Theor Appl Climatol DOI 10.1007/s00704-017-2153-2
ORIGINAL PAPER
Near-surface air temperature lapse rate in a humid mountainous terrain on the southern slopes of the eastern Himalayas Dambaru Ballab Kattel 1,2 & Tandong Yao 1,3 & Prajjwal Kumar Panday 4
Received: 21 June 2016 / Accepted: 2 May 2017 # Springer-Verlag Wien 2017
Abstract Based on climatic data from 18 stations on the southern slopes of the eastern Himalayas in Bhutan for the period from 1996 to 2009, this paper investigates monthly characteristics of the near-surface air temperature lapse rate (TLR). The station elevations used in this study range from 300 to 2760 m a. s. l. TLRs were evaluated using a linear regression model. The monthly values of maximum TLRs were always smaller than those of the minimum TLRs, which is in contrast to results from the surrounding mountainous regions. In this study, annual patterns of TLRs were somewhat consistent, particularly in the summer; during the other seasons, patterns contrasted to results from the southeastern Tibetan Plateau (China) and were almost comparable to results from Nepal. The shallowest observed values for TLRs in summer are due to intense latent heating at the higher elevation, associated with water vapor condensation from moist convection and
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evapotranspiration, and decreasing sensible heating at lower elevation, due to heavier rainfall, cloud, and forest cover. When compared to summer, the steeper TLRs in the non-monsoon season are due to sensible heating at the lower elevations, corresponding to dry and clear weather seasons, as well as increasing cooling at higher elevations, particularly in winter due to snow and cloud cover. Owing to lower albedo and higher aerodynamic roughness of forested areas, the TLRs were considerably reduced in daytime because of the dissipation of sensible heat to the atmospheric boundary layer. The distinct variation in diurnal TLR range is due to the diurnal variation in net radiation associated with reduced turbulent heating in the day and increased turbulent heating in the night, in addition to the effect of moisture and cloud cover. The shallower values of TLRs in this study when compared with the surrounding mountainous regions are due to high humidity, as well as the differing elevations and local climates. Keywords Temperature lapse rate . Monthly variation . Controlling factors . Southern slopes . Eastern Himalayas
* Dambaru Ballab Kattel
[email protected];
[email protected]
1 Introduction 1
Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, No. 16, Lincui Road, Chaoyang District, Beijing 100101, China
2
Department of Meteorology, COMSATS Institute of Information Technology, CIIT, Islamabad, Pakistan
3
CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
4
Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540, USA
Prediction of near-surface temperature lapse rate (hereafter referred to as TLR) in mountainous terrain might be the simplest technique to better understand the physical process of the atmospheric thermodynamic system, in particular adiabatic (dry and moist) processes near the surface (for example, see Kattel et al. 2013, 2015). In addition, TLR might be an indicator in determining the onset and offset of the monsoon system (Komatsu et al. 2010) and can be important in explaining the cloudiness-surface
