A regional climate model (RegCM4.3.4) coupled with an aerosol–snow/ice feedback module was used to simulate the deposition of anthropogenic light-absorbing impurities in snow/ice and the potential radiative feedback of black carbon (BC) on temperature and snow cover over the Tibetan Plateau (TP) in 1990–2009. Two experiments driven by ERA-interim reanalysis were performed, i.e., with and without aerosol–snow/ice feedback. Results indicated that the total deposition BC and organic matter (OM) in snow/ice in the monsoon season (May–September) were much more than non-monsoon season (the remainder of the year). The great BC and OM deposition were simulated along the margin of the TP in the non-monsoon season, and the higher deposition values also occurred in the western TP than the other regions during the monsoon period. BC-in-snow/ice decreased surface albedo and caused positive surface radiative forcing (SRF) (3.0–4.5 W m-2 ) over the western TP in the monsoon season. The maximum SRF (5–6 W m-2 ) simulated in the Himalayas and southeastern TP in the non-monsoon season. The surface temperature increased by 0.1–1.5 °C and snow water equivalent decreased by 5–25 mm over the TP, which showed similar spatial distributions with the variations of SRF in each season. This study provided a useful tool to investigate the mechanisms involved in the effect of aerosols on climate change and the water cycle in the cryospheric environment of the TP.


Black carbon ; Tibetan Plateau ; Aerosol–snow/ice radiative effects ; Regional climate model

1. Introduction

Light-absorbing impurities in snow/ice are derived from the wet and dry deposition of light-absorbing particles in the atmosphere. A few light-absorbing impurities in snow/ice can reduce ground albedo and increase the absorption efficiency of solar radiation, resulting in the melting of snow/ice at the surface. The main components of light-absorbing impurities in snow/ice are mineral dusts, black carbon (BC), brown carbon, and organic matter (OM). Mineral dusts and BC have strong absorption in the visible band, whereas brown carbon and OM absorb in the ultraviolet band. In general, mineral dusts originate from natural sources, whereas BC and brown carbon are mainly emitted from the incomplete combustion of fossil fuels and biomass during anthropogenic activities.

The Tibetan Plateau (TP), which has an abundance of snow and ice cover, is referred to as the water tower of Asia. Melting snow/ice makes a large contribution to regional hydrological resources and has direct impacts on local society and economic development. Recent studies have found that light-absorbing impurities, which may accelerate snow/ice melting, are considered as a key factor in cryospheric changes (Flanner et al., 2009 ; Doherty et al., 2010 ; Xu et al., 2009 ; Wang et al., 2013  ;  Dumont et al., 2014 ). However, there have been few assessments of the radiative effects of light-absorbing impurities on snow/ice cover over the TP (Ming et al., 2009a  ;  Qu et al., 2014 ). Flanner et al. (2007) coupled a snow radiative model with a global climate model (GCM) and estimated the anthropogenic radiative forcing by the deposition of BC in snow averaged 1.5 W m−2 over the TP. Qian et al. (2011) also found the effects of BC and mineral dusts on changes in surface radiative forcing with the range of 5–25 W m−2 and increased the temperature by 1.0 °C on average and reduced the snowpack in spring over the TP through a GCM. However, the coarse resolution of GCMs cannot capture the spatial distribution of snow cover against observations. In this study, we used a regional climate model, which performed well in climatology over the TP (Ji and Kang, 2013 ), to simulate the concentrations and deposition of anthropogenic light-absorbing impurities (BC and OM) in snow/ice and to investigate the potential radiative effects of BC on snow/ice melting.

2. Model, data, and details of the experiments

The Regional Climate Model version 4.3.4 (RegCM4.3.4) updated from RegCM4 (Giorgi et al., 2012 ) was used in this study. RegCM series models follow a hydrostatic equilibrium transplanted from the dynamic core in mesoscale model MM5 (Grell et al., 1994 ). The radiative transfer module is taken from the U.S. National Center for Atmospheric Research (NCAR) Community Climate Model 3.0 (CCM3) (Kiehl, 1996 ). In this study, we used the Grell (1993) convective precipitation scheme due to its good performance over these regions (Ji et al., 2015 ). The land surface module was coupled with the Community Land Model version 4.5 (CLM4.5) (Oleson et al., 2010 ). The snow module in CLM4.5 was modified by coupling it with the Snow and Ice Aerosol Radiation package (SNICAR), which can reproduce the effect of light-absorbing impurities (e.g., BC and mineral dust) on snow albedo (Flanner et al., 2007 ). In this study, the top of snow layer with the maximum thickness of 0.02 m was applied.

The initial conditions and lateral boundary conditions (ICBC) were derived from European Centre for Medium-Range Weather Forecasts (ECMWF) re-analysis ERA-interim data at 1.5° × 1.5° horizontal resolution (Dee et al., 2011 ). Sea surface temperatures were obtained from the National Oceanic and Atmospheric Administration (NOAA; Reynolds et al., 2002 ). The land cover data were obtained from the moderate-resolution imaging spectroradiometer (MODIS) (Lawrence and Chase, 2007 ). A BC and organic carbon (OC) emissions inventory (Junker and Liousse, 2008  ;  Liousse et al., 1996 ) was interpolated to the model grid via a bilinear method. Emissions of OC were multiplied by 1.4 to represent OM. This OM/OC ratio is appropriate for representing fossil fuel-derived OC emissions (Russell, 2003 ). The effect of OM on snow is not considered in the current model version; therefore, only the BC in snow was investigated in this study. The model resolution was 50 km, and the domain was centered at 27°N, 85°E, with 90 and 95 grids in the north–south and west–east directions, respectively. Two simulations were performed, one with and one without aerosol–snow radiative feedback, for the period of 1989–2009 (the first year was the model spin-up). According to Wu and Zhang (1998) , we defined the monsoon season as May to September, and the non-monsoon season to be the remainder of the year.

3. Results

A previous study (Ji et al., 2015 ) validated RegCM4.3 performance and confirmed that the model could reproduce the spatial distributions of atmospheric circulation, temperature, and precipitation over the TP. The model also captured the aerosol concentration and optical depth well compared with observations. Therefore, we did not repeat the model evaluation of the climatology in this study.

Fig. 1 shows BC and OM deposition on top of the snow layer based on simulations of the monsoon and non-monsoon seasons. The deposition of OM was much greater than that of BC, consistent with the difference in their atmospheric concentrations. OC emissions were 2–3 times greater than BC emissions in Asia (Ohara et al., 2007  ;  Ji et al., 2015 ). In the monsoon season, BC (Fig. 1 a) and OM (Fig. 1 c) depositions were in the range of 20–120 and 60–200 μg m−2 , respectively, over the western TP and Himalayas. Aerosol deposition was very low in the inland regions of the TP. In the non-monsoon season, there were high levels of BC deposition along the margin of the Third Pole, and low levels in the inland regions (Fig. 1 b). These topographic patterns were probably associated with a terrain blocking effect on the particles in the atmosphere. In the Himalayas, Hindu-Kush, Tianshan, Kunlun, and Qilian Mountains, BC and OM depositions (Fig. 1 d) were in the range of 40–70 and 120–200 μg m-2 , respectively. In terms of seasonal differences, both BC and OM depositions were greater during the monsoon season than the non-monsoon season over the western TP. However, in the Tianshan, Kunlun, Qilian, and Himalayas, the maximum values occurred in the non-monsoon season.

Full-size image (179 K)

Fig. 1.

Seasonal mean black carbon (BC) and organic matter (OM) deposition in snow (unit: μg m−2 ) and season mean changes of surface albedo in monsoon season (a, c, e) and non-monsoon season (b, d, f) during 1990–2009.

