Abstract

This paper gives an overview of the current understanding of the observations of black carbon (BC) in snow and ice, and the estimates of BC deposition and its radiative forcing over the Arctic. Both of the observations and model results show that, in spring, the average BC concentration and the resulting radiative forcing in Russian Arctic > Canadian and Alaskan Arctic > Arctic Ocean and Greenland. The observed BC concentration presented a significant decrease trend from the Arctic coastal regions to the center of Arctic Ocean. In summer, due to the combined effects of BC accumulation and enlarged snow grain size, the averaged radiative forcing per unit area over the Arctic Ocean is larger than that over each sector of the Arctic in spring. However, because summer sea ice is always covered by a large fraction of melt ponds, the role of BC in sea ice albedo evolution during this period is secondary. Multi-model mean results indicate that the annual mean radiative forcing from all sources of BC in snow and ice over the Arctic was ∼0.17 W m−2 . Wet deposition is the dominant removal mechanism in the Arctic, which accounts for more than 90% of the total deposition. In the last part, we discuss the uncertainties in present modeling studies, and suggest potential approaches to reduce the uncertainties.

Keywords

Arctic ; Black carbon ; Snow ; Ice ; Radiative forcing

1. Introduction

The Arctic region widely covered with snow and ice is especially sensitive to climate change. The warming rate in this region is almost twice the global average rate (IPCC, 2013 ). It is believed that the Arctic snow and ice has been undergoing rapid melting in the past decades, reflecting in retreat of sea ice (Comiso et al., 2008 ; Stroeve et al., 2012a ; Stroeve et al., 2012b  ;  Overland and Wang, 2013 ), reductions in snow cover (Brown and Mote, 2009 ; Brown et al., 2010 ; Bulygina et al., 2009  ;  Derksen and Brown, 2012 ) and large area ablation in Greenland ice sheet surface (Tedesco, 2007 ; Mernild et al., 2009  ;  Nghiem et al., 2012 ). Except for the contribution of rising air temperature, the light-absorbing aerosols (called as impurities in snow and ice) are considered to be important factors leading to the rapid melting of Arctic snow and ice (Clarke and Noone, 1985 ; Flanner et al., 2007 ; Dumont et al., 2014  ;  AMAP, 2015 ).

Black carbon (BC) is the most efficient atmospheric particulate species at absorbing visible light (Bond et al., 2013 ). It is emitted directly through incomplete combustion and remains in the atmosphere until it is removed by wet or dry deposition. BC-containing aerosols in the Arctic can perturb the radiation balance in a number of ways. BC aerosols absorb solar radiation and hence warm the atmosphere (Haywood and Shine, 1995 ). BC may also affect the distribution, lifetime, and microphysical properties of clouds through indirect and semi-direct effects (Koch and del Genio, 2010  ;  AMAP, 2015 ). When deposited to snow and ice surfaces, BC can darken the surface, enhance the absorption of radiation (Warren and Wiscombe, 1980 ; Hansen and Nazarenko, 2004  ;  Quinn et al., 2011 ), warm the lower atmosphere and accelerate snow and ice melting (Clarke and Noone, 1985 ). In addition, the BC snow/ice forcing mechanism can trigger the snow albedo feedback through acceleration of snow melt, giving a further warming (Flanner et al., 2009 ).

Here, we provide a brief review to the present field measurements of BC in snow and ice over the Arctic. We also describe the observed and modeled BC deposition, along with the models that can be applied to characterize the BC distribution and its climate impacts over the Arctic. Quantitative descriptions of radiative forcing estimates from previous literature and observations are also provided. In the last part, we discuss the uncertainties existing in present studies and give a conclusion.

2. Field measurements

Present field campaigns in the Arctic have provided snapshot and detailed pictures of the spatial distribution and properties of BC in snow and ice over the Arctic. The earliest campaign was performed over the western Arctic in the 1980s by Clarke and Noone (1985) . Later, snow samples were gathered across the Arctic Ocean for composition analysis including BC during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment (Grenfell et al., 2002 ). The measurements of BC in snow and ice were greatly expanded during 2005–2009 (Doherty et al., 2010 ), which involve the sites in Russian Arctic, Arctic Ocean, the Canadian and Alaskan Arctic and few points in Greenland. Another campaign took BC measurements in Scandinavia and the European Arctic (Forsström et al., 2013 ). In summer 2010, a dozen of snow samples were gathered over the Canada Basin and Arctic Ocean center, enriching the measurements over the Arctic Ocean during summer time (Dou et al., 2012 ). The locations of present BC observations are shown in Fig. 1 .


Fig. 1


Fig. 1.

The BC concentrations (unit: ng g−1 ) in snow and ice in the sampling sites over the Arctic in spring (a) and summer (b). The measurements in spring were conducted during 2007–2009 (Doherty et al., 2010 ), and those in summer were conducted in 2005 (Perovich et al., 2009  ;  Doherty et al., 2010 ), 2008 (Doherty et al., 2010 ), and 2010 (Dou et al., 2012 ), respectively. All of the data used in this figure have been reported in Doherty et al. (2010) and Dou et al. (2012) . The different dot sizes mark various ranges of concentration.