D. B. Kattel et al.
temperature-feedback (Schneider and Dickinson 1974) in a complex mountainous terrain. Numerous studies have highlighted the significance of TLRs to quantify the temperature range in mountainous regions for accurately modeling stream flow, ecosystems distributions, and glacier mass variation (Diaz and Bradley 1997; deScally 1997; Rolland 2003; Harlow et al. 2004; Blandford et al. 2008; Minder et al. 2010; Kattel et al. 2013, 2015). Generally, TLR changes with space and time. The magnitudes of TLR and their controls may also differ with locations and time as a function of local energy balance regime (Lookingbill and Urban 2003; Marshall et al. 2007; Gouvas et al. 2011; Kattel et al. 2013, 2015), slope and aspect effect on solar insolation (Stone and Carlson 1979; Pepin 2001; Barry and Chorley 2003; Tang and Fang 2006; Kattel et al. 2013, 2015), synoptic conditions, latitude, humidity, wind speed, forest coverage, and distance from the sea (Pepin 2001; Blandford et al. 2008; Kattel et al. 2013, 2015). Numerous studies have reported on seasonal trends of TLR, i.e., steepest in summer (warmer months) and shallowest in winter (cooler months) (e.g., Diaz and Bradley 1997; Pepin 2001; Pepin and Losleben 2002, Rolland 2003; Blandford et al. 2008). A recent study of Kattel et al. (2015) from the southeastern Tibetan Plateau (China) on the northern slopes of the eastern Himalayas reported distinct seasonal trends of TLR, i.e., steepest in winter (colder months) and shallowest in summer (warmer months). Kattel et al. (2013) has reported contrasting results from these findings (i.e., a bimodal pattern of TLR; two higher values in pre- and postmonsoon season and two lower values in winter and summer, respectively) from the southern slopes of the central Himalayas, Nepal. This investigation is the extension of recent research carried out by the authors in the surrounding mountainous region along the Himalayas and the Tibetan Plateau in order to better understand the monthly behaviors of TLR in the same seasonal periods, but under different local climatic regimes. Using observed data series from 70 stations on the southern slopes of the eastern Himalayas, Bhutan, Dorji et al. (2016) calculated and briefly discussed the monthly values of maximum, minimum, and mean TLRs. Kattel (2012) also briefly discussed the causes of monthly variation of TLRs on the southern slopes of the eastern Himalayas in his Ph.D dissertation; however, detailed and systematic studies of monthly characteristics of TLRs and associated forcing factors for the variation are still rare. Thus, the major objective of this paper is to investigate the monthly characteristics of TLRs and forcing mechanisms for their variability in humid and forestcovered mountainous terrain. The results from this study are also compared with recent results from the surrounding mountainous regions.
2 Data and methodology 2.1 Site descriptions and climatology Bhutan is a mountainous country in the Himalayas, located in the northeastern part of the south Asian continent. It represents a unique climatic diversity, adjoining two climatically contrasting areas: Assam in the south, noted for its heavy precipitation, and the Tibetan Plateau on the northern part across the Himalayas, mainly famous for its dry highlands (Eguchi 2008). The country is located between the latitudes of 26° 47′ N and 28° 26′ N, and longitudes of 88° 52′ E and 92° 03′ E (Baillie and Norbu 2004), and covers an area of about 38,394 km2 (NSB 2011). The east-west and south-north dimension of the country is approximately 300 and 170 km, respectively. The country is characterized topographically by a series of altitudinal zones that are aligned roughly east-west directionally (Baillie and Norbu 2004). The elevation distribution in the country ranges from 100 m above sea level in south to more than 7000 m in the north (Tshering and Sithey 2008). Over two-thirds of the country’s territory is above 2000 m, and a quarter is above 4000 m a.s.l. Most of the country (70.5%) is covered by forest. Alluvial lowland river valleys are found in the foothills of the Himalayas in the south, and the majority of areas in this region (below 1500 m a. s. l.) are covered by the dense deciduous forest. The overall climate of Bhutan is humid. Two main factors control variations in climatic conditions and average temperature: the vast differences in altitude and the influence of the Indian monsoon (Banerjee and Bandopadhyay 2016). Most rainfall occurs in the south and on south-facing slopes during summer and in winter on north and on north-facing slopes (Dorji et al. 2016). The southern plains and foothills of the Himalayas in Bhutan mainly experience a subtropical climate and are generally hot and humid throughout the year (NSB 2011; Banerjee and Bandopadhyay 2016). The climate in the northern portion is cold, and year-round snow, particularly on the main Himalayan summits, can be observed. In the central areas of the country, the climate is cooler than in the south, changing to deciduous and temperate forests with warm summers and cool, dry winters (Banerjee and Bandopadhyay 2016). The Indian summer monsoon remains from late June to late September and is mostly limited to the low elevations, the southern plains, foothills, and border areas of Bhutan, resulting in heavy rain and high humidity in the region (Banerjee and Bandopadhyay 2016; Dorji et al. 2016). The country receives extremely high rainfall in this season, which is associated with the frequent orographic uplift of monsoonal moist air masses due to the presence of mountains (Baillie and Norbu 2004; Kattel et al. 2013, 2015). The annual precipitation in Bhutan reaches >4000 mm along the southern foothills (Eguchi 2008). In contrast, in the inter-montane basin, about 70–80 km north of the southern