We summarized the mass concentrations of BC and OC in snow from 11 sites (Table 1 ) for comparison with model-simulated carbonaceous impurities on top of the snow layer. Muztagh Ata in the eastern Pamir Mountains experiences prevailing westerlies throughout the year. Meikuang Glacier is located in the eastern Kunlun Mountains, on the southern margin of the Qaidam Basin, Laohugou No. 12 Glacier, and Qiyi Glacier are located in the western Qilian Mountains, northeast of the TP. Dongkemadi Glacier is in the Tanggula Mountains in the central TP, and Lanong Glacier and Zhadang Glacier are situated in the eastern Nyainqentanglha Mountains. Qiangyong Glacier is located in the southern TP, Namunani Glacier is in the western Himalayas, and Kangwuer Glacier on Mount Shishapangma and East Rongbuker Glacier on Mount Qomolangma are located in the central Himalayas, where the monsoon climate dominates in summer. At Muztagh Ata, Qiyi, Qiangyong, Namunani, and Kangwure Glaciers, measured BC and OC concentrations were obtained from snow pits and surface snow using a two-step heating–gas chromatography method (Xu et al., 2006 ). At Meikuang, Laohugou, Qiyi (Ming et al., 2009a ), Lanong, Zhadang, and East Rongbuk, the BC mass concentrations were measured in samples from snow pits using a coulometric titration-based instrument (Ming et al., 2009a ). Another set of surface snow samples from Zhadang Glacier were measured using a thermal–optical method (Qu et al., 2014 ).

Table 1. Sampled (BC-OBS, OC-OBS) and simulated (BC-RCM, OC-RCM) black carbon (BC) and organic carbon (OC) surface concentration (unit: μg kg−1 ).
Region Site Location Elevation a. s. l. (m) Time BC-OBS (μg kg−1 ) BC-RCM (μg kg−1 ) OC-OBS (μg kg−1 ) OC-RCM (μg kg−1 ) OC/BC (obs) OC/BC (RCM) References
Pamir Muztagh Ata 38.28°N, 75.02°E 6350 2001 52.1 17.6 113.2 67.9 5.4 3.9 Xu et al., 2006
Kunlun Meikuang 35.67°N, 94.18°E 5200 11/2005 81.0 61.9 n.a. 171.8 n.a. 2.8 Ming et al., 2009b
Qilian Lahugou 39.43°N, 96.56°E 5045 10/2005 35.0 56.3 n.a. 142.5 n.a. 2.5 Ming et al., 2009a
Qilian Qiyi 39.23°N, 97.06°E 4850 6−8/2001 52.6 10.9 195.5 27.2 3.7 2.5 Xu et al., 2006
7/2005 22.0 n.a. n.a. Ming et al., 2009a
Tanggula Dongkemadi 33.10°N, 92.08°E 5600 2005 36.0 7.9 n.a. 24.6 n.a. 3.1
Ming et al., 2009a
Nyainqentanglha Lanong 30.42°N, 90.57°E 5850 6/2005 67.0 17.4 n.a. 59.6 n.a. 3.4 Ming et al., 2009a
Nyainqentanglha Zhadang 30.47°N, 90.63°E 5800 7/2006 114.0 17.4 n.a. 59.6 n.a. 3.4 Ming et al., 2009a
7−8/2012 140.0 n.a. n.a. Qu et al., 2014
South TP Qiangyong 28.83°N, 90.25°E 5400 2001 43.1 31.8 117.3 109.9 2.7 3.5 Xu et al., 2006
Himalayas Namunani 30.45°N, 81.27°E 5900 2004 4.3 19.1 51.2 69.1 11.9 3.6 Xu et al., 2006
Himalayas Kangwure 28.47°N, 85.82°E 6000 2001 21.8 14.3 161.1 51.9 7.4 3.6 Xu et al., 2006
Himalayas East Rongbuk 28.02°N, 86.96°E 6500 10/2004 18.0 13.8 n.a. 50.1 n.a. 3.6 Ming et al., 2009a
9/2006 9.0 n.a. n.a.

