Influence of dust and black carbon on the snow albedoin the NASA Goddard Earth Observing Systemversion 5 land surface model

Teppei J. Yasunari,1,2 Randal D. Koster,1 K.‐M. Lau,1 Teruo Aoki,3 Yogesh C. Sud,1

Takeshi Yamazaki,4 Hiroki Motoyoshi,5 and Yuji Kodama6

Received 4 August 2010; revised 25 October 2010; accepted 18 November 2010; published 27 January 2011.

[1] Present‐day land surface models rarely account for the influence of both black carbonand dust in the snow on the snow albedo. Snow impurities increase the absorption ofincoming shortwave radiation (particularly in the visible bands), whereby they have majorconsequences for the evolution of snowmelt and life cycles of snowpack. A newparameterization of these snow impurities was included in the catchment‐based landsurface model used in the National Aeronautics and Space Administration Goddard EarthObserving System version 5. Validation tests against in situ observed data were performedfor the winter of 2003–2004 in Sapporo, Japan, for both the new snow albedoparameterization (which explicitly accounts for snow impurities) and the preexistingbaseline albedo parameterization (which does not). Validation tests reveal that dailyvariations of snow depth and snow surface albedo are more realistically simulated with thenew parameterization. Reasonable perturbations in the assigned snow impurityconcentrations, as inferred from the observational data, produce significant changes insnowpack depth and radiative flux interactions. These findings illustrate the importance ofparameterizing the influence of snow impurities on the snow surface albedo for propersimulation of the life cycle of snow cover.

Citation: Yasunari, T. J., R. D. Koster, K.‐M. Lau, T. Aoki, Y. C. Sud, T. Yamazaki, H. Motoyoshi, and Y. Kodama (2011),Influence of dust and black carbon on the snow albedo in the NASA Goddard Earth Observing System version 5 land surfacemodel, J. Geophys. Res., 116, D02210, doi:10.1029/2010JD014861.

1. Introduction

[2] A large amount of water, roughly 24 million km3, isstored in present‐day glaciers and snow packs [Oki andKanae, 2006]. These reservoirs vary in size over theannual cycle, thereby affecting available water resources inmany regions of the world [e.g., Mote, 2003; Yao et al.,2004]. As noted in scores of studies [e.g., Barnett et al.,1989; Zhang et al., 2004], changes in snow cover anddepth can also affect the surface fluxes that in turn modulatethe atmospheric circulation and, accordingly, climate.[3] Snow albedo is a critical player in the growth and

ablation of snowpack; a higher albedo implies less availableenergy for melting or sublimating snow. Several factors

work together to determine snow albedo, including snowgrain size (branch width and length for dendrite snowcases), solar zenith angle (SZA), liquid water content, andsnow impurities [Wiscombe and Warren, 1980; Warren andWiscombe, 1980; Grenfell et al., 1994; Aoki et al., 1999,2000, 2006, 2007; Motoyoshi et al., 2005; Tanikawa et al.,2006, 2009; Flanner et al., 2007; Aoki and Tanaka, 2008].Here we examine a factor that is often neglected in the snowalbedo component of land surface model (LSM) studies:the deposition of atmospheric black carbon and dust (BCD)onto the snow surface, which are well‐known absorbers ofsolar radiation [e.g., Warren and Wiscombe, 1980, 1985;Aoki et al., 2000; Hansen and Nazarenko, 2004; Flanneret al., 2007; Aoki and Tanaka, 2008]. Through their radia-tive effects on snow [e.g., Lau et al., 2006, 2010; IPCC,2007], these aerosols can, in turn, affect the life cycle ofthe snowpack, the surface heat budget, and the atmosphericcirculation. The long‐range transport of BCD is welldocumented [e.g., Hadley et al., 2007; Yasunari et al., 2007;Yasunari and Yamazaki, 2009; Uno et al., 2009], implyingthat aerosol emission in one part of the globe can affectsnowpack optical properties and evolution in another.[4] The impact of deposited black carbon (BC) on melt

water from Himalayan glaciers is a major concern for peopleliving in the Indo‐Gangetic Plains and eastern China, wheremeltwater runoff is a primary source of potable water and

1NASA Goddard Space Flight Center, Greenbelt, Maryland, USA.2Also at Goddard Earth Science and Technology Center, University of

Maryland Baltimore County, Baltimore, Maryland, USA.3Meteorological Research Institute, Tsukuba, Japan.4Department of Geophysics, Graduate School of Science, Tohoku

University, Sendai, Japan.5Snow and Ice Research Center, National Research Institute for Earth

Science and Disaster Prevention, Nagaoka, Japan.6Institute of Low Temperature Science, Hokkaido University, Sapporo,

Japan.

Copyright 2011 by the American Geophysical Union.0148‐0227/11/2010JD014861

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D02210, doi:10.1029/2010JD014861, 2011

D02210 1 of 15

where substantially large amounts of BC would be depositedon the snow owing to the proximity to heavily pollutedregions [Ramanathan et al., 2007]. The IPCC [2007], citingstudies by Hansen and Nazarenko [2004] and Hansen et al.[2005], noted the positive radiative forcing impact of BC onsnow cover. Recent studies suggest that BC deposits oversnow in Tibetan and Himalayan regions contribute to snowalbedo reductions and that those reductions likely increasemeltwater runoff from glaciers [Ming et al., 2009; Yasunariet al., 2010]. However, only very limited observationalstudies of BC suspended in the air or deposits in snow overthese regions have been carried out [Xu et al., 2006, 2009a,2009b;Ming et al., 2008, 2009; Cong et al., 2009, 2010]. Asfor dust, the timing of dust events and the amount of dust canaffect runoff production and stagger the timing of snowmeltin the melting season [Fujita, 2007; Steltzer et al., 2009].[5] A quantitative assessment of snow albedo changes and

resulting runoff changes induced by BCD deposition ontoglacier and seasonal snow pack is thus critical for manywater resources and climate change applications. Regionalor global modeling studies have begun to address this issue,with inclusions of BC effects on snow albedo [e.g., Hansenand Nazarenko, 2004; Jacobson, 2004; Hansen et al., 2005;Koch et al., 2009; Qian et al., 2009] and the combinedeffects of BCD on albedo examined in a few studies[Flanner et al., 2009; Aoki and Tanaka, 2008; Watanabeet al., 2010; Qian et al., 2010]. Here we assess a newsnow albedo parameterization in using the LSM componentof the Goddard Earth Observing System version 5 (GEOS‐5) Earth system model [Rienecker et al., 2008], developedby the National Aeronautics and Space Administration(NASA) Global Modeling and Assimilation Office(GMAO). Our study is unique in that our model uses iceplate scattering theory, which is different conceptually fromthe Mie scattering theories ofWiscombe and Warren [1980],Flanner et al. [2007], Aoki et al. [1999], and Aoki andTanaka [2008]. Mie scattering theory requires size dis-tributions of snow grain size, which are hard to estimateaccurately. With our approach, the relevant snow physicalproperty is snow density, an easily diagnosed variable in theLSM that can be estimated relatively accurately, and thesnow density is used to calculate snow specific surface area(SSA) relevant to optical properties of snow. Also, com-pared to previous studies, this study is unique in thatit provides a detailed validation for one winter, againstobservations, of BCD impacts on the temporal (hourly anddaily) evolution of simulated albedos and snow mass.[6] The GEOS‐5 LSM includes a three‐layer snowpack

module [Lynch‐Stieglitz, 1994; Stieglitz et al., 2001] coupledto a catchment‐based treatment of land surface hydrology[Koster et al., 2000; Ducharne et al., 2000]. We upgradedthe snow albedo module to include the influence of BCD onsnow albedo, taking account of snow‐impurity opticalparameters and the associated changes in multiple reflec-tions. We then validated the offline simulations with theupgraded module against meteorological observations takenduring the winter of 2003–2004 in Sapporo, Japan. Ouroverall goal is to improve the simulation of snow physics,with an eye toward eventually examining, with the coupledEarth system model, the global impacts of BCD depositionon snow: impacts such as the potentially accelerated retreatof Himalayan and Tibetan glaciers.

2. Model Description

2.1. The Original GEOS‐5 Snow Module

[7] The GEOS‐5 snowpack module uses three prognosticvariables (heat content, snow water equivalent, and snowdepth) for each of three vertically stacked layers [Stieglitzet al., 2001]. The model explicitly parameterizes meltingand refreezing of snow, snow compaction, liquid waterretention, and the impact of snow density on the thermalconductivity and albedo of snow. Fractional snow cover istreated by imposing a minimum snow water equivalent(SWE) of 13 mm in the vertical dimension; if the areallyaveraged snow amount in the grid element decreases belowthis minimum value, the areal fraction of the snowpackdecreases so as to maintain this minimum SWE where thesnow does exist. Snow albedo in the original snow moduleuses different reflectances for the visible (VIS) and near‐infrared (NIR) radiation bands, with reductions in albedoimposed by both vegetation masking and (in effect) frac-tional snow cover [Hansen et al., 1983; Stieglitz et al., 2001].[8] Simulations with the GEOS‐5 LSM using the original

snow albedo model [e.g., Stieglitz et al., 2001] have shownthat it produces reasonable SWE and snow depths, fractionalsnow coverage, snow density, and surface temperature atsome locations. Nevertheless, this snow model ignores theimpacts of BCD on snow albedo. This is rectified with themodifications described below.

2.2. Upgrades to the Snow Model in the GEOS‐5 LSM

[9] Our modifications follow the snow albedo scheme ofKondo et al. [1988] and Yamazaki et al. [1991, 1993]. Thescheme, which was validated against observations at severalJapanese sites by Kondo et al. [1988] and Yamazaki et al.[1993], utilizes the two‐stream approximation; in theirstudies, snow albedo is the net result of multiple reflectionsof broadband shortwave (SW) radiation under the assump-tion that snowpack consists of ice plates and air layers. Thesnow albedo, As, in the Yamazaki et al. [1991, 1993] schemeis given by

As ¼ rI þ 1� rIð Þ2 �1D1 þ �1ð Þ1þ D1 � rI �1D1 þ �1ð Þ ; ð1Þ

where

Di ¼ �i � �iþ1ð ÞDiþ1 exp 2iDz�iþ1ð Þ þ �i � �iþ1

�iþ1 � �ið ÞDiþ1 exp 2iDz�iþ1ð Þ þ �iþ1 � �i

� �

� exp �2iDz�ið Þ i ¼ 1; . . . ; n� 1ð Þ;

Dn ¼ 0;

�i ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA2i � B2

i

p;

�i ¼ Ai � �i

Bi;

�i ¼ Ai þ �i

Bi;

Ai ¼ 1� TIilIi

�dry;i�I

;

Bi ¼ RIi

lIi

�dry;i�I

;

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

2 of 15

RIi ¼ rI þ 1� rIð Þ2rI exp �2kI lIið Þ1� rI exp �kI lIið Þ½ �2 ; ð2Þ

and

TIi ¼ 1� rIð Þ2exp �kI lIið Þ1� rI exp �kI lIið Þ½ �2 : ð3Þ

[10] In these equations i represents the snow layer (i = 1–3in this model). The parameter mi represents the extinctioncoefficient of solar radiation, RI is the reflectance of a singleice plate, TI is the transparency of a single ice plate, rI isthe reflectivity of ice, lI is the effective ice thickness, rI isthe density of ice (assumed to be 917 kg m−3), and kI is theabsorption coefficient of ice. The vertical snow layer depthand dry snow density in each snow layer are represented byDz and rdry,i, respectively. The effective ice thickness lIi canbe determined from

lIi ¼ 2

�I S*i; ð4Þ

where S* represents specific surface area (SSA, the area ofthe surface of the ice particles in unit mass of snow: m2

kg−1), computed with

log10 S*i ¼ �15:32� �dry;i=1000

� �3 þ 16:65� �dry;i=1000� �2

� 7:30� �dry;i=1000� �þ 2:23: ð5Þ

[11] The empirical equation (5) for SSA is based on theobservational data of Narita [1971]; rdry,i is expressed inunits of kilograms per cubic meter.[12] Overall, although the concept of equal volume‐to‐

surface‐area (equal‐V/A) ratio, as reflected in the SSA,provides a useful proxy for ice optical properties [e.g.,Bergen, 1975; Wiscombe and Warren, 1980; Warren, 1982;Grenfell and Warren, 1999; Neshyba et al., 2003; Flannerand Zender, 2006], the relationship between SSA andsnow density is sometimes not as robust, as discussed later.In addition, even under similar snow density conditions,SSA can change with changing temperature [Legagneuxet al., 2004; Flanner and Zender, 2006]. Hence, to makethe snow model applicable to a wider variety of snowconditions, future work will consider the effects of tem-perature and other factors on SSA.[13] The ice plate assumption requires that the snow

albedo model use an optical ice plate thickness for thealbedo calculation. Kondo et al. [1988] determined theeffective ice thickness based on the expectation by Warren[1982] that if the SSA does not change, the ice plate isconsidered to have the same optical properties as the SSA.For Kondo et al. [1988] and Yamazaki et al. [1991, 1993],the numerator 2 (for effective ice thickness) in equation (4)implies the existence of an ice plate, based on an ideaderived fromWarren [1982]. A numerator of 3 (for effectivesnow grain radius) must be used in equation (4) if sphericalparticles are assumed [e.g., Flanner et al., 2007; Picardet al., 2009]. When the snow temperature rises to 0°C

(computationally above −0.001°C), a meltwater effect isinvoked, and SSA is decreased to 60% of its original value,producing results consistent with observed snow albedos[Kondo et al., 1988]. The ice plate or snow grains coveredby melt water have a decreased SSA, which is equivalent toan increased effective ice thickness or effective snow grainsize [e.g., Wiscombe and Warren, 1980;Warren, 1982; Aokiand Tanaka, 2008]. This SSA effect, which is reflected inequation (4), is important for detailed snow albedo fluctua-tions, as shown in section 3.2.[14] In the present study we modify the Yamazaki et al.

[1991, 1993] scheme by replacing kI with a “total”absorption coefficient (kall), one that also accounts for BCD:

kall ¼ MadustCdust þMaHyPhoBCCHyPhoBC þMaHyPhiBCCHyPhiBC

� ��I

þ 1� Cdust � CHyPhoBC � CHyPhiBC

� �kI ; ð6Þ

where Ma and C denote the mass absorption coefficients(MACs) and mass concentrations in snow, respectively,for dust, hydrophobic BC (HyPhoBC), and hydrophilic BC(HyPhiBC). Note that these three BCD components are cur-rently included in the Goddard Chemistry Aerosol Radiationand Transport model (GOCART) [Chin et al., 2000, 2002;Ginoux et al., 2001; see also http://acdb‐ext.gsfc.nasa.gov/People/Chin/gocartinfo.html], a chemical transport modelcoupled to GEOS‐5. Using realistic MAC values with massconcentrations in equation (6), together with snow densityinformation, a first‐order representation of BCD on snowalbedo can be estimated. Equation (6) is based on the snowimpurity factor defined by Aoki and Tanaka [2008], modi-fied here to work with our model.[15] The refractive index data of Warren [1984] (http://

omlc.ogi.edu/spectra/water/abs/index.html) were used toestimate the reflectivity of ice, rI,; these data were in turnused to determine the ice absorption coefficients kI at eachwavelength using equation (2) of Picard et al. [2009]. TheMACs used for dust, hydrophobic BC, and hydrophilic BCat each wavelength were derived from data provided by M.G. Flanner and C. S. Zender (personal communication,2009). In the calculation of MAC values, hydrophobic BCis assumed to be uncoated by any liquid aerosols, whereashydrophilic BC is assumed to be coated with sulfate. Theseoptical properties are identical to those applied by Flanneret al. [2009].[16] Figure 1a shows the spectral snow albedos calculated

by the new snow albedo model without snow impuritiestogether with the snow albedos by a Mie scattering‐basedsnow albedo model, multilayer Snow, Ice, and AerosolRadiative model (SNICAR), calculated with the onlinesnow albedo simulator (SAS; http://snow.engin.umich.edu/)based on Flanner et al. [2007]. Our preliminary calculationhere assumed five 2 cm thick snow layers and a typical freshsnow density of 110 kg m−3, corresponding to an effectiveice thickness of 54 mm (effective snow grain radius of81 mm for the SAS calculation). The output of our modelcorresponds well with the Mie‐scattering‐based calculationat an SZA of 50°. Hence, our snow albedo model wellrepresents diffuse albedos under cloudy conditions, gener-ally corresponding to an SZA of about 50° [Wiscombe and

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

3 of 15

Warren, 1980; Warren, 1982]. For comparison, one exam-ple of the albedos observed over Antarctica (very cleansnow) under cloudy conditions from Grenfell et al. [1994] isalso shown in Figure 1a. Their measurements of snowdensity over Antarctica are not directly comparable to thosein midlatitudes because of the higher surface snow density

in Antarctica (more than 300 kg m−3) resulting from theunique environment of wind speed, lower temperature, andslope inclination [Endo and Fujiwara, 1973]. We will thusneed to modify the snow density treatment in the LSM whenwe apply our snow albedo model to Antarctica in futurestudies. However, the optically effective snow grain radii of81 mm, based on the snow density in the model, is close tothe range of grain radii observed by Grenfell et al. [1994],one of the reasons for the agreement in Figure 1a.[17] Figure 1b also shows an example of the calculated

spectral snow albedos for different snow densities indicat-ing different SSAs in the absence of impurities (BCD). Thesnow density in the figure ranges from 110 to 600 kg m−3.These reductions in snow albedo with increasing snowdensity, particularly for the NIR, agree well with previousstudies [e.g., Wiscombe and Warren, 1980; Aoki et al.,1999]. Note that the snow albedo changes are in factphysically due to SSA changes rather than to snow densitychanges; the apparent snow density effect is indirect, aresult of the relationship in equation (5) between snowdensity and SSA.[18] Figure 1c shows, for a fresh snow density of 110 kgm−3,

the spectral reflectances produced by the model under dif-ferent impurity concentrations. For each curve we assumedthe same impurity concentration in all the snow layers andthe effective ice thickness used in equation (4) was 54 mm,corresponding to an effective grain radius of 81 mm. Asmentioned in previous studies [e.g., Warren and Wiscombe,1980; Flanner et al., 2007; Aoki and Tanaka, 2008], var-iations in the mass concentration of dust and BC affect theVIS albedo far more than the NIR albedo, which is insteadmore directly affected by SSA changes, which in turn areassociated with snow density changes.[19] Overall, the characteristics of the curves across the

spectrum in Figure 1 are consistent with those found inprevious studies [Wiscombe and Warren, 1980; Warren andWiscombe, 1980; Grenfell et al., 1994; Aoki et al., 1999;Tanikawa et al., 2006; Flanner et al., 2007; Aoki andTanaka, 2008]. Hence, we infer that our snow albedomodel fairly well captures the response of snow albedo tovariations in BCD.[20] Currently, for computational expediency, the GEOS‐

5 LSM uses only two spectral bands: VIS and NIR. Wemodify our snow albedo formulation accordingly, using icereflectivities, ice absorption coefficients, and MACs fordust, hydrophobic BC, and hydrophilic BC averaged overthe VIS (300–700 nm) and NIR (700–2500 nm) bands (seeTable 1). The spectrally weighted mean MAC of BCD forVIS and NIR was assumed to be representative. The MACsof dust for four size bins were averaged. The reflectivities ofice rI for VIS and NIR were estimated to be 0.018 and0.017, respectively, based on the data of Warren [1984].The ice absorption coefficients show large variability overthe VIS‐NIR range [e.g., Grenfell and Perovich, 1981;Warren et al., 2006]. Here we use the data ofWarren [1984]to calculate the mean absorption coefficient of ice for theVIS band; some more recent technical updates to these dataare available [Warren and Brandt, 2008], but the net effectof these updates is found to be small given the emphasis inour studies on heavily contaminated snow. Given thecomputed VIS ice absorption coefficient, we determine theNIR ice absorption coefficient that would produce a

Figure 1. Spectral snow albedos calculated by the newsnow albedo model. (a) Comparison between our modeland the Snow, Ice, and Aerosol Radiative (SNICAR) modelof Flanner et al. [2007] in the case of no impurities.(b) Spectral snow albedo changes due to snow densitychanges without impurity. (c) Spectral snow albedo changesdue to impurity changes for dust, hydrophobic black carbon(HYPHO_BC), and hydrophilic BC (HYPHI_BC) withconstant snow density. Filled circles denote the data observedby Grenfell et al. [1994] over Antarctica, with very little BC,together with standard deviations.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

4 of 15

broadband SW value of 10 m−1, the broadband value givenby Kondo et al. [1988], required to explain observed snowalbedos.[21] Note that in the LSM the thicknesses for the three

layers differ, with the maximum depth of the top layer being8 cm. Hence, for implementation of the snow albedo modelin the LSM, the Di calculation in equation (2) is modified toallow a different Dz for each snow layer.[22] The snow albedo model described thus far [Kondo

et al., 1988; Yamazaki et al., 1991, 1993] does notinclude the influence of SZA variations on albedo, insteadrepresenting the snow albedos at a SZA of 50° as shown inFigure 1a. However, it is well established that the SZAaffects the snow albedo [e.g., Wiscombe and Warren, 1980;Warren, 1982; Aoki et al., 1999]. To account for this, we usesome parts of the SZA formulation in equations (6) and (7)of Marks and Dozier [1992]. VIS and NIR snow albedos(AlbVIS and AlbNIR, respectively) are computed as follows:

AlbVIS ¼ Albcal VIS � ffiffiffiffiffiffiffireff

p � 1:375� 10�3� �� 1� cos �50ð Þ

þ ffiffiffiffiffiffiffireff

p � 1:375� 10�3� �� 1� cos �ð Þ;

ð7Þ

AlbNIR ¼ Albcal NIR� ffiffiffiffiffiffiffireff

p � 2:0� 10�3� �þ 0:1� �� 1� cos �50ð Þ

þ ffiffiffiffiffiffiffireff

p � 2:0� 10�3� �þ 0:1� �� 1� cos �ð Þ; ð8Þ

where Albcal_VIS and Albcal_NIR are the VIS and NIR snowalbedos calculated with equations (1)–(3), reff is the effec-tive grain radius (mm) calculated with the modified equation(4) (multiplied by 1.5 to change effective ice thickness toeffective grain radius), �50 is for an SZA of 50°, and � isthe SZA at the given time step. SZA is calculated with theformulations used in the NCAR LSM [Bonan, 1996]. Thediffuse component of snow albedo is calculated assuming� = 50° in equations (7) and (8), based on the work ofWiscombe and Warren [1980] and Warren [1982]. If theincoming SW radiation at the surface is less than 30% that atthe top of the atmosphere, we assume that the sky is totallycovered by clouds, and for such a fully cloudy sky, thecorresponding snow albedos are calculated using � = 50°(namely equal to Albcal_VIS and Albcal_NIR). (Note that thesnow albedo under cloudy skies can sometimes be largerthan the snow albedo computed with an SZA of 50°because, under cloudy skies, more of the surface‐incidentflux resides in the visible spectrum, given the clouds’absorption of the NIR. Future work will address this issue.)In this study, for clear sky conditions the snow albedo forbroadband SW is assumed to consist of 80% direct beam

radiation and 20% diffuse radiation, in analogy with Mellohet al. [2002].

2.3. Meteorological and Black Carbon and Dust DataApplied: Sapporo Winter 2003–2004

[23] We validated model performance using a series ofmeteorological and snow observations collected in Sapporo,Japan, from November 2003 (0100 on 1 November 2003)through the beginning of April 2004 (0000 on 6 April 2004).At the Institute of Low Temperature Science (ILTS) atHokkaido University, Sapporo, the total mass concentra-tions of the filtered snow samples in the top 0–2 and 0–10 cm of snow were measured, along with the snow albedosfor the various wave bands (VIS, NIR, and SW), somemeteorological components, and various snow physicalparameters by snow‐pit works [Aoki et al., 2006, 2007]. Forthe 2003–2004 winter, Aoki and Tanaka [2008] measuredmass concentrations of elemental (black) carbon (EC or BC)and organic carbon (OC) for the top 0–2 cm of snow sam-ples using a DRI2001 OC/EC Carbon Analyzer and esti-mated the mass concentration of dust by subtracting theconcentrations of EC and OC from the total mass concen-tration estimated with Nuclepore filters. The observedalbedos and snow depths at ILTS were directly compared tothe albedos and snow depths simulated by our model. Thedaily snow depth data at ILTS were linearly interpolatedinto 1 hourly data.[24] For forcing the LSM a complete set of hourly mete-

orological measurements was needed. With the exception ofdownwelling longwave radiation and specific humidity, thehourly meteorological data used to force the model weretaken from an Automated Meteorological Station (AWS) atthe Sapporo District Meteorological Observatory maintainedby the Japan Meteorological Agency (JMA) (hereafter,AWS/JMA), which is approximately 2.7 km away from theILTS site. The specific humidity was estimated fromatmospheric pressure and water vapour pressure. Thedownwelling longwave radiation, not available from AWS/JMA, was estimated from air temperature using an emis-sivity (0.849) derived from temporally available measure-ments at ILTS. At AWS/JMA, wind measurements werecarried out at 59.5 m above ground level; these were con-verted to 10 m wind values using the following equation[Kajikawa et al., 2004]:

U10 ¼ U59:5 � ln 10=z0ð Þ= ln 59:5=z0ð Þ; ð9Þ

where z0 is the surface roughness of a flat snow surface (1.4× 10−4 m [Kondo, 1994]).[25] Thus, in essence, we used the meteorological data

collected at the AWS/JMA site to force the LSM, and wecompared the simulated snow variables to those measured atthe nearby ILTS site, for which snow albedo measurementsand impurity information were available. We thus implicitlyassume for this study that the relevant meteorologicalforcing variables at these nearby sites are similar. Themeteorological conditions, wind direction, and snow depthsat ILTS and AWS/JMA are shown in Figures 2 and 3. In thewinter of 2003–2004 strong northwest winds dominated,though weak southeast winds were also seen (Figure 3a).Figure 3b shows that the snow depth was similar at ILTSand AWS/JMA during the accumulation period, though

Table 1. Absorption Coefficient for Ice (m−1) and Mass Absorp-tion Coefficient (m2 g−1) for VIS and NIR

VIS NIR

Ice 0.133 19.867Dust 0.046 0.012Hydrophobic BC 8.253 3.770Hydrophilic BC 12.556 5.419

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

5 of 15

snow depths at ILTS were greater during the melting season.Snow disappeared completely at ILTS on 4 April 4 and atAWS/JMA on 3 April. Nevertheless, the snow depth char-acteristics at these two sites show strong similarities.[26] We constructed two impurity data sets for running the

LSM, each based on snow samplings carried out during thiswinter at ILTS. Samplings at 0–2 and 0–10 cm were takenabout twice a week together with snow pit works, and thetotal masses for both 0–2 and 0–10 cm and BCD con-centrations at 0–2 cm in those snow samples were alsomeasured [Aoki et al., 2006, 2007; Aoki and Tanaka, 2008].Note that continuous 1 hourly impurity data are necessary toforce the LSM runs in this study, but an assumption ofstepwise impurity concentrations (i.e., assuming constantimpurity concentrations between the observation times) isnot realistic. Dry deposition, wet deposition, new snowfall,and some impurity flushing due to snow melting and rainare expected and may affect snow impurity concentrationsbetween the observation times. Hence, we use a specificinterpolation procedure to estimate the concentrationsbetween the observation times. Here we especially consid-ered the effects of snowfall and rain on the impurity data.Although this interpolation process is subject to its ownassumptions, we believe that it is better than the stepwiseassumption.

[27] For the first impurity data set (hereafter referred to asthe “original impurity data” and shown in Figure 4a), dustand elemental carbon (BC) values (as total amount) weredirectly read from Figure 5 of Aoki and Tanaka [2008]. Weassumed that in the snow accumulation season (roughlybefore 9 March), the total BC is composed of only hydro-phobic BC because frequent snowfall occurs and the BC haslittle time to age by liquid aerosols such as sulfate. Duringthe melting season (i.e., after March 9), in contrast, fewerprecipitation events occur, and the snow depth decreasesrapidly with time [Aoki et al., 2007]; we can thereforeassume more aged BC (i.e., hydrophilic BC) during thisperiod. We assume that 40% and 60% of the total BC ineach layer is composed of hydrophobic BC and hydrophilicBC, respectively, during the melting season. In addition, alook at Figure 5 of Aoki and Tanaka [2008] shows that BCconcentrations during the melting period are almost con-stant, despite presumably continuous BC deposition; thissuggests that some portion of BC is flushed away from thesnow surface during the melting season, an idea consistentwith the results of Conway et al. [1996], who showed thatmost hydrophilic soot flushes through the snow via melt-water 10 days after particles are first introduced.

Figure 2. Meteorological conditions in winter 2003–2004,Sapporo. All data except for the incoming longwave radia-tion were observed at an Automated Meteorological Station(AWS) at the Sapporo District Meteorological Observatorymaintained by the Japan Meteorological Agency (JMA;AWS/JMA). Long wave radiation is estimated values asmentioned in the text. (a) Air temperature (solid black line)and precipitation (gray bar). (b) Incoming SW radiation(solid gray line) and estimated incoming longwave radiation(solid black line). (c) Converted wind velocity at 10 m (solidgray line) and relative humidity (solid black line).

Figure 3. (a) Wind chart at AWS/JMA and (b) observedsnow depths at AWS/JMA and Institute of Low Tempera-ture Science (ILTS) in the winter of 2003–2004. For ILTSdata, daily data were linearly interpolated into 1 hourly data.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

6 of 15

[28] The second and third snow layers were assumed tocontain 60% and 40% of the dust and BC concentrations atthe top layer, respectively, since the top layer is more con-taminated owing to its direct contact with the atmosphere inthe case of no precipitation [Aoki et al., 2000; Tanikawaet al., 2009]. For the dust data following a heavy Asiandust deposition on 11–12 March [Aoki et al., 2006], weassigned 5% and 3% of the dust concentrations in the toplayer to the second and third layers, respectively, becausethe Asian dust should have been deposited on the top snowlayer (surface) only. In effect, all of these percentages are“tuning parameters” that allow us to ensure that the con-centrations in the lower layers were lower than the surfaceconcentrations and were not modified much by the dustevent, based on the previous studies [Aoki et al., 2000;Tanikawa et al., 2009]. We applied the concentration valuesfrom Aoki and Tanaka [2008] during the hours of 0000–1200 on the snow pit days when morning measurementswere taken. We retained the concentrations on the days ofsnow pit work until the next day of precipitation before thenext observation. On rain days the dust, hydrophobic BC,

and hydrophilic BC concentrations were forced to decreaseby 10%, 10%, and 50%, respectively, to reflect theenhanced efficiency of flushing of the hydrophilic BC,which has a higher mobility according to very limitedstudies [Conway et al., 1996; Flanner et al., 2007]. Notethat these percentages are also tuning parameters; futureanalysis should give us better values. On snow days thedust, hydrophobic BC, and hydrophilic BC concentrationsin the top two snow layers were forced to decrease to theirminimal concentrations (“fresh snow”) during the 2003–2004 winter. The third layer was assumed to remain unaf-fected by rain or snow. Impurity concentrations between arain or snow event and the following next impurity mea-surement date were estimated through linear interpolation.[29] The BCD data taken from Aoki and Tanaka [2008]

were for a 0–2 cm snow surface layer, whereas the topsnow layer depth of the LSM was mostly 8 cm as a maxi-mum value. This suggests the need for some adjustments tothe impurity concentrations. For the second impurity data setwe adjusted the concentrations of BCD in the first data setby observation‐based factors. We first computed the meanratio of total mass concentrations in 0–10 cm to that in 0–2 cm from Aoki et al. [2006], excluding periods (e.g., afternew snowfall) for which the ratio exceeded 1, and we thenmultiplied this ratio by 1.25 (= 10 cm/8 cm) to yield, fordust, a factor of 0.744 for the period prior to 9 March and of0.324 after 9 March. The lower ratio for the melting periodis due to the heavy Asian dust deposition on 11–12 March[Aoki et al., 2006]. However, the BC concentrations did notchange as much from the Asian dust event [Aoki andTanaka, 2008], and thus a single factor of 0.744 wasapplied to BC. The adjusted snow impurity data for 0–8 cmis hereafter referred to as the “reduced impurity data” asshown in Figure 4b, and it is presumably the best 1 hourlyimpurity data set available to force the LSM.

2.4. Sensitivity Tests

[30] For consistency with the ILTS site the GEOS‐5Catchment LSM, modified with our new snow albedo for-mulation, was run with ground cover vegetation parameters.The available hourly forcing was used at each 20 minsimulation time step within the hour. In the simulations theallowable snow density range in the model simulation wasset to 110–550 kg m−3 to maintain stability in model energybalance calculations; this snow density range is realistic formost glacial and seasonal snow surfaces and even for icesheets [e.g., Grenfell et al., 1994; Stieglitz et al., 2001;Shiraiwa et al., 2003, 2004].[31] We performed eight sensitivity tests focusing on the

simulation of snow albedo, depth, and cover duration at theILTS Sapporo site during the winter of 2003–2004 (Table 2).Runs 0 and 1 were performed with the original snow albedomodel of Stieglitz et al. [2001]. In run 0 the default maximumsnow albedos for VIS and NIR were 0.7 and 0.5, respec-tively, and the default minimum values were 0.5 and 0.3 forVIS and NIR, respectively. In run 1 the maximum snowalbedos for VIS and NIR were artificially increased to 1.0and 0.8, respectively. For both runs a fixed SZA of 60° wasused, but only for the calculation of vegetation albedos; theoriginal snow formulation does not include an SZA effect.Both runs assumed clear sky conditions, with (for snow)

Figure 4a. Original snow impurity data prescribed for run7 in the land surface model (LSM). (a) Dust, (b) hydropho-bic BC, and (c) hydrophilic BC concentrations for threesnow layers in the model estimated from data observed byAoki and Tanaka [2008].

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

7 of 15

equal albedos assumed for the diffuse and direct radiationcomponents.[32] Runs 2 and 3 used the new snow albedo formula-

tions, including the impact of SZA variations and assumingthat 80% of the incoming radiation is direct for clear skyconditions. Cloudy sky conditions were calculated with anSZA of 50°. Run 2 assumed no BCD impurities. Run 3 usedthe reduced impurity data set (Figure 4b), our best estimatefrom the field site. Accordingly, run 3 represents our besthope for an accurate snow albedo calculation.[33] Runs 4–7 examine the response of albedo to vari-

ous modifications in the new formulation. In run 4 theeffect of snow melting on SSA was disabled. In run 5 theeffect of vegetation on snow albedo was disabled. In run 6the dust concentrations in snow were set to 0 mg kg−1 toexamine how BC by itself affects snow albedos. Finally,in run 7 the original mass concentration data in Figure 4awere used order to investigate a case of greater snowcontamination. (Again, the original mass concentrationdata set is more “contaminated” because the scaling pro-cess described in the previous section, which converts thehigh mass concentrations for 0–2 cm to corresponding

Tab

le2.

Mod

elSettin

gsforSensitiv

ityTesta

Run

AlbedoMod

elIm

purity

inSno

wSolar

Zenith

Ang

leSky

Con

ditio

nDirectandDiffuse

Albedos

MeltEffecton

Sno

wAlbedo

Add

ition

alNoteon

Settin

gs

0Original

Non

eOnlyVG

(60°

fixed)

Allclearsky

Equ

alNon

eMaxim

alalbedo

sforsnow

:VIS

=0.7,

NIR

=0.5

1Original

Non

eOnlyVG

(60°

fixed)

Allclearsky

Equ

alNon

eMaxim

alalbedo

sforsnow

:VIS

=1.0,

NIR

=0.8

2New

Non

eBothSN

andVG

Allinclud

ingclou

dy80

%dir.and20

%diff.

0.6*

SSA

Sam

eas

3,bu

tno

impu

rity

3New

Reduced

impu

rity

data

BothSN

andVG

Allinclud

ingclou

dy80

%dir.and20

%diff.

0.6*

SSA

4New

Reduced

impu

rity

data

BothSN

andVG

Allinclud

ingclou

dy80

%dir.and20

%diff.

Non

e5

New

Reduced

impu

rity

data

BothSN

andVG

Allinclud

ingclou

dy80

%dir.and20

%diff.

0.6*

SSA

Novegetatio

neffect

onsnow

albedo

6New

Reduced

impu

rity

data

BothSN

andVG

Allinclud

ingclou

dy80

%dir.and20

%diff.

0.6*

SSA

Nodu

stcase

(Cdust=0)

7New

Originalim

purity

data

BothSN

andVG

Allinclud

ingclou

dy80

%dir.and20

%diff.

0.6*

SSA

a SN,snow

;SSA,specific

surfacearea;VG,vegetatio

n.

Figure 4b. Same as Figure 4a, but for the reduced snowimpurity data prescribed for runs 3–6 in the LSM.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

8 of 15

lower values for 0–8 cm, was not performed for this dataset.)

3. Results and Discussion

3.1. General Characteristics

[34] Table 2 outlines the simulations performed with theoriginal GEOS‐5 snow albedo model and the new model,and Figure 5 shows the results in runs 0–3 for noon albedos,along with hourly snow depths. We focus first on the snowalbedo, which was directly measured at noon at ILTS. First,however, a word of caution: the Catchment LSM char-acterizes snow cover across an area, and thus the simulatedalbedos reflect both vegetation “masking” and partial snowcover. During early winter and the melting season theseeffects tend to decrease simulated snow albedo in a way thatcannot be captured by highly localized radiation measure-ments. Thus, in the following analyses, we focus on albedosand snow depth generated after 14 January (Figures 6–9 andTable 3), when these effects are mitigated (Figure 7): by thisdate the Catchment LSM is effectively well covered withsnow. We, nevertheless, look at albedos during the snow-melt season because snow behavior during this period is

strongly affected by impurities; for this season the potentialscale mismatch must be kept in mind. In Figures 6–8 andTable 3 we also do not consider nighttime hours or periodsfor which, presumably owing to measurement error, theobserved snow albedos are greater than 1 or less than 0 orthe observed NIR albedos are higher than those for VIS.[35] Again, run 0 (Figure 5a) was performed with the

original Catchment LSM. The simulated noontime albedos(solid lines) disagree strongly with the measured values(circles): the original model does not capture the high albedoof fresh snow. In run 1 (Figure 5b) the maximum snowalbedos were artificially increased to 1.0 and 0.8, respec-tively, allowing the original model to capture the generalbehavior of snow albedos during the accumulation season.However, these artificial changes introduced a strong bias inthe melting season, with overly high snow albedos that forcethe snowpack to remain too long. (Compare the solid pinkshading, representing simulated snow depth, to the sky blueline, representing observed depths at ILTS, in Figure 5b.)[36] Run 2 (Figure 5c) shows how the new snow albedo

model works in its most basic form, when all impurities areassumed to be absent. The most striking result from thissimulation is the emergence of an ability to generate real-

Figure 5. Sensitivity tests by the old and new snow albedo models (runs 0–3) with different settings,corresponding to (a)–(d). Circles denote the observed snow albedos at noon for the VIS (red) and NIR(blue). Lines denote calculated snow albedos at noon for VIS (red) and NIR (blue). Solid sky‐blue linesdenote the observed snow depth at ILTS. Pink shading denotes the snow depth calculated in the LSM.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

9 of 15

istic‐looking variations in NIR snow albedo, variationsassociated with snow density and snow particle size. Notethat hourly fluctuations in snow albedos (not shown) arealso seen in run 2, owing to variations in SZA, an effectnot captured in the original snow model. Despite theseimprovements, however, snow albedos for VIS are muchtoo high in the melting season. Correspondingly, snowdepth, while well simulated during the accumulation season,remains too large during the snowmelt season (i.e., after9 March), with the snowpack surviving too long. With theaddition of impurities, these melting‐season biases may bereduced, as discussed next.[37] In run 3 (Figure 5d) we used our best estimates for

the reduced BCD concentrations in the new snow albedomodel. The resulting melt‐season snow albedo and snowdepth are considerably improved. Although small amountsof snow still exist in the last time step, run 3 shows a morereasonable timing of snow disappearance than runs 0–2. Thesimulated VIS snow albedos in the snow accumulationperiods are improved as well. The drastic snow albedoreduction after the Asian dust deposition on 11–12 March[Aoki et al., 2006] is also well reproduced. Nevertheless, thecalculated albedos and snow depths during the last few daysof the melt season still differ from the observations, possiblyowing to errors in the estimation of BCD concentrations,snow physical properties at ILTS (see section 3.3.), or theaforementioned disconnect in spatial scale between themodel’s representation of snow and the localized observa-tion site.[38] Run 4 (Figure 6) shows the impact of disabling the

effect of snow melting on SSA. The increase in the NIRalbedos during the melt season is clear. The snow depthaccordingly increases in the melting season relative to run 3(not shown). From run 5 we find that the effect of disablingvegetation masking on the VIS snow albedo is largest inthe initial snow accumulation period (not shown) and is

smaller after that (Figure 7). During fully snow‐coveredperiods the vegetation effect was smaller for the NIRalbedos (not shown). During the melting period the vege-tation effect on the VIS snow albedo in Figure 7 wasgenerally small and outweighed by the effects of snowimpurities (Figures 5c and 5d), suggesting that our newimpurity formulation is especially important in the meltingseason. In our final two experiments we examined the BCeffect in isolation (run 6, Figure 8) and a more contami-nated snow case (run 7; Figure 9). In general, these effectson VIS albedos during the melting period were larger thanthose of vegetation masking.[39] The root mean square errors (RMSEs) between the

observed (at ILTS) and simulated data were computed forthe SW, VIS, and NIR snow albedos and for snow depths(Table 3). In general, the smaller RMSEs were obtainedwith the new snow albedo model using impurity data. Thisreaffirms the need for including the effect of snow impu-rities together with SSA and SZA effects in the new snowalbedo model. Using the reduced impurity data along withour best estimates of the model parameters (run 3), theRMSEs for SW, VIS, and NIR albedos are 6.2%, 6.9%, and7.7%, respectively. For snow depths the estimated RMSEsin runs 3–7 were less than 12.1 cm for ILTS, with run 3producing an RMSE of 9.3 cm. Run 7 produced a slightlybetter snow depth RMSE (8.2 cm), which may suggestsome estimation error for the snow impurity concentrations,but the difference in RMSEs between runs 3 and 7 aresmall, and run 3 outperforms run 7 in terms of lower snowalbedo biases.[40] The RMSE differences between runs 3 and 7 were

largest for VIS albedo, and those between runs 3 and 4were largest for NIR albedo. This suggests that VIS albedois more strongly affected by variations in BCD mass con-centration than by vegetation or snow physical properties(here, SSA), whereas NIR albedo is more strongly affected

Figure 6. Effect of snow melting on 1 hourly NIR snow albedos after 14 January 2004. Circles denoteareas of large difference between runs 3 and 4.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

10 of 15

by variations in the snow physical properties, consistentwith previous studies [e.g., Warren and Wiscombe, 1980;Aoki et al., 2000; Flanner et al., 2007; Aoki and Tanaka,2008]. The similar RMSEs for NIR albedos in runs 3, 6,and 7 further support the idea that NIR albedo is not asstrongly affected by snow impurity changes.

3.2. Melting Effect on Snow Albedo

[41] As mentioned in some previous studies [e.g.,Wiscombe and Warren, 1980; Warren, 1982; Kondo et al.,1988; Aoki and Tanaka, 2008], melting snow decreasesthe SSA and increases the optically effective snow grainsize. Kondo et al. [1988] compared calculated and observedsnow albedos in Shinjo, Japan. They found that their cal-culated albedos during the melting period could not matchthe observed values without multiplication of the SSA by0.6, even if they added an absorption effect associated withsnow darkening. Accordingly, in our calculations weinstantly multiply the calculated SSA by 0.6 when the snowtemperature is equal to 0°C.[42] Again, run 4 shows that when this melting effect is

not accounted for, the calculated NIR albedo values are toohigh (Figure 6). The snow albedos for NIR are largelyaffected by snow physical parameters (SSA through snowdensity in this study) and not as much by snow impurities,as shown in Figure 1 and in previous studies [e.g.,Wiscombe and Warren, 1980; Flanner et al., 2007; Aokiet al., 2000; Aoki and Tanaka, 2008]. The results fromrun 3 compared with those from run 4 suggest that the newsnow model captures the albedo‐related physics of snowmelting (Figure 6).[43] The impact of snow melting on the VIS snow albedos

was small during the snow accumulation period but rela-tively large in the melting season (not shown). This could beeasily inferred from the exponential term in equations (2)and (3). During the melting period, mass concentrations of

BCD were higher (Figure 4a; even for the reduced impuritydata in Figure 4b). The product of the total absorptioncoefficient and the effective ice thickness then becomesmuch larger than that in the snow accumulation period,leading to a higher rate of decrease in the exponentialfunction. In terms of snow physics this means that snowmelting accelerates the snow aging process and reducessnow albedos.

3.3. Impact of Impurity Mass Concentration Changeson Snow Albedos in the Melting Season

[44] Variations in impurity mass concentration affect VIS(and thus SW) snow albedos during the melting season(Figures 5c and 5d and Table 3). Corresponding impacts onNIR snow albedos were seen to a small extent in the meltingperiod because of the small absorptions of BCD for NIR(Table 1).Wewere fortunate to have observations [Aoki et al.,2006] of total mass concentrations in 0–2 and 0–10 cm snowat ILTS, which included information about a heavy Asiandust deposition event on 11–12 March, and we also hadaccess to read the figure from a paper on direct measurementsof BCD concentrations [Aoki and Tanaka, 2008]. However,snow was sampled only about twice a week, together withsnow pit works [Aoki et al., 2006, 2007]. Although we tookinto account as best we could the impacts of snowfall and rainon BCD concentrations, impurity concentration variationsat other times between snow samplings and after 26 Marchare still unknown. This is a limitation of this study. The dif-ference in RMSEs between runs 3 and 7 for VIS albedos(Table 3) suggests a potential error associated with uncer-tainty in impurity concentration. Even with these uncertainconcentration estimates, however, the snow albedo variationsduring the melting season are better reproduced with thenew model than with the original snow albedo model.[45] An increase in the BCD mass concentration leads to a

decrease in the snow depth during the melting season

Figure 7. Effect of vegetation (run 3) and no vegetation (run 5) on 1 hourly VIS snow albedos after14 January 2004.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

11 of 15

(Figure 9), approaching the observed values at ILTS. Thissuggests that our estimation of the reduced snow impuritiesfor the latter part of the melting period in run 3 may beunderestimated. However, this impurity difference by itselfcannot fully explain the rapid decrease in snow depth inthe last couple of days of the melting period (as encircled inFigure 9) together with the rapid decreases in VIS and NIRalbedos (Figures 6–8).[46] Some potential explanations can be offered for the

inability of the model to reproduce the rapid decreases in theobserved snow depth and albedos at ILTS in the last coupleof days of the melting season: (1) the inconsistency in scalebetween the LSM and the point observations, as manifested,

for example, in the treatment of patch‐like melting; (2) thediscrepancy between AWS/JMA and ILTS meteorology(wind velocity, snow drift, etc.); and (3) some other snowmodel deficiency (e.g., a clear submerged snow layer at thebottom of the snowpack, like a water pond, was observed atILTS after the end of February (Figure 1a of Aoki et al.[2007]), whereas the LSM assumed vegetation undersnowpack). At the present time, we cannot say which factoris most important. Future progress on modeling and moredetailed observations will be necessary to address this.

4. Conclusions and Summary

[47] In this study we have introduced a new snow albedoscheme that incorporates BCD effects into the snowpackmodel of Stieglitz et al. [2001], the baseline snow modelused in the NASA GMAO land surface model in GEOS‐5[Ducharne et al., 2000; Koster et al., 2000]. Validation ofthe model was performed with observations taken at ILTS inSapporo, Japan, during the winter of 2003–2004, withforcing meteorological data taken from a nearby AWS/JMA

Figure 9. Differences in snow depths at ILTS and theresults of run 3 (reduced impurity) and run 7 (original impu-rity) after 14 January 2004. Circle denotes the same notablepoint as in Figure 8, discussed in the text.

Table 3. RMSE Between Observed and Calculated Values inEach Sensitivity Testa

Run

RMSE

SW VIS NIR Snow Depth at ILTS (m)

0 0.306 0.313 0.307 0.3071 0.091 0.101 0.094 0.1422 0.122 0.193 0.084 0.1783 0.062 0.069 0.077 0.0934 0.073 0.079 0.086 0.1215 0.067 0.084 0.076 0.1076 0.066 0.078 0.078 0.1067 0.074 0.091 0.079 0.082

aILTS, Institute of Low Temperature Science. Boldface numerals denoteminimal RMSEs.

Figure 8. Comparison between run 3 (reduced impurity) and run 6 (no dust) on VIS snow albedos after14 January 2004. Circle denotes the notable decrease in VIS albedos discussed in the text.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

12 of 15

station. The original GMAO snow albedo model reproducessnow albedos and snow cover duration in Sapporo poorly.The new snow albedo scheme, which includes snow impu-rity, SZA, and SSA effects, produces albedos that agree wellwith the ILTS observations, and these improved albedosappear to translate into improved snow depth estimates.[48] Overall, our results suggest that the accuracy of

simulated VIS snow albedos, snow depth, and snow coverduration depends largely on the accuracy of the estimatedBCD mass concentrations in the snow layers. Of course, wedo not obtain perfect agreement with the observations in thisstudy, possibly because our albedo formulation still requiressome minor modification or, just as likely, because theunderlying snow physics parameterization has deficiencies.Still, the overall agreement with the observations seemsreasonable.[49] The assessment of BCD impacts on seasonal snow-

packs and mountain glaciers, such as those in the Hima-layas, is essential for studies of climate and water resourcesand their sensitivities to anthropogenic forcing. Such studiescould take advantage, for example, of using BCD deposi-tions from chemical transport models such as GOCART[Chin et al., 2000, 2002; Ginoux et al., 2001], if the snowalbedo model used in conjunction with these modelsaccounts properly for BCD impacts on albedo and, thus, onsnow depth and meltwater timing. The new snow albedomodel presented herein is, we feel, a strong step forward indealing with these impacts in a physically reasonable way.

[50] Acknowledgments. This research was conducted as a part of theJoint Aerosol Monsoon Experiment (JAMEX), supported by the NASAInterdisciplinary Investigation Program. The first author is on a visitingfellowship to the Goddard Earth Science and Technology Center at theUniversity of Maryland at Baltimore County (NASA Grant and Coopera-tive Agreement NCC5‐494). Meteorological data at AWS/JMA wereobserved and maintained by the Japan Meteorological Agency. Massabsorption coefficient data were provided by Mark Flanner at the Univer-sity of Michigan and Charlie Zender at the University of California. Theonline snow albedo model based on that of Mark Flanner and others wasused. Tomonori Tanikawa at Earth Observation Research Center (EORC),Japan Aerospace Exploration Agency (JAXA) offered useful comments onformulations of the snow albedo model. We appreciate useful commentsfrom two anonymous reviewers.

ReferencesAoki, Te., and T. Tanaka (2008), Atmospheric aerosol deposition impacton snow albedo (in Japanese), Tenki, 55(7), 538–546.

Aoki, Te., T. Aoki, M. Fukabori, and A. Uchiyama (1999), Numericalsimulation of the atmospheric effects on snow albedo with a multiplescattering radiative transfer model for the atmosphere‐snow system,J. Meteorol. Soc. Jpn., 77(2), 595–614.

Aoki, Te., T. Aoki, M. Fukabori, A. Hachikubo, Y. Tachibana, andF. Nishio (2000), Effects of snow physical parameters on spectral albedoand bidirectional reflectance of snow surface, J. Geophys. Res., 105,10,219–10,236.

Aoki, Te., H. Motoyoshi, Y. Kodama, T. J. Yasunari, K. Sugiura, andH. Kobayashi (2006), Atmospheric aerosol deposition on snow surfacesand its effect on albedo, SOLA, 2, 13–16, doi:10.2151/sola.2006-004.

Aoki, Te., H. Motoyoshi, Y. Kodama, T. J. Yasunari, and K. Sugiura(2007), Variations of the snow physical parameters and their effects onalbedo in Sapporo, Japan, Ann. Glaciol., 46, 375–381.

Barnett, T. P., L. Dumenil, U. Schlese, E. Roeckner, and M. Latif (1989),The effect of Eurasian snow cover on regional and global climate varia-tions, J. Atmos. Sci., 46, 661–685.

Bergen, J. D. (1975), A possible relation of albedo to the density and grainsize of natural snow cover, Water Resour. Res., 11, 745–746.

Bonan, G. B. (1996), A land surface model (LSM version 1.0) for eco-logical, hydrological, and atmospheric studies: technical descriptionand user’s guide, NCAR Technical Note NCAR/TN‐417+STR, 150 pp.,Nat. Cent. for Atmos. Res., Boulder, Colo. (available at: ftp://daac.ornl.gov/data/model_archive/LSM/lsm_1.0/comp/NCAR_LSM_Users_Guide.pdf).

Chin, M., R. B. Rood, S.‐J. Lin, J. F. Muller, and A. M. Thomspon (2000),Atmospheric sulfur cycle in the global model GOCART: Model descrip-tion and global properties, J. Geophys. Res., 105, 24,671–24,687.

Chin, M., P. Ginoux, S. Kinne, B. N. Holben, B. N. Duncan, R. V. Martin,J. A. Logan, A. Higurashi, and T. Nakajima (2002), Tropospheric aerosoloptical thickness from the GOCART model and comparisons with satel-lite and sunphotometer measurements, J. Atmos. Sci., 59, 461–483.

Cong, Z. Y., S. C. Kang, and D. H. Qin (2009), Seasonal features of aerosolparticles recorded in snow from Mt. Qomolangma (Everest) and theirenvironmental implications, J. Environ. Sci., 21(7), 914–919.

Cong, Z. Y., S. C. Kang, S. P. Dong, X. D. Liu, and D. H. Qin (2010), Ele-mental and individual particle analysis of atmospheric aerosols from highHimalayas, Environ. Monit. Assess., 160, 323–335, doi:10.1007/s10661-008-0698-3.

Conway, H., A. Gades, and C. F. Raymond (1996), Albedo of dirty snowduring conditions of melt, Water Resour. Res., 32(6), 1713–1718.

Ducharne, A., R. D. Koster, M. J. Suarez, M. Stieglitz, and P. Kumar(2000), A catchment‐based approach to modelling land surface processesin a general circulation model: 2. Parameter estimation and model dem-onstration, J. Geophys. Res., 105, 24,823–24,838.

Endo, Y., and K. Fujiwara (1973), Characteristics of the snow cover in EastAntarctica along the route of the JARE South Pole traverse and factorscontrolling such characteristics, JARE Sci. Rep. Ser. C., 7, 1–38.

Flanner, M. G., and C. S. Zender (2006), Linking snowpack microphysicsand albedo evolution, J. Geophys. Res., 111, D12208, doi:10.1029/2005JD006834.

Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch (2007),Present‐day climate forcing and response from black carbon in snow,J. Geophys. Res., 112, D11202, doi:10.1029/2006JD008003.

Flanner, M. G., C. S. Zender, P. G. Hess, N. M. Mahowald, T. H. Painter,V. Ramanathan, and P. J. Rasch (2009), Springtime warming andreduced snow cover from carbonaceous particles, Atmos. Chem. Phys.,9(7), 2481–2497.

Fujita, K. (2007), Effect of dust event timing on glacier runoff: Sensitivityanalysis for a Tibetan glacier, Hydrol. Processes, 21(21), 2892–2896,doi:10.1002/hyp.6504.

Ginoux, P., M. Chin, I. Tegen, J. Prospero, B. Holben, O. Dubovik, andS.‐J. Lin (2001), Sources and global distributions of dust aerosols simu-lated with the GOCART model, J. Geophys. Res., 106, 20,255–20,273.

Grenfell, T. C., and D. K. Perovich (1981), Radiation absorption coeffi-cients of polycrystalline ice from 400–1400 nm, J. Geophys. Res., 86,7447–7450.

Grenfell, T. C., and S. G. Warren (1999), Representation of a nonsphericalice particle by a collection of independent spheres for scattering andabsorption of radiation, J. Geophys. Res., 104, 31,697–31,709.

Grenfell, T. C., S. G. Warren, and P. C. Mullen (1994), Reflection of solarradiation by the Antarctic snow surface at ultraviolet, visible, and near‐infrared wavelengths, J. Geophys. Res., 99, 18,669–18,684.

Hadley, O. L., V. Ramanathan, G. R. Carmichael, Y. Tang, C. E. Corrigan,G. C. Roberts, and G. S. Mauger (2007), Trans‐Pacific transport of blackcarbon and fine aerosols (D < 2.5 mm) into North America, J. Geophys.Res., 112, D05309, doi:10.1029/2006JD007632.

Hansen, J., and L. Nazarenko (2004) Soot climate forcing via snow and icealbedos, Proc. Natl. Acad. Sci. U.S.A., 101(2), 423–428.

Hansen, J. E., G. Russel, D. Rind, P. H. Stone, A. A. Lacis, S. Lebedeff,R. Ruedy, and L. Travis (1983), Efficient three‐dimensional dimensionalglobal models for climate studies: Models I and II, Mon. Weather Rev.,111, 609–662.

Hansen, J., et al. (2005), Efficacy of climate forcings, J. Geophys. Res.,110, D18104, doi:10.1029/2005JD005776.

IPCC (2007), Summary for policymakers, in Climate Change 2007: ThePhysical Science Basis. Contribution of Working Group I to the FourthAssessment Report of the Intergovernmental Panel on Climate Change,edited by S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis,K. B. Averyt, M. Tignor, and H. L. Miller, Cambridge Univ. Press, Cam-bridge, U. K.

Jacobson, M. Z. (2004), Climate response of fossil fuel and biofuel soot,accounting for soot’s feedback to snow and sea ice albedo and emissivity,J. Geophys. Res., 109, D21201, doi:10.1029/2004JD004945.

Kajikawa, M., H. Goto, K. Kanaya, and K. Kikuchi (2004), Multipleregression equations for the estimation of new snow density from mete-

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

13 of 15

orological elements (in Japanese with English abstract), J. Japanese Soc.Snow Ice, 66(5), 561–565.

Koch, D., S. Menon, A. Del Genio, R. Ruedy, I. Alienov, and G. A. Schmidt(2009), Distinguishing aerosol impacts on climate over the past century,J. Clim., 22(10), 2659–2677, doi:10.1175/2008JCLI2573.1.

Kondo, J. (1994), Meteolorogy of the Water Environment‐Water andHeat Balance of the Earth’s Surface (in Japanese), Asakura ShotenPress, Tokyo.

Kondo, J., Y. Numata, and T. Yamazaki (1988), Parameterization of snowalbedo (in Japanese with English abstract), J. Jpn. Soc. Snow Ice, 50,216–224.

Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar(2000), A catchment‐based approach to modelling land surface processesin a general circulation model: 1. Model structure, J. Geophys. Res., 105,24,809–24,822.

Lau, K. M., M. K. Kim, and K. M. Kim (2006), Asian monsoon anomaliesinduced by aerosol direct effects, Clim. Dyn., 26, 855–864, doi:10.1007/s00382-006-0114-z.

Lau, K. M., M. K. Kim, K. M. Kim, and W. S. Lee (2010), Enhanced sur-face warming and accelerated snow melt in the Himalayas and TibetanPlateau induced by absorbing aerosols, Environ. Res. Lett., 5, 025204,doi: 10.1088/1748-9326/5/2/025204.

Legagneux, L., A.‐S. Taillandier, and F. Domine (2004), Grain growththeories and the isothermal evolution of the specific surface area ofsnow, J. Appl. Phys., 95(11), 6175–6184.

Lynch‐Stieglitz, M. (1994), The development and validation of a simplesnow model for the GISS GCM, J. Clim., 7, 1842–1855.

Marks, D., and J. Dozier (1992), Climate and energy exchange at the snowsurface in the alpine region of the Sierra Nevada: 2. Snow cover energybalance, Water Resour. Res., 28, 3043–3054.

Melloh, R. A., J. P. Hardy, R. N. Bailey, and T. J. Hall (2002), An efficientsnow albedo model for the open and sub‐canopy, Hydrol. Processes, 16,3571–3584.

Ming, J., H. Cachier, C. Xiao, D. Qin, S. Kang, S. Hou, and J. Xu (2008),Black carbon record based on a shallow Himalayan ice core and its cli-matic implications, Atmos. Chem. Phys., 8(5), 1343–1352.

Ming, J., C. D. Xiao, H. Cachier, D. H. Qin, X. Qin, Z. Q. Li, and J. C. Pu(2009), Black carbon (BC) in the snow of glaciers in west China and itspotential effects on albedos, Atmos. Res., 92(1), 114–123.

Mote, P. W. (2003), Trends in snow water equivalent in the Pacific North-west and their climatic causes, Geophys. Res. Lett., 30(12), 1601,doi:10.1029/2003GL017258.

Motoyoshi, H., Te. Aoki, M. Hori, O. Abe, and S. Mochizuki (2005), Pos-sible effect of anthropogenic aerosol deposition on snow albedo reduc-tion at Shinjo, Japan, J. Meteorol. Soc. Jpn., 83A, 137–148.

Narita, H. (1971), Specific surface of deposited snow, II (in Japanese withEnglish abstract), Low Temp. Sci. Ser. A, 29, 69–79.

Neshyba, S. P., T. C. Grenfell, and S. G. Warren (2003), Representation ofa nonspherical ice particle by a collection of independent spheres forscattering and absorption of radiation: 2. Hexagonal columns and plates,J. Geophys. Res., 108(D15), 4448, doi:10.1029/2002JD003302.

Oki, T., and S. Kanae (2006), Global hydrological cycles and world waterresources, Science, 313, 1067–1072, doi:10.1126/science.1128845.

Picard, G., L. Arnaud, F. Domine, and M. Fily (2009), Determining snowspecific surface area from near‐infrared reflectance measurements:Numerical study of the influence of grain shape, Cold Reg. Sci. Tech.,56, 10–17, doi:10.1016/j.coldregions.2008.10.001.

Qian, Y., W. I. Gustafson Jr., L. R. Leung, and S. J. