Distributions and light absorption property of water soluble organic carbon in a typical temperate glacier, southeastern Tibetan Plateau

Sampling and analytical techniques

3.1.

Surface aged-snow and granular ice samples were collected from different elevations (ranged from 4340 to 4825 m asl) on Baishui glacier mostly in the melt season in 2014. Meltwater of surface snow on glacier was collected along the tributary from the glacier terminus to the upper ablation zone during the extensive melting period. Glacial stream water was collected from miniature streams on glacier surface during the extensive melting season. Two snow pits were excavated on 01 June and 10 June 2014, at the same site with an elevation of 4700 m asl on Baishui glacier. Maximum depth of the two snow pits were 1.6 m (excavated on 01 June) and 1.3 m (excavated on 10 June). Snow pit samples were immediately collected as soon as the pits were excavated, and samples were collected continuously at 10 cm depth intervals from the bottom to the top. Detailed information of sample collection is summarized in Table 1. Air temperature at the glacier surface was measured using a portable temperature transducer. The average temperature at the glacier surface was 0.3 °C on Mt. Yulong, when Baishui glacier has been experiencing extensive ablation.

All samples were collected in 500 ml Whirlpak polyethylene bags (BFL-3070, LABPLAS, Canada) and kept frozen until they could be rapidly melted. Extreme care and stringent sampling protocols (i.e. the authors wore undefiled masks, gloves) were used at all times during sampling campaign. The polyethylene bottles used to load meltwater samples and small shovels were pre-cleaned with ultrapure water and disinfected in the laboratory at the State Key Laboratory of Cryospheric Science in Lanzhou city.

Every melted surface aged-snow and granular ice samples, meltwater sample (including snow meltwater, glacial stream water) were filtered using disposable needles-type filters (MN Chromafil^R, PA-20, 0.20 μm, California, USA) before measuring WSOC concentrations. The WSOC concentrations were determined using a TOC-L analyzer (Shimadzu Corp, Kyoto, Japan) (Stubbins and Dittmar, 2012). Approximately, a volume of 8 ml filtered sample each time was used to detect WSOC concentrations. The detection limit of the analyzer was 0.015 mg L⁻¹, and the average WSOC concentration of the blanks was 0.081 ± 0.017 mg L⁻¹, indicating that contamination can be neglected during the pre-treatment and analysis processing of these samples. Specific method for the determination of WSOC has been reported in Niu et al. (2017b).

Various snow and granular ice samples and meltwater samples were also filtered using pre-combusted (at 550 °C for 6 hours) quartz fiber filter (47 mm in diameter, Whatman™, Manchester, UK) in the clean laboratory. After filtering, the quartz filters were wrapped with aluminum foil and were kept cold and stored. Moreover, the stored filters were dried in an oven for 6 hours before performing IPC analysis. The IPC collected on filtered quartz fiber filters was taken as samples not treated by NH₄H₂PO₄ solution since there was no obvious difference for the two scenarios (Kuchiki et al., 2015; Li et al., 2016a). IPCs (BC and OC) on the filters were analyzed using a Desert Research Institute (DRI) Model 2001 thermal/optical reflectance carbon analyzer (Atmoslytic Inc., Calabasas, California). A temperature peak (550 °C) was designed to decrease measuring time. The measurement of carbon fractions is based on Improve A thermal/optical reflectance protocol. EC is usually used synonymously with BC (Xu et al., 2009; Cheng et al., 2011), we use EC as a proxy for BC. The applied heating conditions permitted the separation of BC portions in O₂ (2%) and helium (98%) atmosphere and OC portions in a helium atmosphere (Wang et al., 2015; Niu et al., 2017a, c). The residence time of each heating step was defined by the flattening of the carbon signal. The results determined from our procedure are consistent with previous protocols. Detailed statement on the IPC’s analytical method was reported in Li et al. (2016a) and Niu et al. (2017a, c).

Moreover, the light absorption spectra of WSOC samples were determined by employing an ultraviolet-visible absorption spectrophotometer (SpectraMax M5, California, USA), scanning wavelengths from 200 to 800 nm at an interval of 5 nm (Yan et al., 2015, 2016). By measuring the absorbance, we assumed that the scattering contribution to spectral extinction is low (Kirillova et al., 2014b).