3. Observed BC in snow and ice

The average concentrations in different Arctic regions are shown in Fig. 2 . There exhibits significant spatial variation over each sector in spring, with the higher mean value in the Western Russia (33.7 ng g−1 ), followed by the Eastern Russia (21.4 ng g−1 ) and the Canadian and Alaskan Arctic region (7.9 ng g−1 ), and the lower concentrations in the Arctic Ocean, Western Svalbard, and Greenland. BC concentrations in Arctic snow and ice are generally lower than those in Mid-Low latitudes region due to far away from human activity areas (Flanner et al., 2007  ;  Ming et al., 2009 ). Russia, however, is an exception, where sampling sites are close to human settlements and suffer from more serious local pollution from industrial and biomass emissions (Doherty et al., 2010 ). The mean and median concentrations north of 65°N are 13.6 ng g−1 and 10.7 ng g−1 ranging from 1.4 to 164.6 ng g−1 . Although current data sets cannot completely map the BC concentrations in the Arctic, they provide insight into the range of variability in different Arctic regions.


Fig. 2


Fig. 2.

The BC concentrations over the Arctic Ocean, Western Russia, Eastern Russia, Western Svalbard, Eastern Svalbard and Greenland. Due to few measurements in Alaska, the values are given together with the adjacent Canadian Arctic. The average value in each sector is also shown. The data were measured using filter-based absorption photometry. The data used here are the same with those in Fig. 1 a.

The measurements also show that the BC concentration significantly decreases with latitude (Fig. 3 ). The maximum gradient is found in Russian Arctic with large values in the coastal region and much smaller ones in the sea ice region. Except for a small contribution of emissions from shipping (Corbett et al., 2010 ) and aviation (Whitt et al., 2011 ) in the Arctic Ocean, most of BC originated from the marginal areas of the Arctic Ocean and long-range transport from Mid-Low latitudes (Law and Stohl, 2007 ). In the long-range transport to the Arctic, the removal of BC from the atmosphere occurs through a variety of processes, including wet and dry deposition (Spackman et al., 2010 ), settling of ice crystals, and downward transport in settling wakes (Quinn et al., 2011 ). Therefore, the BC concentration reduces with increasing distance from source regions, leading to a decrease trend from the marginal region to the center of Arctic Ocean.


Fig. 3


Fig. 3.

The variation of BC concentration in snow and ice in spring with latitude. The BC values used here are the same with those in Fig. 1  ;  Fig. 2 .

In summer time, snow samples were collected on sea ice surface. We compared the BC values obtained in summer and spring Arctic Ocean with the observations carried out in other Arctic regions in spring. Results show that the average BC concentration in snow on summer sea ice is much larger than that in spring, even larger than that obtained in Canada and Alaska in spring. Because of the insolubility in water, BC is able to accumulate in snow surface through melt amplification (Xu et al., 2012 ; Doherty et al., 2013  ;  He et al., 2014 ), i.e. BC deposited onto the snow in winter and spring could accumulate in summer snow on sea ice with snow melts, leading to a higher concentration in summer sea ice region.

4. Modeled BC deposition over the Arctic

Current observations are insufficient to characterize the detailed spatial distribution and interannual variabilities of BC in snow and ice over each sector of the Arctic. It is necessary to investigate BC deposition (or BC in snow and ice) using atmospheric chemistry and climate models. Jiao et al. (2014) used AeroCom (Aerosol Comparisons between Observations and Models) models to simulate the spatial distribution of BC deposition over the Arctic. Two dozen models were applied (see detailed description about these models in Jiao et al. (2014) ), half of which are affiliated with the AeroCom phase I intercomparison project (Kinne et al., 2006 ; Schulz et al., 2006 ; Textor et al., 2006 ; Textor et al., 2007  ;  Koffi et al., 2012 ) and the others with the more recent phase II project (Myhre et al., 2013 ; Samset et al., 2013  ;  Stier et al., 2013 ). Results show that the patterns of BC deposition are similar among the models both for phase I and phase II AeroCom project, with larger concentrations over Northern Europe, North America, and East Asia, and smaller concentrations over Greenland and the Arctic Ocean.

There are inter-model variabilities in the magnitude of BC deposition, which may cause discrepancies in the estimation of BC in snow and ice. Jiao et al. (2014) suggested that aerosol transport, evolution, and removal processes are more important contributors than emissions to inter-model variability over the Arctic. Different models may show advantages in different regions. In the study of BC in a particular area, it needs to choose a specific model by comparing with the observations in that region.

Goldenson et al. (2012) estimated the BC deposition fluxes of 1850 and 2000 using the Community Atmosphere Model version 4 (CAM4.0) and pointed out that the deposition in year 2000 is significantly larger than that of 1850, especially over land regions and the Russian sector of the Arctic Ocean. Compared with the background value before industrial revolution, present BC together with dust (for year 2000 level), could cause 1–2 °C of surface warming over large areas of the Arctic Ocean and subArctic seas in autumn and winter and in patches of Northern land in every season (Goldenson et al., 2012 ).

Several studies (Wang et al., 2011 ; Dou et al., 2012 ; Lee et al., 2013 ; Jiao et al., 2014  ;  AMAP, 2015 ) have been done to evaluate current BC deposition (or BC in the Arctic snow) from Chemistry Climate models (CCMs) against measurements by Doherty et al. (2010) . Most of the measurements were made in the top 5 cm of snow, thus model validations were carried out for the surface layer of the model snowpack. It is confirmed that several models performed well in simulating the BC in Arctic snow. Such as, CESM1.1.1 model, CanAM4.2 model and GISS-E2 model, etc. (Dou et al., 2012  ;  AMAP, 2015 ). Here, we show an evaluation of model outputs from a state of the art composition-climate model, GISS-E2PUCCINI-PUCCINI, which applies a global BC emission of year 2000 (Lamarque et al., 2010 ) and are driven with interannually-varying MERRA (NASA Modern Era Reanalysis for Research and Applications) reanalyzes during the period of measurements. BC in the surface layer of the snowpack (on average ∼2 cm thick) on sea ice was simulated with the Los Alamos sea ice model version 4 (CICE4.0) (Hunke and Lipscomb, 2008 ) that includes snow sublimation, melt removal, and snow layer combinations and divisions (Flanner et al., 2007 ) and is dependent on wet and dry aerosol deposition fluxes, precipitation from the GISS-E2PUCCINI-PUCCINI model.