Temperature lapse rate in a humid mountainous terrain
foothills, annual precipitation is between 600 and 800 mm. Precipitation decreases sharply northward, and many stations record annual means of less than 1000 mm (Baillie et al. 2004). The western part of the country generally receives the highest rainfall, accounting for 60 to 90% of the region’s total rainfall (Banerjee and Bandopadhyay 2016). Because the western part of Bhutan is adjacent to Cherapunji, i.e., located close to the plateau’s southern edges at about 1300 m a.s.l., and has a mean annual rainfall of over 11 m, and an absolute annual maximum of over 25 m, the region is one of the wettest places on earth. Thus, in general, eastern, western, and central Bhutan remains drier throughout the year than the southern and northern regions (Dorji et al. 2016). The annual climate of Bhutan has been generally classified into four distinct seasons. Post-monsoon season (from late September or early October to late November) in Bhutan is characterized by bright sunny days and some early snowfalls at higher elevations (NSB 2011). From late November until March, winter sets in, with frost throughout much of the country and snowfall common above elevations of 3000 m a.s.l. Winter season is mostly dry and bright, due to outflows from the Tibetan high-pressure system (Baillie and Norbu 2004). It is noted that the eastern Himalayas are little affected by the westerlies that bring winter rain to the western Himalayas (Mani 1981). However, the northeast monsoon brings gale force winds down through high mountain passes during this season (NSB 2011). 2.2 Data Located on the southern slopes of the eastern Himalayas (SSEH), Bhutan’s 14-year averages (1996–2009) of monthly maximum, minimum, and mean temperature data were extracted from the Bhutan Statistical Yearbook 2010, published by The National Statistics Bureau of Bhutan (see website: http://www.nsb.gov.bt). Homogenization attempt was not included in the study due to the non-availability of the yearly values of near-surface air temperature data. However, a simple error analysis was conducted for accuracy measurement of estimated TLR values. The seasonal mean values of this study were compared with long-term average values published by Dorji et al. (2016), and it was identified that the results strongly agree, except for three stations (Punakha, Semtokha, and Trongsa). The three stations showed a difference of about ≤1. 5 °C between the two data sets, perhaps due to different time periods used for averaging. We have calculated the TLR values step-by-step, both including and excluding the values of the three stations; it was identified that the results are almost same (error = 0.001 °C/100 m). Again, TLRs were calculated using the long-term seasonal averages of air temperature values published by Dorji et al. (2016) and were compared with our observation. Results also depict negligible variation from the observed lapse rate values. Therefore, the extracted
data sets used in this study are accurate enough for temperature lapse rate computation, seasonal analysis, and comparative analysis. Detailed information on stations and data used in this study are presented in Table 1. Due to the inaccessibility of other Bhutanese climatic data, this work does not explain the TLR relationship with rainfall and relative humidity based on in situ observation. But the large-scale gridded Climate Research Unit cloud cover (CRU TS 3.10-land) and precipitation (CRU TS 3.10.01) data for the same period (1996– 2009) were used to fulfill the gap, and for comparison and analysis. Cloud cover and rainfall values of the nearest 17 grid points from the observed 18 stations in Bhutan were retrieved from the website http://climexp.knmi.nl/. Both data sets are distributed in 0.5° × 0.5° square grids. The spatial distribution of stations and CRU grid points are presented in Fig. 1. In this study, we also used evapotranspiration (ETo) data for analysis and comparison. ETo data for the period from 2002 to 2014 for each station were extracted from the MOD16 Global Terrestrial Evaporation Data Set (Mu et al. 2011). The ETo includes evaporation from wet and moist soil, from rain water intercepted by the canopy before it reaches the ground, and the transpiration through the stomata on plant leaves and stems.
2.3 Methods Near-surface air temperature decreases linearly with elevation. In this study, maximum, minimum, and mean temperature lapse rates were calculated by fitting the linear regression model (e.g. Kattel et al. 2013, 2015), per Eq. (1). T ¼ C 0 −LR E þ e
ð1Þ
In Eq. (1), T (observed temperature) is the dependent variable, E (elevation in km) is the independent variable, and C0 and e are the constant temperature in °C at zero elevation and error, respectively. −LR is the coefficient (in °C/km) of the regression, representing the temperature lapse rate in the study area. The distinction between wet and dry conditions is extremely important in controlling lapse rates near the surface (Kattel et al. 2013). To investigate the moisture presence in the atmosphere, the saturation vapor pressure in the study area was computed, using the following Eq. (2). aT
es ¼ 6:1078 10bþT
ð2Þ
Equation (2) is the well-known formula of Magnus (1844), which is used by Tetens (1930) with a different coefficient, based on the Calusius-Claperon relationship. The values of coefficients a = 7.5 and b = 237.3 were used when T > 273.15 K, while T ≤ 273.15 K, the values of coefficients of a = 9.5 and b = 265.3 were used. The lapse rate of saturation
D. B. Kattel et al. Table 1 Information of stations used in this study
No.
Station name
Longitude (°E)
Latitude (°N)
Elevation (m.a.s.l)
Mean temperature (°C)
1
Chamkhar
90.78
27.54
2470
11.68
2
Dagana
89.88
27.07
1460
15.58
3 4
Gasakhatey Ha Namjayling
89.72 89.28
27.90 27.39
2760 2720
11.24 10.67
5
Monggar
91.24
27.28
1600
17.98
6
Punakha
89.87
27.58
1236
18.54
7 8
Paro Pemagatshel
89.42 91.42
27.38 27.03
2406 1618
14.62 17.22
9 10
Deothang Sipsoo
91.47 88.87
26.86 27.02
300 550
20.78 23.18
11
Bhur
90.43
26.90
375
23.66
12 13
Semtokha Kanglung
89.68 91.52
27.44 27.28
2310 1930
13.88 15.50
14 15
Tashi Yangtse Trongsa
91.50 90.51
27.60 27.50
1830 2120
15.27 15.23
16 17 18
Damphu Wangduephodrang Zhemgang
90.12 89.90 90.66
27.00 27.49 27.21
1520 1180 1905
16.52 19.38 15.93
vapor pressure and evapotranspiration was also computed, per Eq. (1) for analysis and comparison.
3 Results and discussion 3.1 Linear relationship, residual errors, and temperature lapse rate 3.1.1 Temperature-elevation relationship Multicolinearity analysis was performed to quantify the impact of various simultaneous influences upon a single dependent variable (see Kattel et al. 2013, 2015). Here, we considered temperature as the dependent variable and latitude and longitude, and elevation as independent variables. In order to measure the strength of collinearity, the variance inflation factors (VIF) were evaluated. Generally, the VIF > 10 suggests the collinearity problem (Brien and Robert 2007). Although the values of VIFs in this study were found to be low for all independent variables (lat = 2.00, lon = 1.03, and elev = 1.99), the relationships between temperature and latitude and longitude were not statistically significant (p > 0.05) and also had a higher standard error of coefficients (>1.5 °C for the latitudes and >0.40 °C for the longitudes). A direct and statistically significant (p < 0.0001) inverse relationship with lower errors (70%) and rainfall (>350 mm) occur in summer months, while lower cloud cover (