The results showed that simulated BC and OC concentrations in snow were of the same magnitude as in measurements at most sites over the TP (Table 1 ) though there were some differences between observations and simulations. For example, the underestimate of BC concentration in Muztagh Ata and the Zhadang glacier, and a little of overestimated BC in the Namunani located in the western Himalayas. The available OC concentrations at the five sites were greater than those of BC, which was also reproduced in the simulations. The OC/BC ratio can be used to approximately indicate the source of particles. As for the TP, the mean OC/BC in emissions derived from biomass and fossil fuel burning were 6.9 and 2.7 (Streets et al., 2003 ), respectively. When the OC/BC ratio is much higher (lower), it suggests a greater contribution of carbonaceous impurities in snow/ice from biomass (fossil) fuel burning. The largest OC/BC ratios were measured at Namunani and Kangwure, which were considered to be remote areas with few local anthropogenic emissions. In contrast, the low values of the OC/BC ratio at Laohugou and Qiangyong indicated that carbonaceous material from local fossil fuel combustion was probably deposited on the glacier. Compared with measurements, the model produced similar OC/BC ratios in the northeastern (Qiyi) and southern (Qiangyong) TP; however, it underestimated the ratio in the Himalayas (Namunani and Kangwure) and Pamir Mountains (Muztagh Ata). This suggests that the model could reproduce the contributions of anthropogenic impurities in snow/ice better than it could reproduce the regional background. However, it should be noted that there were large uncertainties in the OC/BC ratio due to local effects and chemical processes, and the implications of the OC/BC ratio could not be reliably derived due to the lack of long-term continuous measurements.

The darkening of snow cover by the deposition of BC changed the surface albedo, reducing it by 0.04–0.10 over the western TP and reducing that in the Tianshan and Kunlun Mountains by 0.01–0.04 during the monsoon season (Fig. 1 e). In the non-monsoon season, the surface albedo decreased by 0.04–0.08 along the mountainous regions surrounding the Third Pole (Fig. 1 f), while there was a lesser reduction in surface albedo (range of 0.01–0.04) in the inland regions. The maximum decreases in surface albedo (0.08–0.12) occurred over the western Himalayas during the monsoon season. In the northern and eastern TP, the largest reductions occurred during the non-monsoon season.

In this study, the radiative forcing was defined as an instantaneous change in net radiative fluxes (down minus up) induced by BC-in-snow. The surface radiative forcing (SRF) caused by darkened snow was positive, with values in the range of 0.0–4.5 W m−2 over the TP during the monsoon season (Fig. 2 a). The maximum SRF, with values in the range of 3.0–4.5 W m−2 occurred in the western Himalayas and eastern Pamir Mountains. In the Tienshan, Kunlun, and Qilian Mountains and in the eastern TP, the SRF was in the range of 0–3 W m−2 . During the non-monsoon period (Fig. 2 b), the SRF was larger, with positive values of 5–6 W m−2 in the Himalayas and southeastern TP, whereas in the inland regions of the TP and Tianshan Mountains, the SRF was 1.0–3.5 W m−2 , and it exhibited a topographic pattern consistent with the concentration of carbonaceous particles in snow. Our SRF results were within the range reported in a previous study based on a GCM, which estimated the SRF caused by BC in snow to be in the range of 0.5–8.5 W m−2 (Flanner et al., 2009 ) over the TP in spring. Ming et al. (2013) calculated the area-averaged radiative forcing via BC in snow as 2.9–10.3 W m−2  at 17 sites over the TP.

Full-size image (175 K)

Fig. 2.

Seasonal mean changes in surface radiative forcing (a, unit: W m−2 ), temperature (c, unit: °C) and snow water equivalent (SWE) (e, unit: mm) in the monsoon season during 1990–2009 (b, d, f as a, c, e but for the non-monsoon season).

