Sciences in Cold and Arid Regions  2017, 9 (2): 97-111   PDF    

Article Information

YuLan Zhang, ShiChang Kang, Min Xu, Michael Sprenger, TanGuang Gao, ZhiYuan Cong, ChaoLiu Li, JunMing Guo, ZhiQiang Xu, Yang Li, Gang Li, XiaoFei Li, YaJun Liu, HaiDong Han . 2017.
Light-absorbing impurities on Keqikaer Glacier in western Tien Shan: concentrations and potential impact on albedo reduction
Sciences in Cold and Arid Regions, 9(2): 97-111

Article History

Received: November 17, 2016
Accepted: February 14, 2017
Light-absorbing impurities on Keqikaer Glacier in western Tien Shan: concentrations and potential impact on albedo reduction
YuLan Zhang1, ShiChang Kang1,2, Min Xu1, Michael Sprenger3, TanGuang Gao4, ZhiYuan Cong2,5, ChaoLiu Li2,5, JunMing Guo5, ZhiQiang Xu6, Yang Li5, Gang Li7, XiaoFei Li1, YaJun Liu1, HaiDong Han1     
1. State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Science, Lanzhou, Gansu 73000, China;
2. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China;
3. Institute for Atmospheric and Climate Science, ETH Zurich, CH-8092 Zurich, Switzerland;
4. Key Laboratory of Western China's Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China;
5. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, CAS, Beijing 100101, China;
6. Meteorological Bureau of Jimunai County, Jimunai, Xinjiang 836800, China;
7. Arid Meteorological Research Institute, Lanzhou Meteorological Bureau, Lanzhou, Gansu 730000, China
Abstract: Light-absorbing impurities on glaciers are important factors that influence glacial surface albedo and accelerate glacier melt. In this study, the quantity of light-absorbing impurities on Keqikaer Glacier in western Tien Shan, Central Asia, was measured. We found that the average concentrations of black carbon was 2,180 ng/g, with a range from 250 ng/g to more than 10,000 ng/g. The average concentrations of organic carbon and mineral dust were 1,738 ng/g and 194 μg/g, respectively. Based on simulations performed with the Snow Ice Aerosol Radiative model simulations, black carbon and dust are responsible for approximately 64% and 9%, respectively, of the albedo reduction, and are associated with instantaneous radiative forcing of 323.18 W/m2 (ranging from 142.16 to 619.25 W/m2) and 24.05 W/m2 (ranging from 0.15 to 69.77 W/m2), respectively. For different scenarios, the albedo and radiative forcing effect of black carbon is considerably greater than that of dust. The estimated radiative forcing at Keqikaer Glacier is higher than most similar values estimated by previous studies on the Tibetan Plateau, perhaps as a result of black carbon enrichment by melt scavenging. Light-absorbing impurities deposited on Keqikaer Glacier appear to mainly originate from central Asia, Siberia, western China (including the Taklimakan Desert) and parts of South Asia in summer, and from the Middle East and Central Asia in winter. A footprint analysis indicates that a large fraction (>60%) of the black carbon contributions on Keqikaer Glacier comes from anthropogenic sources. These results provide a scientific basis for regional mitigation efforts to reduce black carbon.
Key words: light-absorbing impurities     black carbon     mineral dust     glacier     snow albedo     Tien Shan    
1 Introduction

Light-absorbing aerosols have a significant direct and indirect role in changing the earth's climate (Ramanathan et al., 2005; Ramanathan and Carmichael, 2008; IPCC, 2013). In addition to air temperature changes, light-absorbing impurities (LAIs) such as black carbon (BC), organic carbon (OC; in this study, only water-insoluble OC was considered), and mineral dust can reduce the surface albedo of glaciers, causing them to absorb more solar radiation, which then accelerates snow grain growth, and induces melting of darker snow, which further lowers the albedo via the positive albedo feedback mechanism (Andreae and Gelencsér, 2006; Yasunari et al., 2010; Bond et al., 2013; Booth and Bellouin, 2015; Di Mauro et al., 2015). This reduction in albedo increases the heating of the snow and ice surfaces, thus accelerating melting, shortening the snow duration, altering the mass balance, and causing the retreat of mountain glaciers (Xu et al., 2009; Yasunari et al., 2010; Jacobi et al., 2015). These effects can change the amount of available water resources from the cryosphere and affect runoff and social development in downstream regions (Immerzeel et al., 2010).

The Tibetan Plateau and adjacent areas (including the Tien Shan region) contain the largest volume of ice outside the Polar Regions (Kang et al., 2010; Yao et al., 2012). Studies on the Tibetan Plateau and its surroundings have shown that relatively small concentrations of BC can substantially alter the snow surface energy balance and albedo (Ming et al., 2012, 2016; Qu et al., 2014; Yang et al., 2015; Schmale et al., 2017). Analysis of snowpits in western China show that BC concentrations were highest around the margins of the Tibetan Plateau and at lower elevations, likely due to the proximity to BC sources and to melting, which concentrates black carbon loading (Ming et al., 2009, 2016; Xu et al., 2012; Kaspari et al., 2014; Li et al., 2016). Mountains close to human population centers (e.g., Tien Shan) show substantially higher levels of BC than remote glaciers (e.g., East Rongbuk Glacier on Mt. Everest), indicating that anthropogenic emissions served as a source of BC and other LAIs on glaciers (Schmitt et al., 2015; Li et al., 2016). In addition to the impact of BC, mineral dust deposits can alter the radiative balance of a glacier by reducing the albedo through increased melting, which exposes darker surfaces to direct solar radiation (Kaspari et al., 2014; Qu et al., 2014; Di Mauro et al., 2015;). However, estimates of the albedo reduction/glacier melt caused by these LAIs remained uncertain because of sparse in situ observations and the complex interactions between LAIs and albedo (Flanner et al., 2007; Ming et al., 2009; Yasunari et al., 2010; Kaspari et al., 2014; Qu et al., 2014; Qian et al., 2015; Schmitt et al., 2015; Gertler et al., 2016).

Populations in Central Asia, such as in Tien Shan, are heavily dependent on snow and glacial melt for their water supplies. The Tien Shan range is surrounded by vast arid regions of the Taklimakan and Gurbantünggüt deserts and is directly exposed to emissions of carbonaceous aerosols and mineral dusts, with likely impacts from light-absorbing aerosols.Farinotti et al. (2015) suggested that the decline of glaciers in Tien Shan was driven primarily by summer melt and possibly linked to the combined effects of general climatic warming and circulation variability over the north Atlantic and north Pacific. Data on the deposition of LAIs and their effects on reducing the snow albedo of Tien Shan glaciers and on glacier melt are limited to measurements of BC concentrations in snowpack in eastern Tien Shan (Urumqi River No. 1 Glacier) (Xu et al., 2012; Ming et al., 2016) and western Tien Shan (Schmale et al., 2017). An alternative approach to estimating LAIs over Tien Shan glaciers and a better understanding of the impacts of LAIs on albedo reduction and glacier melt are needed.

In this study, surface snow samples were collected from Keqikaer Glacier in western Tien Shan during an expedition in May of 2015 and were analyzed for BC, OC and mineral dust concentrations. The study intended to document LAIs concentrations on Keqikaer Glacier, and to estimate their impacts on surface albedo reduction by using the Snow Ice Aerosol Radiative (SNICAR) model. Furthermore, the origins of BC were traced using backward air-mass trajectories and footprint analysis.