Water-soluble inorganic ions (NO₃ ^-, Cl^-, SO₄ ^2-, K⁺, NH₄ ⁺, Ca²⁺, Na⁺, Mg²⁺) were determined using two ion chromatography (ICS-2500 and Dionex 600, Sunnyvale, CA). The IC systems Dionex 600 and ICS-2500 were used to determine cations and anions, respectively. The instruments were calibrated using standard cation and anion solutions (Dionex, Sunnyvale, California, USA). The detection limits for ion chromatography were 0.01–0.05μeq L⁻¹. The IC analysis results were evaluated in terms of the reproducibility of the peak heights, peak retention times, and linearity of each calibration curve (Xu et al., 2015).

Methods for light absorption of WSOC

3.2.

The mass absorption cross-section (MAC) of WSOC (or MAC_wsoc ) (unit: m² g⁻¹) was computed according to the Lambert–Beer Law (Kirillova et al., 2014a, b; Yan et al., 2016).

Where I and I₀ are the light intensity of incident light and transmitted light, respectively. C is the concentration of WSOC and L is the light path length (1 cm, for the quartz cuvettes used in measurements) and A is the absorbance measured by the spectrophotometer. The MAC of WSOC is presented for wavelength at 365 nm, this wavelength was chosen to avoid interferences from inorganic compounds (e.g. nitrate) and to enable comparison with previous results (Hecobian et al., 2010; Cheng et al., 2011; Kirillova et al., 2014a,b).

Absorption Ångström Exponent (AAE) was investigated to evaluate the wavelength (λ) dependence of the light absorption, of light absorbing WSOC, the AAE was deduced from the following relation:

AAEs were fitted within the wavelengths ranged from 330 to 400 nm, so as to avoid interference from other light absorbing inorganic matter (e.g. nitrate) (Cheng et al., 2011; Bosch et al., 2014; Kirillova et al., 2014b). This simple estimate is based on the assumption that AAE for WSOC measured at 330–400 nm represents the whole ultraviolet spectrum range, and no influences of scattering are included.

Light absorption of WSOC relative to BC (p)

3.3.

The amount of light absorption of WSOC relative to BC is assessed employing a simple model. BC is the most efficient light absorbing carbonaceous matter in the atmosphere (Ramanathan and Carmichael, 2008; Bond et al., 2013).

The fraction of solar energy absorbed by WSOC relative to BC (p) is evaluated as follows:

Where C indicates the concentration of WSOC, BC_m is the measured BC concentrations (in ng g⁻¹) in snow and ice of glaciers; MAC_BC indicates the mass absorption coefficient of BC. There are several values for MAC_BC reported in literatures (Bond and Bergstrom, 2006; Schwarz et al., 2013; Niu et al., 2017a). For our estimation, we used the MAC_BC 7.5 m² g⁻¹ to calculate BC absorption.

The chemical speciation of WSOC in snow and ice

4.1.

Statistical analyses were conducted to determine the general concentration characteristics of WSOC in snow and ice of glacier on Mt. Yulong. Fig. 2 presents the mean WSOC concentrations and the corresponding standard deviations of the five groups of snow, ice, and meltwater samples. Among the mean WSOC concentrations in the five groups of samples, glacial stream water had the lowest mean WSOC concentrations of 0.39 ± 0.08 mg L⁻¹, whereas snow meltwater had the highest average WSOC concentrations at 0.83 ± 0.18 mg L⁻¹. Higher mean WSOC concentrations in snow meltwater could be due to the deposition and accumulation of organic matter from the surface meltwater of the snow and granular ice on glaciers, and the elution (or percolation) effects played a notable role in contributing the higher WSOC contents in snow meltwater (Niu et al., 2017b). In addition, many types of organic compounds have been found to exist at high levels in the Arctic snowpack and to actively participate in snow photochemistry (Anderson et al., 2008). The direct photolysis in the upper layers of snowpack is probably responsible for the oxidation of organic compounds that have been deposited into the snow and glacier (Sumner and Shepson, 1999; Grannas et al., 2004). The photochemical activity of organic compounds somewhat constitutes an artifact in the budget of organic matter since part of it might have been photo-dissociated and re-emitted as gas phase in the atmosphere.