As a response to SRF, the 2-m temperature increased by 0.1–1.5 °C over the Tianshan, Pamir, and the Himalayas during the monsoon season. Maximum warming, in the range of 1–1.5 °C, occurred over the eastern Pamir Mountains and western Himalayas. However, the changes in temperature in the central and eastern TP were not significant. During the non-monsoon season, temperature also increased due to the darkening of snow over the Third Pole regions. Considerable warming (exceeding 1.5 °C) was evident in the western TP, with a smaller temperature increase (1.0–1.5 °C) occurring in the southeastern TP. Because of the warming effects induced by particles in snow, the snow water equivalent (SWE) decreased by 5–25 mm over the western TP and Himalayas during the monsoon season. The maximum reductions in SWE were 10–25 mm in the western Himalayas. In the Tianshan and Kunlun Mountains, the SWE decreased by 1–5 mm. The decrease in the SWE during the non-monsoon season was greater than that during the monsoon season and covered most regions of the Third Pole. The greatest reductions in the SWE (10–25 mm) also occurred over the Himalayas and western TP.

4. Summary and discussion

Light-absorbing impurities in snow/ice have a large impact on cryospheric changes; however, few studies have focused on their radiative effects over the TP. This study simulated the deposition of anthropogenic light-absorbing impurities in snow/ice and assessed the radiative impact of BC over the TP using RegCM4.3.4–CLM4.5. The results indicated that the model performed reasonably well in terms of the spatial distribution of BC and OM deposition and their surface concentrations. The deposition fluxes of BC and OM were greater in the west than in the other regions of the TP during the monsoon season. High values of BC and OM deposition were also found along the margin of the TP due to topographic blocking in the non-monsoon season. The total deposition of BC and OM at the surface during the non-monsoon season was greater than that during the monsoon season over the TP.

The presence of BC caused a reduction in surface albedo and positive SRF over the TP during both the monsoon and non-monsoon seasons. The radiative forcing from BC in snow was 3.0–4.5 W m−2 in the western TP and Himalayas in the monsoon season. During the non-monsoon season, the maximum radiative forcing (5–6 W m−2 ) occurred in the Himalayas and southeastern TP. These radiative effects had a similar spatial distribution to the BC concentrations in snow during each season. Due to the positive radiative forcing induced by BC in snow, surface temperature increased by 0.1–1.5 °C and SWE decreased by 5–25 mm over the TP, with the largest variations in the Himalayas and western TP. In a previous study, RegCM4.3.4 was validated as a useful tool to simulate mineral dusts over High Mountain Asia (Ji et al., 2016 ). However, there were several challenges in modeling anthropogenic light-absorbing impurities in snow/ice. First, the OM emission inventory was obtained from a constant OC emission flux; however, the approximate OM/OC ratio did not represent biomass burning of OM from OC emissions. As a result, there may be large uncertainties in the OM deposition in this study. In the current model revision, the radiative effect of OM was not included, as some previous studies suggested that the optical effects of OM in snow are very small (Hess et al., 1998  ;  Qian et al., 2011 ). Second, the mineral dusts which were considered as the great components of light absorbing impurities were not involved in current study. Also, the impact of light absorbing impurities on glaciers was not considered in the model. Third, the parameterization of the aerosol–snow feedback in the coupled model could not represent the real situation in the TP in terms of particle sizes and shapes and snow grains due to the limited number of samples and laboratory analyses undertaken. The Atmospheric Pollution and Cryospheric Change (APCC) project will focus on the impact of atmospheric pollution on climatic and environmental changes in the cryosphere, and will build a network to address atmospheric aerosols, snow pit sampling, and glacier monitoring over the TP, Antarctic, and Arctic. The datasets from this program will optimize model parameterization and improve our understanding of the effects of light-absorbing impurities in snow/ice on cryospheric change.