2 Methodology 2.1 Study area

The Tien Shan is one of the largest mountain systems in Central Asia, covering an area of 800,000 km2 between 69°E–95°E and 39°N–46°N (Figure 1a). About half of the Tien Shan region is above 3,000 m a.s.l., with the highest point located at 7,439 m a.s.l.. The main mountain ranges are oriented east-west and act as a barrier to northern air masses moving northward toward Central Asia. The Tien Shan thus plays an important role in the climatic processes of northern Central Asia, similar to that of the Himalayas to the south (Aizen et al., 1997). The Tien Shan is in a region of internal drainage (Shi, 2008) and the large amount of meltwater released in the Tien Shan glaciers is the primary water source to the arid regions of western China.

Figure 1 Map showing the locations of (a) western Tien Shan and (b) Keqikaer Glacier. Maps of geopotential heights in the study region based on NCEP/NCAR reanalysis data during (c) summer and (d) winter are also shown

Keqikaer Glacier (Y674A5, 41°48.77′N, 80°10.20′E) (Figure 1b) is located on the south slope of Mt. Tomur, the highest peak in western Tien Shan, on the border between China and Kyrgyzstan. The glacier covers an area of 83.56 km2 and has a length of 26 km (Shi, 2008). Keqikaer Glacier has retreated considerably since 1990 at an annual rate of 15~20 m/a (Xie et al., 2007). The monitoring record of Keqikaer Glacier shows that the equilibrium line altitude (ELA) was 4,300 m a.s.l. (Shi, 2008). Our sampling sites were below the ELA; thus, all of our sites were located in the ablation zone. Abundant solid precipitation in the firn basin and intense ablation in the ablation areas illustrated that Keqikaer Glacier is very active, with high surface velocities and strong erosion, and transport capacity. Rock debris from avalanches and lateral moraines in the ice from the upper reaches of the glacier formed a heavy debris cover that is composed of a mixture of gray and dark brown silt and gray granite detritus at the glacier terminus, which reduces ablation (Han et al., 2006a). The supraglacial debris cover has reversed the ablation gradient and reduced the equilibrium accumulation area ratio, which has allowed the glacial terminus to spread to lower altitudes. Debris thickness increases from zero at 3,900 m a.s.l. to over 2.0 m near the terminus of the glacier (Han et al., 2006b). The glacier has an ablation zone of approximately 30.6 km2, with 60% of the area covered by debris (Zhang et al., 2006; Juen et al., 2014). The majority of the glacier surface area is situated between 4,600 and 4,800 m a.s.l. (Han et al., 2010).

The main factor determining the climatic regimes is the interaction between the southwestern branch of the Siberian anticyclonic circulation and cyclonic activity from the west (Figures 1c and 1d). The western region is weakly influenced by the Siberian anticyclonic circulation and moderately influenced by the southwestern cyclonic circulation which brings warm, moist air masses into the region (Aizen et al., 1997). Maximum precipitation occurs during the spring from March to June. Annual precipitation at the equilibrium line is approximately 750~850 mm (Han et al., 2006a). Fresh snow can quickly change to aged snow in this region.

2.2 Sampling and filtration

During the Keqikaer Glacier expedition in May of 2015, a total of 24 samples of aged snow and firn ice samples were collected at altitudes ranging from 2,990 to 4,000 m a.s.l. (Figure 1 and Table 1). The snow/ice sampling procedure followed the "Clean Hands-Dirty Hands" principle. These samples were preserved in Whirl-Pak bags (Nasco, USA) and were kept frozen during transportation until they were analyzed in the laboratory.

Table 1 Detailed information of sampling in Keqikaer Glacier
Sample No. Lat. (°N) Lon. (°E) Elevation (m a.s.l.) Snow type Sampling time
KQKE-1 41.702 80.162 2,997 Aged snow 2015.5.1; 11:11
KQKE-2 41.701 80.171 3,003 Aged snow 2015.5.1; 11:21
KQKE-3 41.701 80.171 3,003 Aged snow 2015.5.1; 11:30
KQKE-4 41.701 80.171 3,003 Firn ice 2015.5.1; 12:00
KQKE-5 41.701 80.158 3,095 Aged snow 2015.5.1; 12:35
KQKE-6 41.701 80.160 3,095 Aged snow 2015.5.1; 12:25
KQKE-7 41.753 80.136 3,522 Aged snow 2015.5.4; 9:45
KQKE-8 41.761 80.127 3,583 Aged snow 2015.5.4; 10:15
KQKE-9 41.763 80.123 3,610 Aged snow 2015.5.4; 10:40
KQKE-10 41.760 80.115 3,582 Aged snow 2015.5.4; 11:40
KQKE-11 41.765 80.111 3,622 Aged snow 2015.5.4; 12:25
KQKE-12 41.768 80.108 3,658 Aged snow 2015.5.4; 13:25
KQKE-13 41.779 81.100 3,729 Aged snow 2015.5.4; 15:35
KQKE-14 41.791 80.096 3,814 Aged snow 2015.5.5; 8:30
KQKE-15 41.805 80.105 3,899 Aged snow 2015.5.5; 10:00
KQKE-16 41.806 80.106 3,916 Aged snow 2015.5.5; 10:20
KQKE-17 41.811 80.122 4,001 Aged snow 2015.5.5; 13:50
KQKE-18 41.736 80.141 3,362 Aged snow 2015.5.6; 10:45
KQKE-19 41.721 80.143 3,254 Firn ice 2015.5.6; 11:40
KQKE-20 41.712 80.147 3,190 Aged snow 2015.5.6; 12:35
KQKE-21 41.777 80.102 3,709 Firn ice 2015.5.10; 11:40
KQKE-22 41.777 80.102 3,718 Firn ice 2015.5.10; 11:50
KQKE-23 41.777 80.100 3,713 Firn ice 2015.5.10; 13:00
KQKE-24 41.777 80.102 3,700 Firn ice 2015.5.10; 13:40

In the laboratory, snow and bare ice samples were melted rapidly by placing the Whirl-Pak bags in warm water (usually approximately 30 °C). The samples were filtered immediately after melting to minimize adhesion of particles to the surface of the bag. Once melted, snow-sourced water was poured into a 300 mL percolator and pumped slowly through the dried 47 mm quartz fiber filters (Whatman TM). A total of approximately 600 mL snow-water was filtered per sample unless the filter became too clogged, in which case less water was filtered, and the amount of filter water was recorded. The time between the starting of melting and the completion of filtered was 15~20 minutes. The filters were removed from the filter holders, placed into plastic capsules designed for coin collections, and held in place with a thin foam ring. These filters were returned to the Stake Key Laboratory of Cryospheric Sciences, Chinese Academy of Sciences in Lanzhou where they were stored in a freezer. Before analysis, the filters were dried in an oven at 50 °C for 24 hours to eliminate the water vapor.

2.3 Measurement of dust, BC and OC

The quartz filters were used to measure dust, BC and OC concentrations. The quartz filters were weighted gravimetrically after filtration. Since the mass of dust on the filters was far larger than the masses of BC and OC on the filters (Kaspari et al., 2014), it was assumed that the total dust mass was equal to the difference in the weight of the filters before and after filtration.