Fig. 2.

Compared with other glacial regions in the world, our average values (0.61 ± 0.21 mg L⁻¹) are comparable with the WSOC concentrations in snow of the Zhadang glacier (0.37 ± 0.28 mg L⁻¹) (Li et al., 2016b) on the TP and the Mendenhall glacier in Alaska (0.29 ± 0.02 mg L⁻¹) (Stubbins and Dittmar 2012). Our findings are also comparable to the results found in LHG glacier (0.42 ± 0.29 mg L⁻¹) (Yan et al., 2016) and the Antarctic ice sheet (0.46 ± 0.12 mg L⁻¹) (Hood et al., 2015) (Table 2). In addition, the IPC concentrations in snow (0.94 ± 0.09) on Mt. Yulong were, taking into the large SD at the two latter sites, comparable to that found in the snow on Zhadang (0.64 ± 0.45) and LHG (1.01 ± 0.47) glaciers (Li et al., 2016a), which was due to the large fraction of carbonaceous matter and dust deposition in these areas, primarily transported from South Asia (Lüthi et al., 2014; Cong et al., 2015a, b; Li et al., 2016c) and local motor vehicle emissions (Niu et al., 2017b). It was also reported that high WSOC concentrations in ice were mainly due to contributions from the living biosphere (approximately 85%) (Legrand et al., 2013b), and 15% of WSOC was related to the elevated fossil fuel emissions. Based on the WSOC and IPC concentrations, the calculated WSOC/TC ratios were also presented in Table 2. Results indicated that WSOC accounted for a large fraction of TC in snow and ice with an average value of 26.9%, which was almost comparable with the results found in the TP, e.g. LHG (29%), Zhadang (37%) (Li et al., 2016a). Yet, in ice core drilled from glaciers in Europe, WSOC accounted for absolutely high percentage of the TC, with the average WSOC/TC of 86% (Legrand et al., 2013b) and 57% (Pio et al., 2007), this was primarily contributed by the abundant biogenic volatile organic compounds (Legrand et al., 2013b; Kirillova et al., 2013). Moreover, in very remote places, i.e. Alpine, Greenland, and Antarctic sites, insoluble OC (WinOC) is not expected to dominate the WSOC budget and the WinOC/WSOC ratio was close to 10% at summit (Hagler et al., 2007; Legrand et al., 2013b; Niu et al., 2017b).

Depletion and leaching of WSOC in the snowpack

4.2.

It is known that organic compounds deposited on glaciers will experience a series of depletion processes and photochemical reactions (Grannas et al., 2004; Antony et al., 2011; Niu et al., 2017b). Depletion process of WSOC in surface aged snow was investigated based on successive in-situ observations, the results indicated that WSOC concentrations were dramatically decreased with the time extension during the entire monsoon season (Fig. 3), although with sporadic fluctuations. The decreased trend of WSOC concentrations in surface aged snow was due to extensive glacial (or snow) melting and scavenging effects by meltwater, similar results were also found in our previous study (Niu et al., 2017b). In addition, photochemical reaction and photolysis of WSOC (Grannas et al., 2004), air-snow exchange processes, leaching processes, or washout by the snow meltwater, or WSOC in surface snow was re-emitted in the atmosphere when snow is aging, all of those factors might result in the decreasing trend of WSOC concentrations in surface aged snow on glaciers during the entire monsoon season. Moreover, since a large portion of glacier-derived WSOC has proven to be highly bioavailable (Hood et al., 2009; Singer et al., 2012; Legrand et al., 2013a; Spencer et al., 2014), WSOC content was exponentially decreased during the period of in situ observations (biodegradation experiment) (Yan et al., 2016).

Fig. 3.