This study is supported by National Nature Science Foundation of China (41301061 ), Chinese Academy of Sciences (KJZD-EW-G03-04 ) and China Meteorological Administration Special Public Welfare Research Fund (GYHY201306019 ).


  1. Dee et al., 2011 D.P. Dee, S.M. Uppala, A.J. Simmons, et al.; The ERA-interim reanalysis: configuration and performance of the data assimilation system; Q. J. R. Meteorol. Soc., 137 (656) (2011), pp. 553–597 http://dx.doi.org/10.1002/qj.828
  2. Doherty et al., 2010 S.J. Doherty, S.G. Warren, T.C. Grenfell, et al.; Light-absorbing impurities in Arctic snow; Atmos. Chem. Phys., 10 (2010), pp. 11647–11680
  3. Dumont et al., 2014 M. Dumont, E. Brun, G. Picard, et al.; Contribution of light-absorbing impurities in snow to Greenlands darkening since 2009; Nat. Geosci., 7 (7) (2014), pp. 509–512
  4. Flanner et al., 2007 M.G. Flanner, C.S. Zender, J.T. Randerson, et al.; Present-day climate forcing and response from black carbon in snow; J. Geophys. Res. Atmos., 112 (2007), p. D1120
  5. Flanner et al., 2009 M.G. Flanner, C.S. Zender, P.G. Hess, et al.; Springtime warming and reduced snow cover from carbonaceous particles; Atmos. Chem. Phys., 9 (7) (2009), pp. 2481–2497
  6. Giorgi et al., 2012 F. Giorgi, E. Coppola, F. Solmon, et al.; RegCM4: model description and preliminary tests over multiple CORDEX domains; Clim. Res., 52 (2012), pp. 7–29
  7. Grell, 1993 G.A. Grell; Prognostic evaluation of assumptions used by cumulus parameterizations; Mon. Weather Rev., 121 (1993), pp. 764–787
  8. Grell et al., 1994 G.A. Grell, J. Dudhia, D.R. Stauer; A Description of the Fifth-generation Penn state/NCAR Mesoscale Model (MM5); Technical report National Center for Atmospheric Research (1994)
  9. Hess et al., 1998 M. Hess, P. Koepke, I. Schult; Optical properties of aerosols and clouds: the software package OPAC; Bull. Am. Meteorol. Soc., 79 (5) (1998), pp. 831–844
  10. Ji and Kang, 2013 Z.M. Ji, S.C. Kang; Double nested dynamical downscaling experiments over the Tibetan Plateau and their projection of climate change under two RCP scenarios; J. Atmos. Sci., 70 (2013), pp. 1278–1290
  11. Ji et al., 2015 Z.M. Ji, S.C. Kang, Z.Y. Cong, et al.; Simulation of carbonaceous aerosols over the third pole and adjacent regions: distribution, transportation, deposition, and climatic effects; Clim. Dyn., 45 (2015), pp. 2831–2846
  12. Ji et al., 2016 Z.M. Ji, S.C. Kang, Q.G. Zhang, et al.; Investigation of mineral aerosols radiative effects over High Mountain Asia in 1990–2009 using a regional climate model; Atmos. Res., 178 (2016), pp. 484–496
  13. Junker and Liousse, 2008 C. Junker, C. Liousse; A global emission inventory of carbonaceous aerosol from historic records of fossil fuel and biofuel consumption for the period 1860–1997; Atmos. Chem. Phys., 8 (2008), pp. 1195–1207 http://dx.doi.org/10.5194/acp-8-1195-2008
  14. Kiehl, 1996 J.T. Kiehl, J.J. Hack, G.B. Bonan, et al.; Description of the NCAR Community Climate Model (CCM3); NCAR Technical Note, NCAR/TN-420 STR  (1996) http://dx.doi.org/10.5065/D6FF3Q99
  15. Lawrence and Chase, 2007 P.J. Lawrence, T.N. Chase; Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0); J. Geophys. Res., 112 (2007), p. G01023