Using an adapted IMPROVE protocol (Cao et al., 2003; Chow et al., 2004), the amounts of BC and OC on the quartz filters were measured by using a DRI® Model 2001A thermal optical carbon analyzer. Because the dust load in the snow/ice samples was greater than that in normal aerosol samples, the IMPROVE protocol was modified that only one temperature plateau (550 °C) was reached in a 100% helium atmosphere in order to reduce the time that the BC was exposed to a catalyzing atmosphere ( Yang et al., 2015). The reported OC concentrations from snow and ice samples can account only for water-insoluble OC; most of the water-soluble OC was not captured by the filter-based method. During the analysis, the detection limit of the analyzer was 0.19±0.13 μg total carbon (TC)/cm2 and the filter blank was 1.23±0.38 μg TC/cm2, which was much lower than the measured sample values.

2.4 Albedo reduction and radiative forcing

The SNICAR model can be used to simulate the hemisphere reflectance of snow and ice for unique combinations of impurity contents (e.g., BC, dust, volcanic ash), snow effective grain size, and incident solar flux characteristics (Flanner et al., 2007). In the SNICAR model, snow effective grain sizes were derived from the stratigraphy. These grain size values ranged from 250 to 1,500 μm for aged snow and bare ice, and they were classified into low, medium and high grain size scenarios in the model runs. Specifically, the snow effective grain sizes are listed in Table 2 based on the layer-specific snow types. Snow density varies with crystal size, shape, and the degree of rimming (Judson and Doesken, 2000). According to previous studies that have examined snow and ice cores (Judson and Doesken, 2000; Sjögren et al., 2007), snow density data used in the SNICAR model are summarized in Table 3 with low, medium and high density scenarios in the model runs.

Table 2 The effective grain sizes of snow used in the simulation of albedo with the SNICAR model
Snow state description Medium scenario (μm) Low scenario (μm) High scenario (μm)
Aged snow 500 250 1,000
Moderately dirty, firn ice, coarse grains 500 250 1,000
Table 3 Snow density values used in the simulation of albedo with the SNICAR model
Snow state description Medium scenario (kg/m3) Low scenario (kg/m3) High scenario (kg/m3)
Aged snow 300 200 600
Moderately dirty, firn ice, coarse grains 600 300 800

In terms of the albedo calculation, radiative forcing (RF) values due to BC and dust can be obtained by using Equation (1) (Kaspari et al., 2014; Yang et al., 2015):

${\rm{RF}} = \sum\nolimits_{0.325{\text{μ}}\rm m}^{1.075{\text{μ}}{\rm m}} E \left({\textit{λ}, \textit{θ} } \right)\left({{\textit{α} _{\left({r, \textit{λ} } \right)}} -{\textit{α} _{\left({r, \textit{λ}, imp} \right)}}} \right)\Delta \textit{λ} $ (1)

where α is the modeled snow spectral albedo with or without the impurities (imp) of BC and/or dust; E is the spectral irradiance; r is the snow optical grain size; λ is wavelength (μm); and θ is the solar zenith angle for irradiance.

2.5 Footprint analysis

To determine the probable source of the LAIs deposited on Keqikaer Glacier, back-trajectory analyses were performed using European Center for Medium-Range Weather Forecasts (ECMWF) analysis fields with the Lagrangian analysis tool LAGRANTO (Wernli and Davies, 1997; Sprenger and Wernli, 2015), which was launched every six hours during the study periods. The ECMWF fields (horizontal and vertical wind components) were retrieved on 137 model levels and then interpolated onto a 0.25°×0.25° latitude-longitude grid.

Seasonal footprints were derived for Keqikaer Glacier from December 2014 to February 2015 and from May 2015 to September 2015. For the seven-day back trajectories, trajectory divergence was taken into account by varying the release altitude in 50-hPa steps and shifting the starting positions by ±0.05° in the east-west and north-south directions. The positions of the air parcels along their backward trajectories are available at a 6-h temporal resolution.

For BC emissions from fire spots (open fires), the FINN v1.5 global fire emission inventory in 2013, speciated with the GEOS-chem mechanism (Wiedinmyer et al., 2011), was used to estimate BC emissions attributable to natural fires. The fire inventory provides a daily and global fire emission product. The spatial resolution of the inventory is approximately 1 km2. To estimate whether an air parcel at the measurement site is influenced by fire emissions, fire spots are counted along the backward trajectories. However, because the temporal resolution of the FINN inventory (1 day) does not coincide with that of the trajectories (6 hours), some assumptions have to be made. In particular, it is assumed that the fire emissions (or fire spots) are valid during a whole day and that these fire spots contribute to an air parcel along a trajectory if this parcel passes nearby. Further, it is assumed that the air parcel can be influenced by fire spots within a certain distance. The sensitivity of our results is tested by applying different neighborhood criteria, specifically 50, 100, 150, and 200 km.

3 Results and discussion 3.1 Concentrations of LAIs

The average concentrations of BC was 2,180 ng/g, with a range that spanned from 250 ng/g to more than 10,000 ng/g (Figure 2). BC in aged snow of Keqikaer Glacier showed little difference relative to that measured on other glaciers in Tien Shan, for example, BC concentrations of aged snow on the Urumqi Glacier No.1 were also as high as 1,738 ng/g and displayed large variations (Ming et al., 2016). On the other hand, previous studies based on snowpit records documented concentrations of about 50 ng/g on the Tien Shan glaciers (Ming et al., 2012). Meanwhile, even in the Himalayan region, BC concentrations can reach several hundred ng/g as a result of post-depositional processes that cause snow metamorphism (Kaspari et al., 2014). Ming et al. (2012) and Xu et al. (2012) suggested that the distribution of BC on the surfaces of high Asian glaciers depend primarily on elevation (i.e., higher sites have lower concentrations) and secondarily on regional BC emissions and surface melting conditions.

Figure 2 Average concentrations of BC, OC and mineral dust on the Keqikaer glacial snow. (The solid black, red, and blue squares represent the mean concentrations of BC, OC, and dust concentrations, respectively. The outlier box plot is a graphical summary of the distribution of the data. The median is the 50th percentile, and the 25th and 75th percentiles (25% and 75%, respectively) are called the quartiles.)

The average OC concentration on Keqikaer Glacier was 1,739 ng/g. This was much higher than the highest concentrations of OC in aged snow on Muji Glacier (in the western-most Tibetan Plateau) of approximately 325 ng/g (Yang et al., 2015). At the Laohugou No. 12 Glacier (in the Qilian Mountains), the OC concentrations in aged snow were approximately 2,800 ng/g (unpublished data), while in the southeastern Tibetan Plateau, OC concentration in aged snow was about 7,000 ng/g (Zhang et al., 2016). The large variations among these values may be attributable to variations in snow melting or regional emission.

The concentrations of mineral dust in aged snow of Keqikaer Glacier varied from 18.2 to 570 μg/g, with average concentrations of 194 μg/g (Figure 2). The average concentration of dust (194 μg/g) was similar to that found in glacier snow from Tien Shan by Ming et al. (2016), but much higher than that found in the southern Tibetan Plateau by Zhang et al. (2016). The debris cover on Keqikaer Glacier may play an important role in the deposition of dust.