To clarify the physico-chemical processes and leaching effect of the WSOC concentrations in snow pits, we analyzed the vertical distribution of WSOC concentrations. The trends of WSOC concentrations in the profiles of snow pits were generally consistent (Fig. 4), but the concentration curve of the second snow pit (excavated on 10 June 2014) was obviously smoothed. Particularly, at the time of excavating the second snow pit, the snowpack was thinned considerably (approximately 0.3 m) during the past several days. Therefore, snow quick-melt effects (Niu et al., 2017b) and the leaching processes in snow pit substantially affected the vertical distribution of WSOC concentrations, this point is somewhat consistent with the trend of WSOC in surface aged snow (Fig. 3). Furthermore, Hagler et al. (2007) found the concentrations of particulate organic carbon in surface snow to be much greater than those in shallow snow pits, suggesting that the snowpack loses a significant fraction of the carbon mass originally deposited.

Fig. 4.

Light absorption property of WSOC

4.3.

MAC values of WSOC (MAC_wsoc ) and the spectral dependence

4.3.1.

The mass absorption cross section at 365 nm for WSOC is an important parameter for the radiative forcing calculation. The MAC values of WSOC calculated at 365 nm by Equation (1) was 5.23 ± 0.34 m² g⁻¹ in snow pit, 8.59 ± 0.27 m² g⁻¹ in granular ice, and 5.10 ± 0.41 m² g⁻¹ in surface aged snow on Baishui glacier (Table 3), with an average value of 6.31 ± 0.34 m² g⁻¹ on Mt. Yulong. The results indicated that the high MAC_wsoc values for low WSOC concentrations are located at the surface samples of snow pits (0–20 cm), which have a considerable effect on the energy budget of the snow pack. Granular ice had the highest MAC_wsoc values among the three types of snow and ice samples, higher MAC_wsoc values in granular ice was due to direct exposure to solar radiation (Yan et al., 2016). MAC_wsoc values of snow and ice on Mt. Yulong glacier were higher than those from the glaciers in other regions, such as Barrow, Alaska (Voisin et al., 2012) and LHG glacier in the TP (Yan et al., 2016) (Table 3). The MAC values of WSOC from different sources vary widely. Typically, the MAC of WSOC at the wavelength of 365 nm derived from motor vehicle emissions can reach 5 m² g⁻¹ (Kirchstetter et al., 2004). In addition, it was reported that mineral-dust originated WSOC had high MAC_wsoc values (Yan et al., 2016).

Table 3.

Mass absorption cross section (MAC) of WSOC and absorption Ångström exponent (AAE_330–400) of snow and ice from Mt. Yulong and of carbonaceous aerosols from other regions. λ represents the wavelength at which MAC of WSOC was measured.

Site/sourceSample typeMAC (m² g⁻¹)AAE_330–400λ (MAC)Mt. Yulong (This study)Snow pit5.23 ± 0.340.63 ± 0.22365Granular ice8.59 ± 0.270.73 ± 0.21365Surface aged snow5.10 ± 0.410.77 ± 0.46365LHG (Yan et al., 2016)Snow1.4 ± 0.45.0 ± 5.9365Ice1.3 ± 0.73.4 ± 2.736Barrow, Alaska (Voisin et al., 2012)Snowpack2.6 ± 1.16.1 ± 0.27250Beijing, China (Cheng et al., 2011)PM_2.51.3 ± 0.767.2 ± 0.28365New Delhi (Kirillova et al., 2014b)Winter aerosols1.1–2.73.1–9.3365Berkeley, California (Kirchstetter et al., 2004)Vehicle emissions Aerosols54.8350Everest (Li et al., 2016b)Carbonaceous Aerosols1.18 ± 0.644.64 ± 1.15365