  16. Liousse et al., 1996 C. Liousse, J. Penner, C. Chuang, et al.; A global three-dimensional model study of carbonaceous aerosols; J. Geophys. Res., 101 (D14) (1996), pp. 19411–19432
  17. Ming et al., 2009a J. Ming, C. Xiao, H. Cachier, et al.; Black Carbon (BC) in the snow of glaciers in west China and its potential effects on albedos; Atmos. Res., 92 (1) (2009), pp. 114–123
  18. Ming et al., 2009b J. Ming, C. Xiao, Z. Du, et al.; Black carbon in snow/ice of west China and its radiave forcing; Adv. Clim. Change Res., 5 (6) (2009), pp. 328–335 (in Chinese)
  19. Ming et al., 2013 J. Ming, P. Wang, S. Zhao, et al.; Disturbance of light-absorbing aerosols on the albedo in a winter snowpack of Central Tibet; J. Environ. Sci., 25 (8) (2013), pp. 1601–1607 http://dx.doi.org/10.1016/S1001-0742(12)60220-4
  20. Ohara et al., 2007 T. Ohara, H. Akimoto, J. Kurokawa, et al.; An Asian emission inventory of anthropogenic emission sources for the period 1980–2020; Atmos. Chem. Phys., 7 (2007), pp. 4419–4444
  21. Oleson et al., 2010 K.W. Oleson, D.M. Lawrence, G.B. Bonan, et al.; Technical Description of Version 4.0 of the Community Land Model (CLM); NCAR Technical Note NCAR/TN-478 + STR National Center for Atmospheric Research, Boulder, CO (2010)
  22. Qian et al., 2011 Y. Qian, M.G. Flanner, L.R. Leung, et al.; Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate; Atmos. Chem. Phys., 11 (5) (2011), pp. 1929–1948
  23. Qu et al., 2014 B. Qu, J. Ming, S.C. Kang, et al.; The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities; Atmos. Chem. Phys., 14 (20) (2014), pp. 11117–11128
  24. Reynolds et al., 2002 R.W. Reynolds, N.A. Rayner, T.M. Smith, et al.; An improved in situ and satellite SST analysis for climate; J. Clim., 15 (13) (2002), pp. 1609–1625
  25. Russell, 2003 L.M. Russell; Aerosol organic-mass-to-organic-carbon ratios; Environ. Sci. Technol., 37 (2003), pp. 2982–2987
  26. Streets et al., 2003 D.G. Streets, K.F. Yarber, J.H. Woo, et al.; Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions; Glob. Biogeochem. Cycles, 17 (4) (2003), p. 1099 http://dx.doi.org/10.1029/2003GB002040
  27. Wang et al., 2013 X. Wang, S.J. Doherty, J. Huang; Black carbon and other light-absorbing impurities in snow across Northern China; J. Geophys. Res., 118 (3) (2013), pp. 1471–1492
  28. Wu and Zhang, 1998 G.X. Wu, Y.S. Zhang; Tibetan Plateau forcing and the timing of the monsoon onset over South Asia and the South China Sea; Mon. Weather Rev., 126 (1998), pp. 913–927
  29. Xu et al., 2006 B. Xu, T. Yao, X. Liu, et al.; Elemental and organic carbon measurements with a two-step heating gas chromatography system in snow samples from the Tibetan Plateau; Ann. Glaciol., 43 (1) (2006), pp. 257–263
  30. Xu et al., 2009 B. Xu, J. Cao, J. Hansen, et al.; Black soot and the survival of Tibetan glaciers; Proc. Natl. Acad. Sci., 106 (52) (2009), pp. 22114–22118
Back to Top

Document information

Published on 15/05/17
Submitted on 15/05/17

Licence: Other

Document Score


Views 6
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?