3.2 Potential sources of impurities

Backward air-mass analysis offers a simple assessment of source-receptor relationships (Stein et al., 2015). Only backward trajectories starting below 500 hPa are taken into account in the footprint analysis (see section 2.5). In Figure 3, the impact of fire spots on the air mass transportation to Keqikaer Glacier is indicated by colored dots. More specifically, black dots represent air parcels that did not pass near a fire between −96 h and arrival at the study site. Green dots represent air parcels that passed by a fire between −96 h and −48 h, but not afterwards,i.e., the 'contact' with a fire occurred at least 48 h before the air parcel arrived at Keqikaer Glacier. Therefore, magenta dots represent air parcels with no contact to a fire before −48 h, but do pass by a fire during the time 'immediately' (between −48 h and 0 h) before arriving at the site. Finally, red dots show air parcels that have contact with fires before and after −48 h. Each dot represents the position of an air parcel 48 h before it arrives at the measurement site. The footprint analysis indicates that the summer air masses originate from Central Asia, Siberia, western China (including the Taklimakan Desert), and part of South Asia (Figure 3a). On the other hand, during the winter (Figure 3b), due to the strong westerly transport, air masses mainly come from the Middle East and Central Asia, while those originating from western China are rarer. This discrepancy emphasizes the importance of source strength changes, which can affect the concentrations of specific impurities in the snow.

Figure 3 Footprints launched over Keqikaer Glacier (star) during (a) summer (June–August 2015) and (b) winter (December 2014 to February 2015). Only trajectories starting below 500 hPa were taken into account. Black dots: the air parcel did not pass near a fire between −96 h and arrival at the study site. Green dots: air parcels that passed by a fire between −96 h and −48 h, but not afterwards;i.e., the 'contact' with a fire occurred at least 48 h before the air parcel arrived at Keqikaer Glacier. Magenta dots: air parcels that have no contact with a fire before −48 h, but do pass by a fire during the time 'immediately' (between −48 h and 0 h) before

Data from the CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation) mission ( was used to characterize the vertical distribution of atmospheric pollution transportation around the study area. CALIPSO transects show that many pollutants were noted over Tien Shan (Figure 4), which indicated an influence from atmospheric pollutants transported to Tien Shan. Although, there was heavy pollution over South Asia, backward trajectories implied the transportation of air masses to the study area from central Asia, rather than from South Asia (Figure S1). By comparing atmospheric circulation (Figure 1c and 1d), air mass trajectories (Figure 3), and CALIPSO results (Figure 4), we concluded that the impurities deposited on Keqikaer Glacier were influenced mainly by the Siberian anticyclone, and the Westerlies in winter. The study region was primarily impacted by dust aerosols transported from the arid regions of the Middle East, and central Asia (Figure 3). Thus, the regional atmospheric environment and large-scale circulation exerted a considerable impact on the deposition of LAIs (Dong et al., 2016).

Figure 4 (a) CALIPSO vertical profile at 20:54 UTC on May 4, 2015, showing the color-coded total attenuated back scatter Lidar return signal at 532 nm in green, yellow and red, indicating aerosols at low, medium and high concentrations, respectively; (b) Cloud-aerosol output, with 1 clear air, 2 cloud, 3 aerosol, 4 stratospheric layer, 5 surface, 6 subsurface and 7 totally attenuated; and (c) Aerosol subtype profiles with 1 clean marine, 2 dust, 3 polluted continental, 4 clean continental, 5 polluted dust and 6 smoke
Figure S1 Backward trajectories arrived at Keqikaer glacier on May 4, 2015, calculated every 6 h based on NCEP/NCAR reanalysis data

A large fraction of atmospheric BC concentrations is the result of anthropogenic activities (Bond et al., 2013). To further identify the overall contributions from biomass (mainly from natural fires and straw burning) and non-biomass (mainly from anthropogenic activities, such as fossil fuel combustion) sources of BC deposition on Keqikaer Glacier, the Lagrangian footprints for natural fire BC emissions in summer and winter were calculated (Figure 5). The FINN fire emission inventory was used for the period from December 2014 to September 2015 to derive BC contributions from biomass burning, and it was found that they contributed less than 40% of the overall BC deposition, with a greater amount occurring during summer (approximately 40%) than in winter (approximately 10%) (Figures 5a and 5b). This indicates that non-biomass combustion (representing anthropogenic emissions, mainly emitted by vehicles and industrial sources) were the main source of BC, accounting for at least 60% of the mass found at Keqikaer Glacier in summer and 80% of that deposited in winter. In western Tien Shan, more than 90% of the BC was from anthropogenic sources, with Central Asia being the largest contributor (Schmale et al., 2017). Along the northern edge of the Tibetan Plateau (at the Laohugou No. 12 Glacier in Qilian Shan), fossil fuels contributed 66%±16% of the deposited BC, based on snowpit results, reflecting anthropogenic emissions transported from western China (Li et al., 2016). As the dominant contributor, anthropogenic BC emissions may play an important role in glacier melt (Schmale et al., 2017).

Figure 5 Contributions of biomass sourced BC emissions deposited at Keqikaer Glacier during (a) summer (June–August 2015) and (b) winter (December 2014 to February 2015). (A value of 40% att=−48 h, for instance, means that 40% of the trajectories passed a fire spot within the search radius.). The four bars at each time correspond to the neighborhood distance applied during the analysis (see section 2.5)

During the summer, the prevailing Siberian anticyclone transports air masses that originate in Russian Siberia (Figure 1c and Figure 3a) to Keqikaer Glacier. The extratropical cyclones of the mid-latitudes are responsible for air mass transport from South Asia and from the east (western China), in particular from the Taklimakan region. During the winter, the westerlies transport air masses originating from Central Asia and the Middle East (Figure 1d and Figure 3b). The growing economies in these regions exacerbate anthropogenic emissions of BC. For example, in India, domestic fuel use and industry contribute approximately 69% to total BC emissions, while open burning contributes only 12% (Paliwal et al., 2016). The residential use of solid fuels, including coal, firewood, and crop residues, contributes more than half of total BC emissions in China, while diesel vehicles and brick kilns contribute approximately 15% ( Wang et al., 2012). Recent studies indicate that BC emissions could be underestimated by a factor of 2~3, particularly in Asian regions experiencing rapid growth (Menon et al., 2010; Bond et al., 2013; Wang et al., 2014). Thus, the true contributions of anthropogenic BC may be underestimated.

3.3 Estimation of albedo reduction and radiative forcing

The surface energy balances and mass balances of glaciers depend on the albedo of the snow, which controls the amount of solar energy absorbed (Dumont et al., 2014; Qu et al., 2014). In this study, we used the SNICAR model, which is based on Matlab software, to investigate the impact of LAIs on reductions in surface albedo (Flanner et al., 2007). Because our objective was to estimate the order of magnitude of the effects of impurities on the snow radiation balance, we ran the SNICAR model using several simplifying assumptions, including low, medium, and high scenarios for snow density and grain size (Tables 2 and 3). Cosines of the solar zenith angle in May at solar noon were calculated based upon the position of the sun relative to the location of Keqikaer Glacier. The albedo of the underlying ground was taken to be 0.2 in the visible band and 0.4 in the near-infrared band. The mass absorption cross-section (MAC) of BC was set to be 1, which is the median of typical MAC values (Hadley and Kirchsteller, 2012) and has been used for Tibetan glaciers (Qu et al., 2014). The incident radiation was assumed to be direct and the volcanic ash concentration (μg/g) was set to be zero. The detailed parameter values used in the sensitivity analysis of the SNICAR model are listed in Table 4.