Spectral dependence of MAC_wsoc was systematically determined in this study, the results suggested that there exists distinct spectral dependence of MAC_wsoc with the wavelength ranged from 260 to 700 nm (Fig. 5), all MAC_wsoc in granular ice, snow pit, surface aged snow were considerably decreased within the wavelength range. The variation trends of light absorbing property of WSOC in the three types of snow and ice samples were consistent with each other, but different with the results found in LHG glacier (Yan et al., 2016). The MAC spectrum is very different at LHG glacier: absorption of WSOC is weaker, and the inflection of the curve is less pronounced. The three measurements at Mt. Yulong show similar patterns and only the magnitude of the absorption is different, that could be due to the concentration. The variations of MAC_wsoc with wavelengths at Mt. Yulong have similar features at 450 nm, 600 nm, and 650 nm, which were not visible at LHG (Fig. 5). The stronger spectral dependence was the result of enhanced light absorption at wavelengths shorter than 600 nm (Kirchstetter et al., 2004). Moreover, the difference in light absorbing property (particularly for wavelengths larger than 400 nm) among snow and ice samples from Baishui and LHG glaciers indicated their different sources. Intensive exhaust emissions from tourism vehicles were the main local sources of atmospheric pollutants in the Mt. Yulong region and Baishui glacier. Biomass-burning emissions released from South Asia could penetrate into the inland of the TP (Xu et al., 2009; Cong et al., 2015a, b). Since LGH glacier is located in the Qilian mountain and is close to the desert areas in northern China, the light absorption properties of WSOC in snow of glaciers varied considerably in different sites.

Fig. 5.

Principal component analysis (PCA) of chemical data is performed to tag the potential sources of WSOC by investigating the relationship between WSOC and water-soluble inorganic ions. Factor loading or PCA decomposition provides objective representations of multivariate data through the analysis of the covariance structure of its variants (e.g. Meeker et al., 1995; Niu et al., 2014). Generally, it is deemed that Ca²⁺ and Mg²⁺ in glacial regions are mainly from crustal sources (Yao et al., 1995; Kang et al., 2010); K⁺ is emitted from biomass burning such as heating and cooking (use wood and crop straw) of local residents (Legrand et al., 2013a; Niu et al., 2014, 2016); SO₄ ²⁻ and NO₃ ⁻ are primarily generated from fossil fuel combustion (Li et al., 2016a); and NH₄ ⁺ is derived mainly from biogenic emissions (Pio et al., 2007; Kirillova et al., 2014a). Three factors explained over 80% of the total variance (Table 4). The first factor (F1) largely loaded with major ions (NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺, and K⁺) can be attributed to crustal sources, agricultural activities (ammonium salt NH₄NO₃, from fertilizer use) and biomass burning (K⁺, 97%). The second factor (F2) was heavily loading with WSOC (89%) and Ca²⁺ (66%), as previously discussed/reported, the TP glaciers are severely affected by dust storms during the non-monsoon period (Thompson et al., 2000). In addition, the third factor (F3) was loading with IPC (64%) and Mg²⁺ (78%), reflecting IPC is likely associated with continental contribution.

Table 4.

Results of principle component analysis (PCA) carried out on WSOC and major water-soluble inorganic ions in snow pits samples: factor loading (F1, F2, F3), eigenvalues and percentual explained variance. The numbers in the table represent the percent of variance associated with each major ion. Negative values indicate an inverse relationship.

F1F2F3WSOC0.190.89−0.02IPC0.26−0.390.64K⁺0.97−0.09−0.18NH₄⁺0.93−0.24−0.06NO₃⁻0.97−0.12−0.07SO₄²⁻0.93−0.23−0.12Na⁺0.360.16−0.49Mg²⁺0.400.210.78Ca²⁺0.660.660.17Eigenvalues4.441.691.33% variance49.317.7214.73

Relative light absorbance of WSOC and BC

4.4.

Based on our extensively determined WSOC and BC concentrations, and MAC_wsoc values in snow and ice of Baishui glacier, combined with Equation (3), the associated light absorbance of WSOC relative to BC (p) is 3.99 ± 0.76. In other words, the relative absorption of WSOC to BC is almost 4 times higher, on an average. This result indicated that WSOC is more absorptive than BC (in the wavelength range of 330–400 nm) in surface aged snow of glaciers (Fig. 6). Generally, the AAE of BC is low, and often taken to be unity in models since they assume a flat absorption spectrum for BC. Strong light absorbance (in the wavelength range of 330–400 nm) of WSOC is primarily due to its higher concentrations in snow of glaciers. However, approximately 25% of light absorption of WSOC less than that of BC is due to relatively low WSOC concentrations (within 0.5 mg L⁻¹) (Fig. 6).

