.Nature, 229(5279): 44-45.ABSTRACT THREE types of rain forest can generally be recognized on wet tropical mountains: lowland rain forest, lower Montane rain forest and upper Montane rain forest1–3. These forest types can be defined both by distinctive plant associations1 and by the altitudinal limits within which they lie. These limits, however, vary with the type of mountain. On small, isolated mountains and outlying ridges of major ranges, the upper limit of lowland rain forest is about 700–900 m and that of the lower Montane rain forest about 1,200–1,600 m, whereas on the main ridges of major ranges the limits are higher, approximately 1,200–1,500 m and 1,800–2,300 m, respectively4. This phenomenon is known as the ‘Massenerhebung’ effect.

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8
Han Fang, Yao Yonghui, Dai Shibaoet al., 2012. Mass elevation effect and its forcing on timberline altitude.Journal of Geographical Sciences, 22(4): 609-616.Abstract<br/><p class="a-plus-plus">The concept of mass elevation effect (massenerhebungseffect, MEE) was introduced by A. de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and snowline to occur at higher elevations in the central Alps than on their outer margins. It also has been widely observed in other areas of the world, but there have not been significant, let alone quantitative, researches on this phenomenon. Especially, it has been usually completely neglected in developing fitting models of timberline elevation, with only longitude or latitude considered as impacting factors. This paper tries to quantify the contribution of MEE to timberline elevation. Considering that the more extensive the land mass and especially the higher the mountain base in the interior of land mass, the greater the mass elevation effect, this paper takes mountain base elevation (MBE) as the magnitude of MEE. We collect 157 data points of timberline elevation, and use their latitude, longitude and MBE as independent variables to build a multiple linear regression equation for timberline elevation in the southeastern Eurasian continent. The results turn out that the contribution of latitude, longitude and MBE to timberline altitude reach 25.11%, 29.43%, and 45.46%, respectively. North of northern latitude 32°, the three factors’ contribution amount to 48.50%, 24.04%, and 27.46%, respectively; to the south, their contribution is 13.01%, 48.33%, and 38.66%, respectively. This means that MBE, serving as a proxy indicator of MEE, is a significant factor determining the elevation of alpine timberline. Compared with other factors, it is more stable and independent in affecting timberline elevation. Of course, the magnitude of the actual MEE is certainly determined by other factors, including mountain area and height, the distance to the edge of a land mass, the structures of the mountains nearby. These factors need to be included in the study of MEE quantification in the future. This paper could help build up a high-accuracy and multi-scale elevation model for alpine timberline and even other altitudinal belts.</p><br/>

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9
Hastenrath S, 1968. Certain aspects of the three-dimensional distribution of climate and vegetation belts in the mountains of Central America and southern Mexico.Colloquium Geography, 9: 122-130.

10
Hoch G, Körner C, 2005. Growth, demography and carbon relations of Polylepis trees at the world's highest treeline.Function of Ecology, 19(6): 941-951.ABSTRACT

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Holtmeier F K, 2003. Mountain Timberlines: Ecology, Patchiness, and Dynamics. Dordrecht, Boston: Kluwer Academic Publishers.This book aims to explain mountain timberlines as space- and time-related phenomena. After an introduction into the complexities of the subject, the history and present state of timberline research are outlined. Chapters on the tree species at timberline and on the relationship of timberline elevation to macroclimate, climate character and the mass-elevation effect follow. The main chapter deal...

12
Hou Xueyu, 1982. China Vegetation Geography and Dominant Plant Composition. Beijing: Science Press. (in Chinese)

13
Li Qiaoyuan, Xie Zichu, 2006. Analyses on the characteristics of the vertical lapse rates of temperature: Take Tibetan Plateau and its adjacent area as an example. Journal of Shihezi University (Natural Science), 24(6): 719-723. (in Chinese)

14
Liao Ke, 1990. The Atlas of the Tibetan Plateau. Beijing: Science Press. (in Chinese)

15
Liu Dongshen, Sun Honglie, Zheng Du, 2003. The Tibet Plateau research’s scientific paradigm, effect and its spiritual connotation. http: www2.cas.cn/html/Dir/2003/10/14/2458.htm. (in Chinese)

16
Liu Kaifa, 1992. Climate of the Emei Shan.Journal of Mianyang Agricultural College, 9(3): 44-48. (in Chinese)

17
Miehe G, Miehe S, Vogel Jet al., 2007. Highest treeline in the Northern Hemisphere found in southern Tibet.Mountain Research and Development, 27(2): 169-173.ABSTRACT

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Ohsawa M, 1990. An interpretation of latitudinal patterns of forest limits in South and East Asian mountains.Journal of Ecology, 78(2): 326-339.A discussion of forest limits (treelines) in S. and E. Asia in relation to floristics (mainly evergreen conifers with some deciduous broadleaves in northern temperate areas, but evergreen broadleaves on tropical mountains), temperature conditions (seasonality, minimum temperature affecting winter survival, minimum temperature sums affecting summer growth and reproduction), and altitude and lati...