The results for the different scenarios defined in Tables 2 and 3 indicate that, if dust were the only LAI, the albedo reduction would be 0.02 with associated instantaneous radiative forcing of 24.05 W/m2 (ranging from 0.15 to 69.77 W/m2) (Figure 6). If BC were the only impurity in glacial snow, the albedo reduction would be 0.29 with associated instantaneous radiative forcing of 323.18 W/m2 (ranging from 142.16 to 619.25 W/m2). If BC and dust were mixed, the albedo reduction varied minimally from scenarios that include only BC, and radiative forcing increased by less than 1 W/m2 over the BC-only scenarios.

Figure 6 Albedo values and radiative forcing changes resulting from BC and dust in glacial snow on Keqikaer Glacier. (The red and blue squares represent the mean values of albedo and radiative forcing, respectively. The vertical lines represent the error bars.)

Because the optical properties of BC and dust were obtained from the literature and the values were not for this specific region, the modeling of radiative forcing was relatively uncertain (Kaspari et al., 2014). Schwarz et al. (2013) found that BC size in snow was the dominant source of uncertainty in the absorption properties of BC when calculating snow albedo climate forcing attributable to BC.Schwarz et al. (2012, 2013) found that BC particles were larger in snow than in the atmosphere, in part due to the influences of the processes associated with the removal of BC from the atmosphere. The differences of BC size range and morphology between the snow and the ambient atmosphere can cause a change of BC MAC in snow because larger BC particles tend to absorb light less efficiently per unit mass than smaller particles (Liou et al., 2011; Schwarz et al., 2013; Peng et al., 2016). Schwarz et al. (2013) noted substantial decreases (up to 40%) in MAC calculated for BC in the size range observed in snow compared to that seen in the atmosphere. In this study, we based the radiative forcing on the assumption that MAC was equal to 1; this may have introduced some uncertainties with respect to the actual albedo and radiative forcing because of the difference in BC's MAC in glacial snow. Overall, the uncertainty of the maximum forcing by impurities at Keqikaer Glacier is judged to be an order of magnitude.

Our results also indicate that the effects of BC on albedo and radiative forcing were considerably greater than those of dust (Figure 6). BC in snow is regarded as an active factor controlling snow albedo and radiative forcing (Bond et al., 2013). However, Kaspari et al. (2014) and Yang et al. (2015) noted that increasing concentrations of dust may also play an important role, particularly when dust is at an extremely high level near the glacier terminus or in early autumn. The presence of dust in snow decreases its albedo in the visible wavelengths up to 40% (Di Mauro et al., 2015). In this analysis, we inferred dust concentrations from the gravimetric mass, which may underestimate the effects of dust in glacial snow due to light-absorbing iron oxides (Kaspari et al., 2014; Zhang et al., 2015). Furthermore, high BC concentrations in the snow on Keqikaer Glacier may mask the effects of dust. Future studies should include high-resolution sampling of fresh snow to determine the concentrations of impurities during the melt season.

Table 4 Parameters for sensitivity analysis with SNICAR model for glaciers in the southeastern Tibetan Plateau
Site 1 2 3 4* 5 6# 7a 7b 8 9-BC 10-Dust 11 12
KQKE-1 direct 26.74 a 0.05 0.2 0.4 1 1,823 33 0 0
KQKE-2 direct 26.74 a 0.09 0.2 0.4 1 11,360 570 0 0
KQKE-3 direct 26.74 a 0.09 0.2 0.4 1 6,279 292 0 0
KQKE-4 direct 26.74 a 0.10 0.2 0.4 1 1,296 86 0 0
KQKE-5 direct 26.74 a 0.05 0.2 0.4 1 914 15 0 0
KQKE-6 direct 26.74 a 0.05 0.2 0.4 1 1,158 59 0 0
KQKE-7 direct 25.90 a 0.05 0.2 0.4 1 5,590 475 0 0
KQKE-8 direct 25.90 a 0.10 0.2 0.4 1 3,188 293 0 0
KQKE-9 direct 25.90 a 0.05 0.2 0.4 1 5,665 458 0 0
KQKE-10 direct 25.90 a 0.05 0.2 0.4 1 964 81 0 0
KQKE-11 direct 25.90 a 0.05 0.2 0.4 1 600 158 0 0
KQKE-12 direct 25.90 a 0.05 0.2 0.4 1 819 86 0 0
KQKE-13 direct 25.90 a 0.05 0.2 0.4 1 946 75 0 0
KQKE-14 direct 25.66 a 0.05 0.2 0.4 1 1,024 60 0 0
KQKE-15 direct 25.66 a 0.05 0.2 0.4 1 863 92 0 0
KQKE-16 direct 25.66 a 0.14 0.2 0.4 1 252 80 0 0
KQKE-17 direct 25.66 a 0.10 0.2 0.4 1 809 565 0 0
KQKE-18 direct 25.31 a 0.26 0.2 0.4 1 374 102 0 0
KQKE-19 direct 25.31 a 0.15 0.2 0.4 1 1,626 520 0 0
KQKE-20 direct 25.31 a 0.05 0.2 0.4 1 970 41 0 0
KQKE-21 direct 24.25 a 0.05 0.2 0.4 1 1,008 163 0 0
KQKE-22 direct 24.25 a 0.05 0.2 0.4 1 335 18 0 0
KQKE-23 direct 24.25 a 0.05 0.2 0.4 1 4,048 275 0 0
KQKE-24 direct 24.25 a 0.05 0.2 0.4 1 423 47 0 0
Note: 1-Incident radiation (a, Direct, b, Diffuse); 2-Solar zenith angle; 3-Surface spectral distribution (a, Mid-latitude winter, clear-sky, cloud amount<5. b, Mid-latitude winter, cloudy, cloud amount ≥5); 4-Snow grain effective radius (μm); 5-Snowpack thickness (m); 6-Snowpack density (kg/m3); 7-Albedo of underlying ground (a, Visible, 0.3–0.7 μm, b, Near-infrared, 0.7–5.0 μm); 8-MAC scaling factor (experimental) for BC; 9-BC concentration (ppb, sulfate-coated); 10-Dust concentration (ppm, 5.0–10.0 μm diameter); 11-Volcanic ash concentration (ppm); 12-Experimental particle 1 concentration (ppb).* is referred to Table 2 and # referred to Table 3, according to different snow type.

Numerous studies of radiative forcing caused by BC have been conducted on the Tibetan Plateau (Figure 7). Our estimated instantaneous radiative forcing at Keqikaer Glacier was larger than the values obtained by most previous studies, perhaps as a result of BC enrichment by melt scavenging or snow aging. For instance, snowpit results from the accumulation zones of glaciers showed that BC deposition caused a mean forcing of only about 5 W/m2 (Ming et al., 2012). In eastern Tien Shan, the difference in albedo between fresh and aged snow can be as great as 0.40, indicating a consequent forcing of 180 W/m2 during the post-deposition process (Ming et al., 2016). A previous study on the southern slope of the Himalayas indicated that the concentrations of LAIs in aged snow corresponded to localized instantaneous radiative forcing values of 75~120 W/m2 (Kaspari et al., 2014). Meanwhile, the input parameters (including incident radiation, solar zenith angle, surface spectral distribution, snow grain effective radius, snowpack thickness and density, albedo of the underlying ground, and the concentrations of impurities) of the SNICAR model can also produce differences in albedo reduction and radiative forcing, especially snow aging (Guo et al., 2015; Ming et al., 2016; Schmale et al., 2017). Another important input parameter is the BC's MAC (Wang et al., 2015; Ming et al., 2016), which may impact the calculations of RF presented by different studies.