The model-observation comparison showed that GISS-E2PUCCINI-PUCCINI model could well simulate BC concentration in the Arctic snow both for the Arctic Ocean and surrounding land areas (Fig. 4 ). In this model, the wet deposition depends upon solubility and on transport within convective plumes, scavenging within and below updrafts, rainout within both convective and large scale clouds, and washout below precipitating regions (Schmidt et al., 2006  ;  Shindell et al., 2006 ). Dry deposition is calculated using a resistance-in-series model coupled to a global, seasonally varying vegetation dataset (Koch et al., 1999  ;  Shindell et al., 2001 ). Dou et al. (2012) evaluated the relative contribution of dry and wet depositions to BC in Arctic snow with this model, and suggested that wet deposition is the dominant removal mechanism in the Arctic, which accounts for more than 90% of the total deposition (Table 1 ). For the global average, this proportion is slightly low of about 80%.


Fig. 4


Fig. 4.

The comparison of modeled BC concentration in spring snow over the Arctic from GISS model versus the observations from Doherty et al. (2010) .

Table 1. Proportion of wet deposition in the GISS-E2PUCCINI-PUCCINI model from September to May during 2007–2009.
Region Sep Oct Nov Dec Jan Feb Mar Apr May
North of 66°N 0.95 0.94 0.92 0.91 0.90 0.90 0.92 0.93 0.92
Northern Hemisphere 0.78 0.78 0.77 0.76 0.74 0.75 0.76 0.77 0.76
Worldwide 0.81 0.81 0.81 0.81 0.81 0.82 0.82 0.82 0.81

Source: Dou et al. (2012) .

Table 2. The description of models used to estimate radiative forcing due to BC in snow and ice.
Model Resolution (Lon × Lat × Lev) Year of available deposition field Global emission rate (Tg per year) Arctic emission rate (107  kg per year) Arctic deposition rate (107  kg per year) Reference
CCSM4.0 144 × 96 × 26 Generic present-day meteorological conditions 10.62 5.61 21.45 Kirkevåg et al. (2013)
CAM3.1 144 × 96 × 30 2006 7.76 5.64 13.19 Liu et al., 2012  ;  Ghan et al., 2012
GISS-E2 144 × 90 × 40 2004–2008 7.59 7.68 22.05 Koch et al., 2006 ; Koch et al., 2007  ;  Bauer et al., 2007
IMPACT 144 × 91 × 30 Generic present-day meteorological conditions 10.55 3.94 16.13 Yun and Penner (2012)
AeroCom models see Table 1 in Jiao et al. (2014) See Jiao et al. (2014) 6.63–10.62 3.71–6.77 13.19–34.45 Jiao et al. (2014)

5. Radiative forcing due to BC in snow and ice

BC deposition greatly impacts the Arctic surface albedo, which may trigger the snow- and ice-albedo feedback that amplifies surface warming (Hansen and Nazarenko, 2004 ; Jacobson, 2004 ; Flanner et al., 2012 ; He et al., 2014  ;  Liou et al., 2014 ), although the averaged concentration is much smaller than those of other regions in the Northern Hemisphere (Xu et al., 2006 ; Ming et al., 2009  ;  Wang et al., 2013 ). In this section, we summarize recent estimates of radiative forcings due to Arctic BC deposition (Table 2 ). Based on the BC depositions from AeroCom models, Jiao et al. (2014) applied the offline CICE4.0 sea ice model that was driven with the atmospheric reanalysis data, to simulate the vertically resolved BC in snow over the Arctic and its radiative forcing. Multi-model mean result indicated that the radiative forcing from all sources of BC in snow and ice was ∼0.17 W m−2 . Flanner et al. (2009) suggested a radiative forcing of 0.28 W m−2 over the Arctic (60–90°N), with contributions of 0.14 W m−2 from snow and sea ice. Dou et al. (2012) estimated the snow albedo reduction due to BC deposition, and assessed springtime surface radiative forcing using the GISS-physical understanding of composition-climate interactions and impacts (PUCCINI) model, and suggested radiative forcings of 0.7, 1.1, and 1.0 W m−2 for 2007, 2008, and 2009, respectively, showing a substantial interannual variability over the Arctic. Quinn et al. (2011) applied the Community Climate System Model (CCSM4.0) to simulate the impacts of BC on Arctic climate and suggested a radiative forcing of 0.13 W m−2 , with contributions of 0.03 W m−2 from snow and sea ice. Zhou et al. (2012) used BC deposition fluxes from the Integrated Massively Parallel Atmospheric Chemical Transport (IMPACT) model as the input field of an offline sea ice model, and produced an Arctic-mean forcing of 0.11–0.13 W m−2 .