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Shi Yafeng, Zheng Benxing, Li Shijie, 1992. Last Glaciation and Maximum Glaciation in the Qinghai-Xizang (Tibet) Plateau: A controversy to M. Kuhle's ice sheet hypothesis.Chinese Geographical Science, 2(4): 293-311.<p>Since the late 1950's, many Chinese scientists have explored the remains of the Quaternary glaciation in the Qinghai-Xizang (Tibet) Plateau and its surrounding mountains. In the main, 3-4 glaciations have been recognized. The largest one occurred in the Late Middle Pleistocene with piedmont glaciers, ice caps and trellis valley glaciers in many high peak regions. But here is no evidence of a unified ice sheet covering the whole plateau as described by M. Kuhle. Due to the further uplifting of the Himalayas and Qinghai-Xizang Plateau the climate became progressively drier, diminishing the extension of glaciers during the Late Pleistocene. The elevation of the snow line during the Last Glaciation was about 4,000 m on the south, east and northeast edges of the plateau and ascended to 5500 m on the hinder northwest of the plateau. The thermal effect of the big plateau massif, the sharp increase of aridity from the southeast rim to the northwest inland area and the abrupt decrease of precipitation during the Ice Age largely account for the distribution of the Quaternary glaciers in the Qinghai-Xizang Plateau. The neglect of Chinese literature may be one of the causes accounting for M. Kuhle's misinterpretation on the environment of the Quaternary glaciations in the Qinghai-Xizang Plateau.</p>

20
Sun Ranhao, Zhang Baiping, 2008. Exploring the method of digital identification of mountain altitudinal belts.Geo-information Science, 10(6): 690-696. (in Chinese)Geo-info Tupu methodology is a new direction for geographic researches,and it's not only a way for presentation but also a method of analysis in geo-information science.Based on the idea of Geo-info Tupu and digital mountain altitudinal belts(digital MABs),the objective of this paper is to construct the models of multi-scales spatial patterns in altitudinal belts and develop the digital framework that identifies the horizontal and vertical differentiation from multi-source data.According to three patterns of digital identification,i.e.,single flank,single peak and multiple peaks,this paper develops different algorithms using the Matlab programme.Contrasting the identification patterns,we indicate the relative merits among them in digital identification and Tupu visualization,and show clearly their special scale for the study regions.Based on the VB.NET platform,we build a programming system,named &quot;Mountain Altitudinal Belts Digital Identification System&quot;(MABsDIS),which provides several modules with useful tool sets,such as data in-out module,data processing module and graphic plotting module.Using the MABsDIS software,the mountain altitudinal belts are identified from digital terrain models and digital vegetation data in the Helan Mountains,China.The study reveals that the digital identification of MABs could enrich the source materials of mountain researches and enhance the digital comparison and analysis method of MABs.And it also could reveal more geographical information than ever before and is an effective attempt for Geo-info Tupu practices.

21
Sun Ranhao, Zhang Baiping, Tan Jing, 2008. A multivariate regression model for predicting precipitation in the Daqing Mountains.Mountain Research and Development, 28(3): 318-325.Multivariate regression analysis, combined with residuals correction, was carried out to develop a precipitation prediction model for the Daqing Mountains of Inner Mongolia in northern China. Precipitation data collected at 56 stations between 1955 and 1990 were used: data from 48 stations for model development and data from 8 stations for additional tests. Five topographic factors-altitude, slope, aspect, longitude, and latitude-were taken into account for model development. These topographic variables were acquired from a 100m resolution digital elevation model (DEM) of the study region, and the mean values of the sub-basin in which a precipitation station is located were used as the values of the respective variables of that station. The multivariate regression model can explain 72.6% of the spatial variability of precipitation over the whole year and 74.4% of variability in the wet season (June-September). Precipitation in the dry season (October-May) is hard to model owing to little rainfall (21.78% of annual rainfall) and a different synoptic system. Interpolation-based residuals correction did not significantly improve the accuracy of our model, which shows that our model is quite effective. The model, as presented in this paper, could potentially be applied to other mountains and in mountain climate research.

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Tollner H, 1949. Der Einfluß großer Massenerhebungen auf die Lufttemperatur und die Ursachen der Hebung der Vegetationsgrenzen in den inneren Ostalpen.Theoretical and Applied Climatology, 1(3): 347-372.

23
Troll C, 1973. The upper timberlines in different climatic zones.Arctic and Alpine Research, 5(3): 3-18.In the past the upper timberlines and their ecological causality were mostly studied in high mountains of the humid temperate zones of the Northern Hemisphere with their strong thermal contrasts of summer and winter. They are generally determined by the duration of certain summer temperature values and in their topoclimatic differentiation controlled by the accumulation and deflation of snow. But they are not climatically equivalent, not even in a relatively small mountain system such as the Alps or the Tatra mountains from what is shown by the change in the limit-forming trees (spruce, larch, pine, fir, beech, birch, etc.). The upper timberlines in the humid tropics are completely different in physiognomy, life forms, climatic conditions, and topoclimatic effects; they are without seasonal variations of temperatures. In most cases they are formed by a dense evergreen forest with dozens of broad-leaved trees, sometimes by a fringing woodland (Polylepis, Ericacea, Hagenia, Leptospermum). In the arid belts, which extend from tropical to cold temperate latitudes, where the forest belts have a lower and an upper limit ("girdle forests"), also the upper timberline can be caused, at least in part, by aridity factors. In the Mediterranean area and in the Middle East we distinguish a sub-Mediterranean subzone with upper timber belts of boreal types, a fully Mediterranean subzone with specific Mediterranean trees as uppermost timber belt (Quercus ilex, Q. tozza, Pinus leucodermis, Cedrus atlantica), and a southerly Mediterranean steppe belt where generally juniper species are upper limiting trees. The timberlines in the cool temperature zone of the Southern Hemisphere with its high oceanity show more affinity to the tropical highlands than to the boreal zones.

24
Wang Chuanhui, Zhou Shunwu, Tang Xiaopinget al., 2011. Temporal and spatial distribution of heavy precipitation over Tibetan Plateau in recent 48 years.Scientia Geographica Sinica, 31(4): 470-477. (in Chinese)Based on the daily precipitation data of 48 stations over the Tibetan Plateau from 1961 to 2008,the characteristics of spatial and temporal distribution during the summer and winter half years over the Plateau are analyzed.It is shown that the spatial distribution of strong precipitation is very similar to that of the total precipitation over the Plateau,which decreases from southeast to northwest during the summer half year,while declines from the hinterland of the Plateau located in the east of Tanggula Mountains to the surrounding during the winter half year.The heavy precipitation during the summer half year has inter-annual oscillation cycle with quasi-three years,quasi 6 years and decadal oscillation cycle with quasi 10-11 years,while has the cycle with 6-7 years and decadal cycle with quasi 15 years during the winter half year.The trend of heavy rainfall is quite different in spatial distribution.During the summer half year,it increases(decreases) in most regions over the northern(southern) Plateau.The overall heavy precipitation of the Tibetan Plateau shows a weak decreasing trend during the summer half year and an increasing trend in the Yarlung Zangbo River during the winter half year.There was an abrupt change of precipitation in 1976.

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Wu Guoxiong, Liu Yiming, Liu Xinet al., 2005. How the heating over the Tibetan Plateau affects the Asian climate in summer.Chinese Journal of Atmospheric Sciences, 29(1): 47-57. (in Chinese)

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Wu Zhangwen, 1996. Local climate measurement of Qingcheng Shan.Journal of Sichuan Forestry Science and Technology, 17(1): 74-76. (in Chinese)

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Yao Yonghui, Zhang Baiping, 2013a. A preliminary study of the heating effect of the Tibetan Plateau.PLOS One. doi: 10.1371/journal.pone.0068750.The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect of the plateau and its implications. This paper firstly collects climate data (2001–2007) from 109 observation stations and MODIS-based estimated monthly mean temperature data in the plateau and the neighboring Sichuan Basin, and conducts correlation and simple linear regression to reveal the altitudinal pattern of temperature. Then, according to the linear relationships of temperature and altitude for each month, it compares air temperature differences on the same elevation between the main plateau and surrounding mountains and the Sichuan Basin so as to quantify the heating effect and discuss its implication on timberline of the plateau. The results show that: 1) the heating effect of the plateau is significant. The temperature of the main plateau area was higher than that of free air on the same elevation above the neighboring areas; on the elevation of 4500 m (the main plateau), temperature is 1–6°C higher in the main Plateau than over the Sichuan Basin for different months and 5.9–10.7°C higher than in the Qilian Mountains in the northeastern corner of the plateau. 2) Even at altitudes of 5000–6000 m in the main Plateau, there are 4 months with a mean temperature above 0°C. The mean temperature of the warmest month (July) can reach 10°C at about 4600–4700 m. This may help explain why the highest timberline in the northern hemisphere is on the southeastern Tibetan Plateau.

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Yao Yonghui, Zhang Baiping, 2013b. MODIS-based estimation of air temperature of the Tibetan Plateau.Journal of Geographical Sciences, 23(4): 627-640.The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression (OLS) and geographical weighted regression (GWR) of monthly mean air temperature (Ta) with monthly mean land surface temperature (Ts) were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R (2) > 0.91 and RMSE = 1.13-1.53A degrees C than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2A degrees C. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July (the warmest month) at altitudes of 4500 m can reach 10A degrees C. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.

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Yao Yonghui, Zhang Baiping, 2013c. MODIS-based estimation of air temperature and heating effect of the Tibetan Plateau.Acta Geographica Sinica, 68(1): 93-104. (in Chinese)Time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, were used to estimate and map the spatial distribution of monthly mean air temperatures of the Tibetan Plateau and neighboring areas. Time series and regression analyses of monthly mean land surface temperature (Ts) and monthly mean air temperature (Ta) were conducted using both ordinary linear regression (OLS) and geographical weighted regression (GWR) methods. Analysis shows that recorded Ta is rather closely related to Ts, and that the GWR method has a much better result (adjusted R 2 0.91, root mean square error (RMSE) = 1.16-1.58 ℃) for estimating Ta than OLS. The GWR model, with MODIS Ts and altitude as independent variables, was thus used to estimate Ta for the Tibetan Plateau. For more than 80% of the stations, the Ta retrieved from Ts had residuals lower than 2 ℃. Analysis of the spatial pattern of retrieved Ta data showed that the mean Ta of the summer half year was higher than 0℃ even at high altitudes of 5000±600 m of the plateau, especially in the warmest month (July) the Ta in high mountain areas with altitudes of 4000-5500 m could reach as high as 10 ℃. This may help explain why the highest timber line in the northern hemisphere is located on the Tibetan Plateau. According to our results, Ta in July was probably 6-10 ℃ warmer in the inner plateau than in the outer plateau at any given elevation which resulted from the heating up effect of the Plateau.

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Yao Yonghui, Zhang Baiping, 2014. The mass elevation effect of the Tibetan Plateau and its implications for Alpine treelines.International Journal of Climatology. doi: 10.1002/joc.4123.ABSTRACT Top of page ABSTRACT 1Introduction 2Study area 3Data and data sources 4Methods 5Results 6Discussion 7Conclusions Acknowledgements Appendix References Supporting Information The immense and towering Tibetan Plateau (TP) acts as a heating source and shapes the climate of not only the Eurasian continent but also the entire world. The mass elevation effect of the TP was first observed in the 1950s; however, due to the scarcity of meteorological observation stations and limited climatic data, little information on the mass elevation effect of the plateau and its implications for the position of Alpine treelines in the southeastern part of the TP is quantitatively known. This paper compares monthly mean air temperature differences at elevations of 4000, 4500, 5000, 5500 and 600065m between the main plateau, the Qilian Mts. in the northeastern corner of the plateau and the Sichuan Basin to the east of the plateau to quantify the mass elevation effect of the plateau. The TP air temperature data are retrieved from Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and the free-air temperatures over the westernmost Sichuan Basin are estimated using the measured lapse rate from Mt. Emei, which is located in the western portion of the Sichuan Basin. The results demonstrate the following important characteristics. (1) Owing to the mass elevation effect, air temperatures gradually increase from the eastern edge to the interior main TP. The monthly mean air temperature in the interior main plateau is approximately 2–765°C higher than in the surrounding mountains and adjacent lowland areas. At an elevation of 450065m (corresponding to the mean altitude of the TP), the monthly mean temperature differences between the plateau and the Sichuan Basin range from 3.5865°C (April) to 6.6365°C (June); the monthly temperature differences between the plateau and the Qilian Mts. range from 1.665°C (July) to 7.765°C (March). (2) The mass elevation effect of the plateau pushes the 1065°C isotherm upward in the warmest month and is indicative of a warmth index of 1565°C65month up to elevations of 4600–470065m, which enables the treeline altitude in the interior TP 500–100065m higher than along the eastern edge. Therefore, mass elevation effect contributes to the occurrence of the highest treeline in the Northern Hemisphere, which is present on the southeastern TP.

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Ye Duzheng, 1982. Some aspects of the thermal influences of Qinghai-Tibetan Plateau on the atmospheric circulation.Archives for Meteorology, Geophysics, and Bioclimatology, 31(3): 205-225.Die Meteorologie des Qinghai-Tibet-Plateaus erfuhr in jüngsten Jahren bedeutende Beachtung und Fortschritte. Bemerkenswert ist die Errichtung eines Beobachtungsnetzes in dieser Region. Aus diesen Beobachtungen entsprangen neue, signifikante Erkenntnisse, die teilweise in dem Buch 67Meteorologie des Qinghai-Tibet-Plateau’ [13] erw01hnt sind. Die neuen Forschungsresultate weisen auf die dynamische und thermische Bedeutung des Plateaus in der Erzeugung atmosph01rischer Zirkulationssysteme hin. Die dynamischen Effekte des Plateaus sind schon l01nger bekannt [3, 2, 5]. Auf die thermischen Effekte wurde ebenfalls schon in früherer Literatur hingewiesen [20, 7], doch wurden diese Effekte neuerlich intensiv untersucht. In der vorliegenden Arbeit wird der thermische Einflu08 des Plateaus diskutiert, beginnend mit einer Absch01tzung des Jahresganges der Intensit01t der W01rmequelle über dem Plateau und des Einflusses dieser Quelle auf die atmosph01rische Zirkulation. Sodann wird die Rolle der Konvektion in der Aufrechterhaltung der gro08r01umigen Sommerzirkulation über dem Plateau beschrieben. Schlie08lich werden Vergleiche zwischen den Effekten des Plateaus und der tropischen Ozeane auf die Sommerzirkulation angestellt.

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Ye Duzheng, Luo Siwei, Zhu Baozhen, 1957. The flow pattern and heat budget in the troposphere over the Tibetan Plateau and surrounding area.Acta Meteorologica Sinica, 28(2): 108-121. (in Chinese)

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Zhang Baiping, 2008. Progress in the study on digital mountain altitudinal belts.Journal of Mountain Science, 26(1): 12-14. (in Chinese)Vertical environmental gradient is 1 000 times as high as that in the horizontal direction.Altitudinal zonation is actually of the uttermost importance in the study of mountain environment.In recent years,we have successfully developed a data model for mountain altitudinal belts,which can be used to integrate all altitudinal belts in China and in the whole world.We have generalized 31 physico-geographical zones and 32 altitudinal belts for China,which acts as the geographical basis to digitally integrate China's altitudinal belts.A mountain altitudinal belt information system has been developed and upgraded.A quadratic model for altitudinal belt distribution on the continental scale has been proposed,which,of course,needs to be improved with more data.Our research in the coming years will be concentrated on improving the accuracy in identifying altitudinal belts and on extending the work to the whole Eurasian continent or even the world.

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Zhang Baiping, Chen Xiaodong, Li Baolinet al., 2002. Biodiversity and conservation in the Tibetan Plateau.Journal of Geographical Sciences, 12(2): 135-143.<a name="Abs1"></a>The Tibetan Plateau (Qinghai-Xizang Plateau) is a unique biogeographic region in the world, where various landscapes, altitudinal belts, alpine ecosystems, and endangered and endemic species have been developed. A total of 26 altitudinal belts, 28 spectra of altitudinal belts, 12,000 species of vascular plant, 5,000 species of epiphytes, 210 species of mammals, and 532 species of birds have been recorded. The plateau is also one of the centers of species formation and differentiation in the world To protect the biodiversity of the plateau, about 80 nature reserves have been designated, of which 45 are national or provincial, covering about 22% of the plateau area. Most of the nature reserves are distributed in the southeastern plateau. Recently, the Chinese government has initiated the &#8220;Natural Forests Protection Project of China,&#8221; mainly in the upper reaches of the Yangtze and Yellow rivers. &#8220;No logging&#8221; policies have been made and implemented for these areas.

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Zhang Baiping, Tan Jing, Yao Yonghui, 2009. Digital Information and Patterns of Mountain Altitudinal Belts. Beijing: China Environmental Sciences Press. (in Chinese)

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Zhao Fang, Zhang Baiping, Tan Jinget al., 2011. Structure and function of the digital integrated system for the Eurasian mountain altitudinal belt.Journal of Geo-information Science, 13(3): 346-355. (in Chinese)In this paper we developed a digital integrated system for the Eurasian mountain altitudinal belts based on the digital integrated framework of mountain altitudinal belts and 880 Mountain Altitudinal Belt Spectra data of Eurasian Continent.The main functions of this system are: 1) dynamic visualization function of mountain altitudinal belts,including real time generation of geographic distribution map,stacked bar chart and curve of upper and lower height limits change with latitude and longitude;2) extraction of upper and lower limits of mountain altitudinal belts and transformation between absolute height and relative height;3) query and analysis of mountain altitudinal belts,including query of mountain altitudinal belts based on geographic coordinate,temperature zone and vegetation zone and analysis of the query results by mapping;and 4) extraction of altitudinal boundary,including curve plotting of single lines(such as timberline,snow line and frozen line,etc.) with changes of geography,climate and the topography,export of single line data.The open feature of this system makes it convenient to add data and to improve the functions and to upgrade,and consequently makes it a very unique geographic information system.This system provides a new platform for analyzing the geographic and ecological features of the mountain altitudinal belts on continental and global scale and lays the groundwork for revealing the vertical distribution and three-dimensional distribution of the Eurasian mountain environment.

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Zhao Y, Li H J, Huang A Net al., 2013. Relationship between thermal anomalies in Tibetan Plateau and summer dust storm frequency over Tarim Basin, China.Journal of Arid Land, 5(1): 25-31.The dust storm is the most important and frequent meteorological disaster over Tarim Basin, which causes huge damages on local social economics. How to predict the springtime and summertime dust storm occurrence has become a hot issue for meteorologists. This paper employed the data of dust storm frequency and 10-m wind velocity at 35 stations over Tarim Basin and the reanalysis data from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) during 1961-2007 to study the relationship between dust storm frequency (DSF) in summer over Tarim Basin and the thermal anomalies in Tibetan Plateau in May by using the statistical methods, such as Empirical Orthogonal Function (EOF), correlation and binomial moving average. The results show when negative anomalies in Tibetan Plateau and positive anomalies in its southern region are present along 30 degrees N (the second mode of surface temperature anomalies by EOF decomposition) in May, the time coefficient (PC2) plays an important role in summer DSF variation and has a close relation with the summer DSF at both inter-annual and decadal time scales. When negative anomalies in Tibetan Plateau and positive anomalies are present in its southern region (PC2 in positive phase), there is an anomalous anticyclone in North China, which weakens the northwest wind and is not beneficial for cold air moving from high latitude to the Tarim Basin, and the circulation pattern is hard to result in dust storm weather. Furthermore, the sea level pressure (SLP) increased over Tarim Basin and the direction of SLP gradient reversed, which resulted in the 10-m wind velocity slowing down, so the DSF decreased. From above all, it can be conclude that the thermal anomalies in Tibetan Plateau in May has important effects on the summertime dust storm frequency over Tarim Basin and the PC2 can be used as a prediction factor for the summertime dust storm occurrence.

DOI

38
Zheng Du, Li Bingyuan, 1990. Evolution and differentiation of the natural environment of the Qinghai-Tibet Plateau.Geographical Research, 9(2): 1-10. (in Chinese)

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Zheng Yuanchang, Gao Shenghuai, Chai Zongxin, 1986. A preliminary study on the vertical natural zones in the Hengduan Mountainous region.Mountain Research, 4(1): 75-83. (in Chinese)Hengduan mountainous region is situated in the east and southeast of Qinghai-Xizang Plateau. In this region, the geomorphology is complex, with great difference of height, and the ranges and valleys, arranging in parallel, extending from north to south; the climatic types are various, the territorial differences of the water-heat conditions and vertical variation are quite distinct.The vertical natural zones, with a variety of types, may be divided into 9 types according to the characters of the spectra. The structures of spectra are complex. There are at most 7 zones from the valleys to the tops of the mountains.The characters of the types and spectrum structures oft he vertical zones have clear variations with latitude, longitude and slope.The dry-hot (dry-warm ) valley is a particulary type. It is of an importance in the vertical natural zones of this region.

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