Figure 7 Ranges of radiative forcing (RF) values caused by BC in snow on the Tibetan Plateau and surrounding areas identified by previous studies

The current SNICAR model cannot simulate the effect of OC in snow in the same way as for BC because of a lack of reliable OC optical properties, such as MAC values and the contributions of brown carbon (light-absorbing OC) to total OC (Flanner et al., 2007). As a result of the light-absorbing properties of OC (Andreae and Gelencsér, 2006), the radiative forcing of organic aerosols deposited on land snow and sea ice ranges from +0.0011 to +0.0031 W/m2, or as much as 24% of the estimated forcing caused by BC in snow and ice as simulated by the model (Lin et al., 2014). Wang et al. (2015) estimated the forcing due to OC in a southeastern Tibetan Plateau ice core and identified a fourfold increase from 0.2 W/m2 (for a mean OC concentration of 13.8 ng/g during 1956–1979) to 0.84 W/m2 (for a mean OC concentration of 61.3 ng/g in 2006), or 27% and 43% of the corresponding BC in snow forcing, respectively. The high OC concentrations noted in this study (Figure 2) suggest that the contribution of OC to the total radiative forcing experienced by the glacier that is induced by snow/ice impurities deserves more attention. Peng et al. (2016) explored how changes in aerosol morphology and coatings affected the absorption by ambient BC and found that aged BC aerosols coated with OC and other impurities had an absorption that was enhanced by a factor of 2.4 relative to BC in fresh emissions (Gustafsson and Ramanathan, 2016). The different absorption and scattering by aggregates of BC in snow with external and internal mixing structures led to substantial under-or overestimation of radiative forcing (Liou et al., 2011; Liu et al., 2015). The effect of OC in glacial snow warrants additional study because of OC's non-negligible light absorption.

3.4 Implications

Glacial meltwater serves as an important natural resource for agriculture, hydroelectric power, and drinking water, particularly in arid regions. An increased understanding of the pressures on this resource could aid regional planners in adapting to future regional climate changes (Schmitt et al., 2015). Central Asia is characterized by a semiarid-to-arid climate and contains large deserts. The water resources for large parts of the region originate in headwater areas (glaciers and snow cover) in high-mountain regions (Immerzeel et al., 2010), such as the Pamir and Tien Shan (Unger-Shayestech et al., 2013). In these mountainous regions, both glaciers and snowpack provide intermediate storage of water resources, on which more than 60% of the population depends (Aizen et al., 1997; Karthe et al., 2015). In recent decades, glacier shrinkage in Tien Shan has been most pronounced in peripheral, lower-elevation ranges near the densely populated forelands, where summers are dry and snow and glacial meltwater is thus an essential water resource (Sorg et al., 2012). In this study, we found that BC and dust were responsible for approximately 64% and 9%, of the albedo reduction, respectively, mainly in the ablation zone of the glacier (Figure 6). This darkening of the glacier due to albedo reduction governed the amount of solar energy that was absorbed, further affecting the surface energy and mass balance (Xu et al., 2009; Dumont et al., 2014; Jacobi et al., 2015). The deposition of LAIs can enhance melting by reducing the surface albedo (Flanner et al., 2007; Xu et al., 2012; Ming et al., 2016).

Due in part to rapid climate change, Central Asian glaciers and the water resources they provide are in peril (Rose, 2012; Sorg et al., 2012; Yao et al., 2012). If the loss of glacial mass continues, the "Asian Water Tower" region, including the Tien Shan and Himalayas, may experience more floods and ultimately a reduced water supply (Immerzeel et al., 2010; Xu et al., 2012; Yao et al., 2012; Jacobi et al., 2015), which may ultimately affect social development. Because BC from non-biomass sources is dominant in this region (Figure 5), regional BC mitigation and adaption efforts should be undertaken (Li et al., 2016).

4 Conclusions

This study presents the result of measurements of LAIs on Keqikaer Glacier in western Tien Shan, central Asia. Snow samples collected from the surface of Keqikaer Glacier were used to measure the LAIs using quartz filters. The results indicate that the average concentration of BC was 2,180 ng/g, and the measured concentrations ranged from 250 to over 10,000 ng/g. Moreover, OC had an average concentration of approximately 1,738 ng/g. The concentrations of LAIs measured on Keqikaer Glacier are a little higher than the values reported by most studies on the Tibetan Plateau, possibly as a result of strong glacier melt or regional emissions. BC and dust were responsible for approximately 64% and 9% of the albedo reduction, respectively, particularly in the ablation zone, with associated instantaneous radiative forcing values of 323.18 W/m2 (ranging from 142.16 to 619.25 W/m2) and 24.05 W/m2 (ranging from 0.15 to 69.77 W/m2), respectively. For different scenarios, the effects of BC on albedo and radiative forcing were considerably greater than those of dust. The estimated RF at Keqikaer Glacier was higher than reported by most previous studies from the Tibetan Plateau, perhaps because of BC enrichment by melt scavenging.

Analysis of the large-scale atmospheric circulation, combined with analysis of air mass backward trajectories and CALIPSO results, indicate that impurities deposited on Keqikaer Glacier are influenced by the Siberian anticyclone and the Indian monsoon in the summer and the westerlies in winter. The study region was primarily impacted by dust aerosols transported from arid deserts (including the Taklimakan and Thar deserts). The results also indicate that the regional atmospheric environment and large-scale circulation exert a considerable impact on the deposition of LAIs on Keqikaer Glacier. Because albedo reduction governs the amount of solar energy that is absorbed, darkening of the glacier further affects the surface energy and mass balance. Since a large fraction (>60%) of BC concentrations in Keqikaer Glacier were found to be caused by anthropogenic activities, these results may provide guidance for regional BC mitigation and adaptation actions.


This study is supported by the National Natural Science Foundation of China (41630754, 41671067, and 41501063), the Chinese Academy of Sciences (KJZD-EW-G03-04), the State Key Laboratory of Cryosphere Science (SKLCS-ZZ-2015) and the Foundation for Excellent Youth Scholars of Northwest Institute of Eco-Environment and Resources, CAS. The authors gratefully acknowledge the National Aeronautics and Space Administration (NASA) for the provision of the CLIPSO model used in this publication. The authors would also thank the reviewers' comments to improve this manuscript.