We calculated the decrease in surface albedo based on the observed BC in snow over each sector of the Arctic, using the method in Warren and Wiscombe (1980) . We assumed the snow grain size to be constant, respectively, at 100 μm (McConnell et al., 2007 ) in spring with no significant aging and 1000 μm (represents old melting snow, as in Warren and Wiscombe (1980) in summer. The albedo reduction due to BC in spring shows significant spatial variations, with a high mean level of 0.94% in the Russian Arctic that is much larger than those in other Arctic regions ranging from 0.20% to 0.30% (Fig. 5 ). During summer time, under the common influences of BC accumulation and enlarging snow grain due to melting, the mean decrease in sea-ice albedo can reach 1.5%. In high-concentration areas, this value can reach 3.8%. By integrating the albedo reduction with NCEP downward solar radiation at the surface, we deduce the averaged radiative forcings over each Arctic region in spring with 0.64 W m−2 in the Arctic Ocean, 0.84 W m−2 in the Canadian and Alaskan Arctic, 2.40 W m−2 in Russian Arctic, and 0.54 W m−2 in Svalbard. The value in Greenland has the largest uncertainty because too few measurements were conducted there.


Fig. 5


Fig. 5.

The mean value and standard deviation of BC concentrations over the Arctic Ocean, Canadian and Alaskan Arctic, Russian Arctic and Svalbard (a), and the albedo reduction and radiative forcing due to BC in snow and ice (b). The standard deviation in each sector is shown. The data used to estimate the spring albedo reduction and radiative forcing are the same with Fig. 1 a (Data source: Doherty et al., 2010 ). The data used to estimate the summer radiative forcing are the same with Fig. 1 b (Data source: Dou et al., 2012 ; Perovich et al., 2009  ;  Doherty et al., 2010 ).

In summer Arctic Ocean, the mean radiative forcing from deposited BC can be up to 3.55 W m−2 . This value is larger than that of Russian Arctic in spring (2.40 W m−2 ), although the averaged BC concentration in summer Arctic Ocean was generally lower than that in the spring Russian Arctic. Except for the increased concentration due to BC accumulation and the stronger incoming solar radiation in summer, the growth of snow grains from melting may be another important reason. Larger grain size allows more solar radiation penetrate into the snowpack, which could further prompt the enrichment of BC in surface snow and accelerate the snow and ice melting process. We note that the summer sea ice is covered by a large fraction of melt ponds, in the evaluation of BCs effects on sea ice melting, the role of melt ponds should be considered together.

6. Conclusions and discussion

This study summarizes present measurements of BC in snow and ice over the Arctic, and describes recent studies that report Arctic radiative forcings from BC deposition to snow and sea ice. The observations and modeled BC deposition both show that the average concentration in Russian Arctic > Canadian and Alaskan Arctic > Arctic Ocean and Greenland. The observed BC concentration in snow and ice presented a significant decreasing trend with the increase of latitude. The observations also indicate that BC concentration in snow on sea ice in summer was generally higher than that in spring due to the contribution from dry deposition and enrichment of BC in surface snow with melt. In addition, the grain size of surface snow in summer is larger than that in spring. The above reasons lead to a larger radiative forcing per unit of area in snow covered region over the summer Arctic Ocean. The averaged radiative forcing there can be up to 3.55 W m−2 , which is larger than that of 2.40 W m−2 in the Russian Arctic. From the validated modeled deposition, it can be seen that wet deposition is the main contributor to the BC in snow and ice over the Arctic, accounting for more than 90% of the total deposition. The multi-model mean radiative forcing from all sources of BC in snow and ice was 0.17 W m−2 over the Arctic (60–90°N). We need to note that the forcings here are direct forcings (either instantaneous or adjusted) that do not account for the high efficacy of snow forcing, which leads to an effective forcing that is greater by a factor of 2–4 (Flanner et al., 2007  ;  Hansen et al., 2007 ).

There are several sources of uncertainty in calculating the radiative forcing from BC deposition. First of all, the observations of BC in snow applied in current studies have involved various campaigns over different periods, snow samples have been gathered at different snow depths, and in some regions very few measurements have been conducted, such as in the Eastern Russian Arctic. The greatest uncertainty existed in the summer Arctic Ocean (Fig. 5 ), with a standard deviation of 2.10 W m−2 . During summer time, it is difficult to collect snow samples because quite a large fraction of the Arctic sea ice is covered with melt ponds. During the sampling process, only surface snow was gathered at some sites, and at other sites, because it is hard to distinguish snow and ice in the interface, the sample was collected as snow and ice mixture. Besides, some regions were covered with new or recent snow, and other regions were covered with aged or melting snow. All of the above reasons could lead to the large deviation of BC over summer Arctic Ocean.

Secondly, there is significant discrepancy in the BC concentrations measured using different methods. For the thermal–optical methods, a complication is the charring of organic carbon (OC) at high temperatures, which reduces its volatility and causes it to become an artifact in the EC (elemental carbon)/OC determination (Chow et al., 2004 ). Variations of this method include different temperature ramping schemes, and the correction for the charring of OC during pyrolysis by monitoring the optical reflectance (Huntzicker et al., 1982 ) or light transmission (Turpin et al., 1990 ). Comparisons of different thermal evolution protocols reveal that EC concentrations can differ by more than an order of magnitude (Schmid et al., 2001 ). For the SP2 method, the measurement of BC in snow has a higher uncertainty (60%) than the measurement of BC in atmospheric aerosol (10%–25%) for the Arctic, which is due to uncertainties related to the aerosol nebulization from snow melt and the larger size of BC particles in snow than in aerosols (Wendl et al., 2014 ).