Fig. 6.

Moreover, there was uncertainty of relative absorbance between WSOC and BC. Actually, several factors can cause the large uncertainty of WSOC and BC absorbance, which includes (1) the exact MAC values for BC; (2) the mixture conditions among BC and other IPC components in snow and ice of glaciers (external or internal mixture); (3) the enrichment forms of BC at the glacier’s surface (Kaspari et al., 2014; Niu et al., 2017a). More work is needed to diagnose the relative absorption of WSOC and BC in snow and ice of glaciers, which is crucial to quantitatively evaluate the solar energy absorbance of WSOC and IPC in snow of glaciers.

Carbonaceous compound flux and glacial retreat

4.5.

The carbonaceous compound flux in glaciers due to precipitation and melting plays an important role in carbon cycle of the earth. The fluxes of carbonaceous compounds of a specific glacier can be determined by the concentrations of carbonaceous matter in glacier and precipitation amount. The annual WSOC flux in Baishui glacier has been estimated based on the averaged WSOC concentrations (Table 2) in snow and granular ice of Baishui glacier, and the precipitation amounts in Mt. Yulong, which were recorded by automatic weather stations installed in the glacier areas (at an elevation of 4800 m asl).

The flux of WSOC in Baishui glacier was 0.99 gC m⁻²yr⁻¹, comparable to the result (0.11 ± 0.49 gC m⁻²yr⁻¹) proposed in Li et al. (2016a) for six glaciers in the TP, but lower than the fluxes of typical polluted areas in North and South Korea (1.9 gC m⁻²yr⁻¹) (Yan and Kim, 2012; Park et al., 2015). Similar method was used to determine the flux of IPC in Baishui glacier, and the flux of IPC was 4.77 gC m⁻²yr⁻¹, indicating a large fraction of IPC, as well as WSOC have contributed to dynamic carbon cycle in glaciers.

Total WSOC storage in the Baishui glacier was estimated to be 1.5 tone C (1 tone =10⁶ g) based on the average WSOC flux and the Baishui glacier size (1.52 km²) (Li et al., 2010) of Mt. Yulong, while the total IPC storage in the Baishui glacier has been estimated to be 7.25 tone C. However, these estimations were somewhat overestimated because during the recent years (Kang et al., 2001, 2015; Du et al., 2013, 2015), dramatic glacial melting in this region have been observed.

In addition to the impacts of the rising temperature (You et al., 2010; Yasunari et al., 2010) and decreasing precipitation trend (low glacial accumulation) (Yao et al., 2012b), large number of studies demonstrated that deposition and enrichment of the LAM onto glaciers can substantially accelerate glacial melting (Xu et al., 2009; Doherty et al., 2013; Ming et al., 2013; Qu et al., 2014; Dong et al., 2017; Li et al., 2017; Niu et al., 2017b) and triggers an earlier spring melt (Flanner et al., 2007, 2009; Niu et al., 2017b). Particularly, if the impurities were exposed at the glacier’s surface during the melting season, glacial melting would be further accelerated. This could occur if snow melting resulted in the exposure of ILAIs at the glacier’s surface and/or if the melting season was reinforced and expanded due to a warming climate (Fujita 2007; Fujita et al., 2011). It was observed that for elevations lower than 4800 m asl on Baishui glacier, there was no snow cover area on glacier's surface during the summer, so those ILAIs remained exposed throughout the summer (Niu et al., 2017b). Therefore, the melting of Baishui glacier is definitely accelerated by the LAM deposited onto snow and glacier’s surface. Furthermore, based on the continuous field hydrological observations, the average velocity of Baishui River in summer season was 0.41 m s⁻¹ in 2011, and it was increased to 0.52 m s⁻¹ during the entire monsoon season in 2014, and the annual runoff was approximately 4.22 × 10⁶ m³, glacial melting is further reinforced by the LAM in glaciers (Li et al., 2017; Niu et al., 2017a). In a word, Baishui glacier as a typical temperate glacier in the southeast TP, can be regarded as a fraction of carbon source on the earth under the scenario of a warming climate.