Aizen VB, Aizen EM, Melack JM, et al, 1997. Climatic and hydrological changes in the Tien Shan, central Asia. Journal of Climate, 10: 1393–1404. DOI: 10.1175/1520-0442(1997)010<1393:CAHCIT>2.0.CO;2
Andreae MO, Gelencsér A, 2006. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols.. Atmospheric Chemistry and Physics, 6(10): 3131–3148. DOI: 10.5194/acp-6-3131–2006
Booth B, Bellouin N, 2015. Black carbon and atmospheric feedbacks. Nature Climate Change, 519: 167–168. DOI: 10.1038/519167a
Bond TC, Doherty SJ, Fahey DW, et al, 2013. Bounding the role of black carbon in the climate system: A scientific assessment. Journal of Geophysical Research-Atmospheres, 118(11): 5380–5552. DOI: 10.1002/jgrd.50171
Cao JJ, Lee S, Ho K, et al, 2003. Spatial and seasonal distributions of atmospheric carbonaceous aerosols in Pearl River delta region, China. China Particuology, 1(1): 33–37. DOI: 10.1016/S1672-2515(07)60097-9
Chow JC, Watson JG, Chen LWA, et al, 2004. Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environmental Science and Technology, 38(16): 4414–4422. DOI: 10.1021/es034936u
Di Mauro B, Fava F, Ferrero L, et al, 2015. Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV and satellite observations. Journal of Geophysical Research-Atmospheres, 120: 6080–6097. DOI: 10.1002/2015JD023287
Dong ZW, Qin DH, Kang SC, et al, 2016. Individual particles of cryoconite deposited on the mountain glaciers of the Tibetan Plateau: Insights into chemical composition and sources. Atmospheric Environment, 138: 114–124. DOI: 10.1016/j.atmosenv.2016.05.020
Dumont M, Brun E, Picard G, et al, 2014. Contribution of light-absorbing impurities in snow to Greenland's darkening since 2009. Nature Geoscience, 7: 509–512. DOI: 10.1038/NGEO2180
Farinotti D, Longuevergne L, Moholdt G, et al, 2015. Substantial glacier mass loss in the Tien Shan over the past 50 years. Nature Geoscience, 8: 716–723. DOI: 10.1038/NGEO2513
Flanner MG, Zender CS, Randerson JT, et al, 2007. Present-day climate forcing and response from black carbon in snow. Journal of Geophysical Research-Atmospheres, 112: D11202. DOI: 10.1029/2006JD008003
Gertler CG, Puppala SP, Panday A, et al, 2016. Black carbon and the Himalayan cryosphere: A review. Atmospheric Environment, 125: 404–417. DOI: 10.1016/j.atmosenv.2015.08.078
Gustafsson O, Ramanathan V, 2016. Convergence on climate warming by black carbon. Proceedings of the National Academy of Sciences of the United States of America, 113(16): 4243–4245. DOI: 10.1073/pnas.1603570113
Hadley OL, Kirchsteller TW, 2012. Black carbon reduction of snow albedo. Nature Climate Change, 2: 437–440. DOI: 10.1038/nclimate1433
Han HD, Liu SY, Wang J, et al, 2010. Glacial runoff characteristics of the Koxkar Glacier, Tuomuer-Khan Tengri Mountain Ranges, China. Environmental Earth Sciences, 61(4): 665–674. DOI: 10.1007/s12665-009-0378-9
Han HD, Liu SY, Ding YJ, et al, 2006a. Investigation of ice cliffs in the debris-covered area of Koxkar glacier, Tien shan. Journal of Glaciology and Geocryology, 28: 879–884.
Han HD, Ding YJ, Liu SY, 2006b. A simple model to estimate ice ablation under a thick debris layer. Journal of Glaciology, 52: 528–536. DOI: 10.3189/172756506781828395
IPCC, 2013. Intergovernmental Panel on Climate Change 2013: The physical science basis. In: Stocker TF, Qin D, Plattner GK, et al. (eds.). New York: Cambridge University Press.
Immerzeel WW, van Beek LPH, Bierkens MFP, 2010. Climate change will affect the Asian water towers. Science, 328(5984): 1382–1385. DOI: 10.1126/science.1183188
Jacobi HW, Lim S, Menegoz M, et al, 2015. Black carbon in snow in the upper Himalayan Khumbu Valley, Nepal: observation and modeling of the impact on snow albedo, melting, and radiative forcing. The Cryosphere, 9: 1685–1699. DOI: 10.5194/tc-9-1685-2015
Judson A, Doesken N, 2000. Density of freshly fallen snow in the central Rocky Mountains. Bulletin of the American Meteorological Society, 81(7): 1577–1587. DOI: 10.1175/1520-0477(2000)081<1577:DOFFSI>2.3.CO;2
Juen M, Mayer C, Lambrecht A, et al, 2014. Impact of variying debris cover thickness on ablation: a case study for Koxkar glacier in the Tien Shan. The Cryosphere, 8: 377–386. DOI: 10.5194/tc-8-377-2014
Kang SC, Xu YW, You QL, et al., 2010. Review of climate and cryospheric change in the Tibetan Plateau. Environmental Research Letter, 5: 015101. DOI: 10.1088/1748-9326/5/1/015101.
Karthe D, Chalov S, Borchardt D, 2015. Water resources and their management in central Asia in the early twenty first century: status, challenges and future prospects. Environmental Earth Science, 73(2): 487–499. DOI: 10.1007/s12665-014-3789-1
Kaspari S, Painter TH, Gysel M, et al, 2014. Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings. Atmospheric Chemistry and Physics, 14(15): 8089–8103. DOI: 10.5194/acp-14-8089-2014
Li CL, Bosch C, Kang SC, et al, 2016. Sources of black carbon to the Himalayan-Tibetan Plateau glaciers. Nature Communications, 7: 12574. DOI: 10.1038/ncomms12574
Li Y, Chen JZ, Kang SC, et al., 2016. Impacts of black carbon and mineral dust on radiative forcing and glacier melting during summer in the Qilian Mountains, northeastern Tibetan Plateau. (Personal communication)
Liou KN, Takano Y, Yang P, 2011. Light absorption and scattering by aggregates: Application to black carbon and snow grains. Journal of Quantitative Spectroscopy and Radiative Transfer, 112: 1581–1594. DOI: 10.1016/j.jqsrt.2011.03.007
Lin GX, Penner JE, Flanner M, et al, 2014. Radiative forcing of organic aerosol in the atmosphere and on snow: effects of SOA and brown carbon. Journal of Geophysical Research-Atmosphere, 119: 7453–7476. DOI: 10.1002/2013JD021186
Liu S, Aiken AC, Gorkowski K, et al, 2015. Enhanced light absorption by mixed source black and brown carbon particles in UK winter. Nature Communications, 6: 8435. DOI: 10.1038/ncomms9435
Menon S, Koch D, Beig G, et al, 2010. Black carbon aerosols and the third polar ice cap. Atmospheric Chemistry and Physics, 10: 4559–4571. DOI: 10.5194/acp-10-4559-2010
Ming J, Xiao CD, Cachier H, et al, 2009. Black Carbon (BC) in the snow of glaciers in west China and its potential effects on albedos. Atmospheric Research, 92(1): 114–123. DOI: 10.1016/j.atmosres.2008.09.007
Ming J, Xiao CD, Du ZH, et al, 2012. An overview of black carbon deposition in High Asia glaciers and its impacts on radiation balance. Advances in Water Resources, 55: 80–87. DOI: 10.1016/j.advwatres.2012.05.015
Ming J, Xiao CD, Wang FT, et al, 2016. Grey Tien shan Urumqi Glacier No.1 and light-absorbing impurities. Environmental Science and Pollution Research, 23(10): 9549–9558. DOI: 10.1007/s11356-016-6182-7
Paliwal U, Sharma M, Burkhart JF, 2016. Monthly and spatially resolved black carbon emission inventory of India: uncertainty analysis. Atmospheric Chemistry and Physics, 16: 12457–12476. DOI: 10.5194/acp-16-12457-2016
Peng JF, Hu M, Guo S, et al, 2016. Markedly enhanced absorption and direct radiative forcing of black carbon under polluted urban environment. PNAS, 113(6): 4266–4271. DOI: 10.1073/pnas.1602310113
Qian Y, Yasunari TJ, Doherty SJ, et al, 2015. Light-absorbing particles in snow and ice: Measurement and modeling of climatic and hydrological impact. Advances in Atmospheric Science, 32(1): 64–91. DOI: 10.1007/s00376-014-0010-0
Qian Y, Yasunari TJ, Doherty SJ, et al, 2015. Light-absorbing particles in snow and ice: measurement and modeling of climatic and hydrological impact. Advances in Atmospheric Sciences, 32: 64–91. DOI: 10.1007/s00376-014-0010-0
Qu B, Ming J, Kang SC, et al, 2014. The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities. Atmospheric Chemistry and Physics, 14: 11117–11128. DOI: 10.5194/acp-14-11117-2014
Ramanathan V, Carmichael G, 2008. Global and regional climate changes due to black carbon. Nature Geoscience, 1(4): 221–227. DOI: 10.1038/ngeo156
Ramanathan V, Chung C, Kim D, et al, 2005. Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proceedings of the National Academy of Sciences of the United States of America, 102(15): 5326–5333. DOI: 10.1073/pnas.0500656102
Rose E, 2012. The ABCs of governing the Himalayas in response to glacial melt: atmospheric brown clouds, black carbon, and regional cooperation. Sustainable Development Law & Policy, 12(2): 33–37, 65–67.
Schmale J, Flanner M, Kang S, et al, 2017. Central Asia anthropogenic black carbon outweighs other impurities in driving regional snow melt. Scientific Report, 7: 40501. DOI: 10.1038/srep40501
Schmitt CG, All JD, Schwarz JP, et al, 2015. Measurements of light-absorbing particles on the glaciers in the Cordillera Blanca, Peru. The Cryosphere, 9: 331–340. DOI: 10.5194/tc-9-331-2015
Schwarz JP, Gao RS, Perring AE, et al, 2013. Black carbon aerosol size in snow. Scientific Reports, 3: 1356. DOI: 10.1038/srep01356
Schwarz JP, Doherty SJ, Ruggiero ST, et al, 2012. Assessing Single Particle Soot Photometer and Integrating Sphere/Integrating Sandwich Spectrophotometer measurement techniques for quantifying black carbon concentrations in snow. Atmospheric Measurement Techniques, 5: 2581–2592. DOI: 10.5194/amt-5-2581-2012
Shi YF, 2008. Concise Glacier Inventory of China. Shanghai: Shanghai Popular Science Press, pp. 1–205.
Sjögren B, Brandt O, Nuth C, et al, 2007. Instruments and methods determination of firn density in ice cores using image analysis. Journal of Glaciology, 53(182): 413–419. DOI: 10.3189/002214307783258369
Sorg A, Bolch T, Stoffel M, et al, 2012. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nature Climate Change, 2: 725–731. DOI: 10.1038/NCLIMATE1592
Sprenger M, Wernli H, 2015. The LAGRANTO Lagrangian analysis tool-version 2.0. Geoscientific Model Development, 8: 2569–2586. DOI: 10.5194/gmd-8-2569-2015
Stein AF, Draxler RR, Rolph GD, et al, 2015. NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society, 96(12): 2059–2077. DOI: 10.1175/BAMS-D-14-00110.1
Unger-Sayesteh K, Vorogushyn S, Farinotti D, et al, 2013. What do we know about past changes in the water cycle of Central Asian headwaters? A review.. Global and Planetary Change, 110(A): 4–25. DOI: 10.1016/j.gloplacha.2013.02.004
Wang M, Xu BQ, Cao JJ, et al, 2015. Carbonaceous aerosols recorded in a southeastern Tibetan glacier: analysis of temporal variations and model estimates of sources and radiative forcing. Atmospheric Chemistry and Physics, 15(3): 1191–1204. DOI: 10.5194/acp-15-1191-2015
Wang R, Tao S, Wang W, et al, 2012. Black carbon emissions in China from 1949 to 2050. Environmental Science & Technology, 46: 7595–7603. DOI: 10.1021/es3003684
Wang R, Tao S, et al, 2014. Exposure to ambient black carbon derived from a unique inventory and high-resolution model. Proceedings of the National Academy of Sciences of the United States of America, 111(7): 2459–2463. DOI: 10.1073/pnas.1318763111
Wiedinmyer C, Akagi SK, Yokelson RJ, et al, 2011. The Fire Inventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning. Geoscientific Model Development, 4: 625–642. DOI: 10.5194/gmd-4-625-2011
Wernli H, Davies HC, 1997. A Lagrangian-based analysis of extratropical cyclones. I: The method and some applications. Quarterly Journal of the Royal Meteorological Society, 123: 467–489. DOI: 10.1002/(ISSN)1477-870X
Xie CW, Ding YJ, Chen C, et al, 2007. Study on the change of Keqikaer glacier during the last 30 years, Mt. Tuomuer, Western China. Environmental Geology, 51: 1165–1170. DOI: 10.1007/s00254-006-0407-x
Xu BQ, Cao JJ, Hansen J, et al, 2009. Black soot and the survival of Tibetan glaciers. Proceedings of the National Academy of Sciences, 106(52): 22114–22118. DOI: 10.1073/pnas.0910444106
Xu BQ, Cao JJ, Joswiak DR, et al, 2012. Post-depositional enrichment of black soot in snow-pack and accelerated melting of Tibetan glaciers. Environmental Research Letters, 7: 014022. DOI: 10.1088/1748-9326/7/1/014022
Yang S, Xu BQ, Cao JJ, et al, 2015. Climate effect of black carbon aerosol in a Tibetan Plateau glacier. Atmospheric Environment, 111: 71–78. DOI: 10.1016/j.atmosenv.2015.03.016
Yao TD, Thompson LG, Yang W, et al, 2012. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change, 2: 663–667. DOI: 10.1038/NCLIMATE1580
Yasunari T, Bonasoni P, Laj P, et al, 2010. Estimated impact of black carbon deposition during pre-monsoon season from Nepal Climate Observatory-Pyramid data and snow albedo changes over Himalayan glaciers. Atmospheric Chemistry and Physics, 10(14): 6603–6615. DOI: 10.5194/acp-10-6603-2010
Zhang XL, Wu GJ, Zhang CL, et al, 2015. What is the real role of iron oxides in the optical properties of dust aerosols?. Atmospheric Chemistry and Physics, 15: 12159–12177. DOI: 10.5194/acp-15-12159–2015
Zhang YL, Kang SC, Zhang QG, et al, 2016. Chemical records in snowpits from high altitude glaciers in the Tibetan Plateau and its surroundings. PLoS ONE, 11(5): e0155232. DOI: 10.1371/journal.pone.0155232
Zhang Y, Liu SY, Ding YJ, et al, 2006. Preliminary study of mass balance on the Keqikaer Baxi Glacier on the south slopes of Tien shan Mountains. Journal of Glaciology and Geocryology, 28: 477–484.