In addition, due to the contribution of dry deposition and snow sublimation in spring, BC could accumulate in the surface snow. When snow began to melt in late spring, most of the soluble impurities are scavenged by melt water, and the insoluble impurities, such as insoluble EC and dust, are retained in the surface layer, leading to higher concentrations of light-absorbing impurities in surface snow (Xu et al., 2012  ;  Doherty et al., 2013 ). This process has not yet been parameterized in current sea ice models (Hunke and Lipscomb, 2008  ;  Holland et al., 2012 ), resulting in underestimation of BC within snow on Arctic sea ice.

Koch et al. (2009) compared the modeled vertical profiles of BC from global aerosol models contributing to the AeroCom project with measurements conducted during several aircraft campaigns (spring ARCTAS, ARCPAC, summer ARCTAS) at high latitudes. They suggested that most of the present models underestimate BC concentrations throughout much of the troposphere, especially in the near surface layer. Estimations derived from various models can differ by up to a factor of five, indicating that there are large uncertainties in simulating the vertical profile of BC aerosols over the Arctic. The parameterizations of BC deposition, transport, and aging process need to be further improved.

To narrow the range of uncertainty in current study, we suggest several efforts should be conducted first and foremost:

Different techniques for BC measurements should be applied at the same site, in order to improve current measuring methods through comparison and choose an optimal one to determine BC concentrations over the whole Arctic.

More vertical profiles of aerosol BC should be observed in data-blank area of the Arctic, such as Svalbard and Russia. Before calculating the radiative forcing, the model results should be firstly constrained by the profile observations.

The redistribution of BC in snow should be investigated both during snow accumulation and melt period. The effects of BC deposition, snow physics, and melt water scavenging should be quantitative analyzed. This will help to improve the parameterization of the BC-albedo feedback in sea ice models at different phases of snow evolution.

The feedback of BC-snow-albedo has been noticed in many studies. However, the relationship between BC and snow density has rarely been investigated. BC can impact albedo by changing the snow density, especially in the regions of higher BC concentration.

Acknowledgment

This study is funded by the Ministry of Science and Technology of China (2013CBA01804 ), the National Nature Science Foundation of China (41425003 , Y51101P1A1 ), the key project of CAMS: Research on the key processes of Cryospheric rapid changes (KJZD-EW-G03-04 ) and the Opening Founding of State Key Laboratory of Cryospheric Sciences (SKLCS-OP-2016-03 ). We would like to thank two anonymous referee for their valuable comments greatly improving the paper.

References

  1. AMAP, 2015 AMAP; Black Carbon and Ozone as Arctic Climate Forcers; Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway (2015)
  2. Bauer et al., 2007 S.E. Bauer, D. Koch, N. Unger, et al.; Nitrate aerosols today and in 2030: a global simulation including aerosols and tropospheric ozone; Atmos. Chem. Phys., 7 (2007), pp. 5043–5059 http://dx.doi.org/10.5194/acp-7-5043-2007
  3. Bond et al., 2013 T.C. Bond, S.J. Doherty, D.W. Fahey, et al.; Bounding the role of black carbon in the climate system: a scientific assessment; J. Geophys. Res. Atmos., 118 (2013) 5380–5552
  4. Brown and Mote, 2009 R.D. Brown, P.W. Mote; The response of Northern Hemisphere snow cover to a changing climate; J. Clim., 22 (2009), pp. 2124–2145
  5. Brown et al., 2010 R. Brown, C. Derksen, L.B. Wang; A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008; J. Geophys. Res. Atmos., 115 (2010), p. D16111
  6. Bulygina et al., 2009 O.N. Bulygina, V.N. Razuvaev, N.N. Korshunova; Changes in snow cover over Northern Eurasia in the last few decades; Environ. Res. Lett., 4 (2009), p. 045026
  7. Chow et al., 2004 J.C. Chow, J.G. Watson, L.-W. Chen, et al.; Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols; Environ. Sci. Technol., 38 (2004), pp. 4414–4422
  8. Clarke and Noone, 1985 A.D. Clarke, K.J. Noone; Soot in the Arctic snowpack: a cause for perturbations in radiative transfer; Atmos. Environ., 19 (1985), pp. 2045–2053
  9. Comiso et al., 2008 J.C. Comiso, C.L. Parkinson, R. Gersten, et al.; Accelerated decline in the Arctic sea ice cover; Geophys. Res. Lett., 35 (2008), p. L01703
  10. Corbett et al., 2010 J.J. Corbett, D.A. Lack, J.J. Winebrake, et al.; Arctic shipping emissions inventories and future scenarios; Atmos. Chem. Phys., 10 (2010), pp. 9689–9704
  11. Derksen and Brown, 2012 C. Derksen, R. Brown; Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections; Geophys. Res. Lett., 39 (2012), p. L19504
  12. 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
  13. Doherty et al., 2013 S.J. Doherty, T.C. Grenfell, S. Forsstrӧm, et al.; Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow; J. Geophys. Res. Atmos., 118 (2013), pp. 5553–5569 http://dx.doi.org/10.1002/jgrd.50235
  14. Dou et al., 2012 T. Dou, C. Xiao, D.T. Shindell, et al.; The distribution of snow black carbon observed in the Arctic and compared to the GISS-PUCCINI model; Atmos. Chem. Phys., 12 (2012), pp. 7995–8007 http://dx.doi.org/10.5194/acp-12-7995-2012
  15. 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
  16. 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. D11202 http://dx.doi.org/10.1029/2006JD008003
  17. Flanner et al., 2009 M.G. Flanner, C.S. Zender, P.G. Hess, et al.; Spring time warming and reduced snow cover from carbonaceous particles; Atmos. Chem. Phys., 9 (2009), pp. 2481–2497 http://dx.doi.org/10.5194/acp-9-2481-2009
  18. Flanner et al., 2012 M.G. Flanner, X. Liu, C. Zhou, et al.; Enhanced solar energy absorption by internally-mixed black carbon in snow grains; Atmos. Chem. Phys., 12 (10) (2012), pp. 4699–4721 http://dx.doi.org/10.5194/acp-12-4699-2012
  19. Forsström et al., 2013 S. Forsström, E. Isaksson, R.B. Skeie, et al.; Elemental carbon measurements in European Arctic snow packs; J. Geophys. Res. Atmos., 118 (2013), pp. 13614–13627
  20. Ghan et al., 2012 S.J. Ghan, X. Liu, R.C. Easter, et al.; Toward a minimal representation of aerosols in climate models: comparative decomposition of aerosol direct, semidirect, and indirect radiative forcing; J. Clim., 25 (2012), pp. 6461–6476 http://dx.doi.org/10.1175/JCLI-D-11-00650.1
  21. Goldenson et al., 2012 N. Goldenson, S.J. Doherty, C.M. Bitz, et al.; Arctic climate response to forcing from light-absorbing particles in snow and sea ice in CESM; Atmos. Chem. Phys., 12 (2012), pp. 7903–7920 http://dx.doi.org/10.5194/acp-12-7903-2012
  22. Grenfell et al., 2002 T. Grenfell, B. Light, M. Sturm; Spatial distribution and radiative effects of soot in the snow and sea ice during the SHEBA experiment; J. Geophys. Res., 107 (2002), p. 8032 http://dx.doi.org/10.1029/2000JC000414
  23. Hansen and Nazarenko, 2004 J. Hansen, L. Nazarenko; Soot climate forcing via snow and ice albedos; Proc. Natl. Acad. Sci., 101 (2004), pp. 423–428
  24. Hansen et al., 2007 J. Hansen, M. Sato, R. Ruedy, et al.; Climate simulations for 1880–2003 with GISS modelE; Clim. Dyn., 29 (2007), pp. 661–696
  25. Haywood and Shine, 1995 J.M. Haywood, K.P. Shine; The effect of anthropogenic sulfate and soot aerosol on the clear sky planetary radiation budget; Geophys. Res. Lett., 22 (1995) http://dx.doi.org/10.1029/95GL00075 issn: 0094-8276
  26. He et al., 2014 C. He, Q.-B. Li, K.N. Liou, et al.; Black carbon radiative forcing over the Tibetan Plateau; Geophys. Res. Lett., 41 (2014), pp. 7806–7813 http://dx.doi.org/10.1002/2014gl062191
  27. Holland et al., 2012 M. Holland, D.A. Bailey, B.P. Briegleb, et al.; Improved sea ice shortwave radiation physics in CCSM4: the impact of melt ponds and aerosols on Arctic sea ice; J. Clim., 25 (2012), pp. 1413–1430 http://dx.doi.org/10.1175/JCLI-D-11-00078.1
  28. Hunke and Lipscomb, 2008 E.C. Hunke, W.H. Lipscomb; CICE: The Los Alamos Sea Ice Model, Documentation and Software, Version 4.0; Tech. Rep. LA-CC-06-012 Los Alamos Natl. Lab. (2008), p. 76
  29. Huntzicker et al., 1982 J.J. Huntzicker, R.L. Johnson, J.J. Shah, et al.; Analysis of organic and elemental carbon in ambient aerosol by a thermal-optical method; G.T. Wolff, R.L. Klimisch (Eds.), Particulate Carbon: Atmospheric Life Cycle, Plenum Press (1982)
  30. IPCC, 2013 IPCC; Climate Change, 2013: The Physical Science Basis; Cambridge University Press, Cambridge (2013)
  31. Jacobson, 2004 M.Z. Jacobson; Climate response of fossil fuel and biofuel soot, accounting for soots feedback to snow and sea ice albedo and emissivity; J. Geophys. Res., 109 (2004), p. D21201
  32. Jiao et al., 2014 C. Jiao, M.G. Flanner, Y. Balkanski, et al.; An AeroCom assessment of black carbon in Arctic snow and sea ice; Atmos. Chem. Phys., 14 (2014), pp. 2399–2417 http://dx.doi.org/10.5194/acp-14-2399-2014
  33. Kinne et al., 2006 S. Kinne, M. Schulz, C. Textor, et al.; An AeroCom initial assessment: optical properties in aerosol component modules of global models; Atmos. Chem. Phys., 6 (2006), pp. 1815–1834 http://dx.doi.org/10.5194/acp-6-1815-2006
  34. Kirkevåg et al., 2013 A. Kirkevåg, T. Iversen, Ø. Seland, et al.; Aerosol–climate interactions in the Norwegian Earth system model: NorESM1-M; Geosci. Model Dev., 6 (2013), pp. 207–244 http://dx.doi.org/10.5194/gmd-6-207-2013
  35. Koch and del Genio, 2010 D. Koch, A.D. del Genio; Black carbon absorption effects on cloud cover: review and synthesis; Atmos. Chem. Phys., 10 (2010), pp. 7685–7696 http://dx.doi.org/10.5194/acp-10-7685-2010
  36. Koch et al., 2006 D. Koch, G.A. Schmidt, C. Field; Sulfur, sea salt and radionuclide aerosols in GISS ModelE; J. Geophys. Res., 111 (2006), p. D06206 http://dx.doi.org/10.1029/2004JD005550
  37. Koch et al., 1999 D. Koch, D. Jacob, I. Tegen, et al.; Tropospheric sulfur simulation and sulfate direct radiative forcing in the Goddard Institute for Space Studies general circulation model; J. Geophys. Res., 104 (1999), pp. 23799–23822
  38. Koch et al., 2007 D. Koch, T.C. Bond, D. Streets, et al.; Global impacts of aerosols from particular source regions and sectors; J. Geophys. Res., 112 (2007), p. D02205 http://dx.doi.org/10.1029/2005JD007024
  39. Koch et al., 2009 D. Koch, M. Schulz, S. Kinne, et al.; Evaluation of black carbon estimations in global aerosol models; Atmos. Chem. Phys., 9 (2009), pp. 9001–9026 http://dx.doi.org/10.5194/acp-9-9001-2009
  40. Koffi et al., 2012 B. Koffi, M. Schulz, F.M. Bréon, et al.; Application of the CALIOP layer product to evaluate the vertical distribution of aerosols estimated by global models: AeroCom phase I results; J. Geophys. Res., 117 (2012), p. D10201 http://dx.doi.org/10.1029/2011JD016858
  41. Lamarque et al., 2010 J.F. Lamarque, T.C. Bond, V. Eyring, et al.; Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application; Atmos. Chem. Phys., 10 (2010), pp. 7017–7039 http://dx.doi.org/10.5194/acp10-7017-2010
  42. Law and Stohl, 2007 K.S. Law, A. Stohl; Arctic air pollution: origins and impacts; Science, 315 (2007), pp. 1537–1540
  43. Lee et al., 2013 Y.H. Lee, J.F. Lamarque, M.G. Flanner, et al.; Evaluation of preindustrial to present-day black carbon and its albedo forcing from atmospheric chemistry and climate model intercomparison project (ACCMIP); Atmos. Chem. Phys., 13 (2013), pp. 2607–2634 http://dx.doi.org/10.5194/acp-13-2607-2013
  44. Liou et al., 2014 K.N. Liou, Y. Takano, C. He, et al.; Stochastic parameterization for light absorption by internally mixed BC/dust in snow grains for application to climate models; J. Geophys. Res. Atmos., 119 (2014), pp. 7616–7632 http://dx.doi.org/10.1002/2014jd021665
  45. Liu et al., 2012 X. Liu, R.C. Easter, S.J. Ghan, et al.; Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5; Geosci. Model Dev., 5 (2012), pp. 709–739
  46. McConnell et al., 2007 J.R. McConnell, R. Edwards, G.L. Kok, et al.; 20th-century industrial black carbon emissions altered Arctic climate forcing; Science, 317 (2007), pp. 1381–1384 http://dx.doi.org/10.1126/Science.1144856
  47. Mernild et al., 2009 S.H. Mernild, G.E. Liston, C.A. Hiemstra, et al.; Greenland ice sheet surface mass-balance modelling and freshwater flux for 2007, and in a 1995–2007 perspective; Hydrol. Proc., 23 (2009), pp. 2470–2484
  48. Ming et al., 2009 J. Ming, C. Xiao, H. Cachier, et al.; Black carbon (BC) in the snow of glaciers in west China and its potential effects on albedo; Atmos. Res., 92 (2009), pp. 114–123 http://dx.doi.org/10.1016/j.atmosres.2008.09.007
  49. Myhre et al., 2013 G. Myhre, B.H. Samset, M. Schulz, et al.; Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations; Atmos. Chem. Phys., 13 (2013), pp. 1853–1877 http://dx.doi.org/10.5194/acp-13-1853-2013
  50. Nghiem et al., 2012 S.V. Nghiem, D.K. Hall, T.L. Mote, et al.; The extreme melt across the Greenland ice sheet in 2012; Geophys. Res. Lett., 39 (2012), p. L20502
  51. Overland and Wang, 2013 J.E. Overland, M. Wang; When will the summer arctic be nearly sea ice free?; Geophys. Res. Lett., 40 (10) (2013), pp. 2097–2101 http://dx.doi.org/10.1002/grl.50316
  52. Perovich et al., 2009 D.K. Perovich, T.C. Grenfell, B. Light, et al.; Transpolar observations of the morphological properties of Arctic sea ice; J. Geophys. Res., 114 (2009), p. C00A04 http://dx.doi.org/10.1029/2008JC004892
  53. Quinn et al., 2011 P.K. Quinn, A. Stohl, A. Arneth, et al.; The Impact of Black Carbon on Arctic Climate; Arctic Monitoring and Assessment Programme (AMAP), Oslo (2011)
  54. Samset et al., 2013 B.H. Samset, G. Myhre, M. Schulz, et al.; Black carbon vertical profiles strongly affect its radiative forcing uncertainty; Atmos. Chem. Phys., 13 (2013), pp. 2423–2434 http://dx.doi.org/10.5194/acp-13-2423-2013
  55. Schmid et al., 2001 H. Schmid, L. Laskus, H.J. Abraham, et al.; Results of the “carbon conference” international aerosol carbon round robin test stage I; Atmos. Environ., 35 (2001), pp. 2111–2121
  56. Schmidt et al., 2006 G.A. Schmidt, R. Ruedy, J.E. Hansen, et al.; Present-day atmospheric simulations using GISS ModelE: comparison to in situ, satellite, and reanalysis data; J. Clim., 19 (2006), pp. 153–192
  57. Schulz et al., 2006 M. Schulz, C. Textor, S. Kinne, et al.; Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations; Atmos. Chem. Phys., 6 (2006), pp. 5225–5246 http://dx.doi.org/10.5194/acp-6-5225-2006
  58. Shindell et al., 2001 D.T. Shindell, J.L. Grenfell, D. Rind, et al.; Chemistry climate interactions in the Goddard Institute for Space Studies general circulation model: 1. Tropospheric chemistry model description and evaluation; J. Geophys. Res., 106 (2001), pp. 8047–8076
  59. Shindell et al., 2006 D.T. Shindell, G. Faluvegi, N. Unger, et al.; Simulations of preindustrial, present-day, and 2100 conditions in the NASA GISS composition and climate model G-PUCCINI; Atmos. Chem. Phys., 6 (2006), pp. 4427–4459 http://dx.doi.org/10.5194/acp-6-4427-2006
  60. Spackman et al., 2010 J.R. Spackman, R.-S. Gao, W.D. Neff, et al.; Aircraft observations of enhancement and depletion of black carbon mass in the springtime Arctic; Atmos. Chem. Phys., 10 (2010), pp. 9667–9680
  61. Stier et al., 2013 P. Stier, N.A.J. Schutgens, N. Bellouin, et al.; Host model uncertainties in aerosol radiative forcing estimates: results from the Aero-Com Prescribed intercomparison study; Atmos. Chem. Phys., 13 (2013), pp. 3245–3270 http://dx.doi.org/10.5194/acp-13-3245-2013
  62. Stroeve et al., 2012a J.C. Stroeve, V. Kattsov, A. Barrett, et al.; Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations; Geophys. Res. Lett., 39 (2012), p. L16502
  63. Stroeve et al., 2012b J.C. Stroeve, M.C. Serreze, M.M. Holland, et al.; The Arctics rapidly shrinking sea ice cover: a research synthesis; Clim. Change, 110 (2012), pp. 1005–1027
  64. Tedesco, 2007 M. Tedesco; A new record in 2007 for melting in Greenland; EOS Trans. Am. Geophys. Union, 88 (2007), p. 383
  65. Textor et al., 2006 C. Textor, M. Schulz, S. Guibert, et al.; Analysis and quantification of the diversities of aerosol life cycles within AeroCom; Atmos. Chem. Phys., 6 (2006), pp. 1777–1813 http://dx.doi.org/10.5194/acp-6-1777-2006
  66. Textor et al., 2007 C. Textor, M. Schulz, S. Guibert, et al.; The effect of harmonized emissions on aerosol properties in global models: an AeroCom experiment; Atmos. Chem. Phys., 7 (2007), pp. 4489–4501 http://dx.doi.org/10.5194/acp-7-4489-2007
  67. Turpin et al., 1990 B.J. Turpin, R.A. Cary, J.J. Huntzicker; An in-situ time resolved analyzer for aerosol organic and elemental carbon; Aerosol Sci. Technol., 12 (1990), pp. 161–171
  68. Wang et al., 2011 Q. Wang, D.J. Jacob, J.A. Fisher, et al.; Sources of carbonaceous aerosols and deposited black carbon in the Arctic in winter-spring: implications for radiative forcing; Atmos. Chem. Phys., 11 (2011), pp. 12453–12473 http://dx.doi.org/10.5194/acp-11-12453-2011
  69. 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 http://dx.doi.org/10.1029/2012JD018291
  70. Warren and Wiscombe, 1980 S. Warren, W. Wiscombe; A model for the spectral albedo of snow. 2. Snow containing atmospheric aerosols; J. Atmos. Sci., 37 (1980), pp. 2734–2745
  71. Wendl et al., 2014 I.A. Wendl, J.A. Menking, R. Färber, et al.; Optimized method for black carbon analysis in ice and snow using the Single Particle Soot Photometer; Atmos. Meas. Tech., 7 (2014), pp. 2667–2681
  72. Whitt et al., 2011 D.B. Whitt, J.T. Wilkerson, M.Z. Jacobson, et al.; Vertical mixing of commercial aviation emissions from cruise altitude to the surface; J. Geophys. Res., 116 (2011), p. D14109 http://dx.doi.org/10.1029/2010JD015532
  73. 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 (2006), pp. 257–262 http://dx.doi.org/10.3189/172756406781812122
  74. Xu et al., 2012 B. Xu, J. Cao, D.R. Joswiak, et al.; Post depositional enrichment of black soot in snow-pack and accelerated melting of Tibetan glaciers; Environ. Res. Lett., 7 (2012) http://dx.doi.org/10.1088/17489326/7/014022
  75. Yun and Penner, 2012 Y. Yun, J.E. Penner; Global model comparison of heterogeneous ice nucleation parameterizations in mixed phase clouds; J. Geophys. Res. Atmos., 117 (2012), p. D07203 http://dx.doi.org/10.1029/2011JD016506
  76. Zhou et al., 2012 C. Zhou, J.E. Penner, M.G. Flanner, et al.; Transport of black carbon to polar regions: sensitivity and forcing by black carbon; Geophys. Res. Lett., 39 (2012), p. L22804 http://dx.doi.org/10.1029/2012GL053388
Back to Top

Document information

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

Licence: Other

Document Score

0

Views 1
Recommendations 0

Share this document

Keywords

claim authorship

Are you one of the authors of this document?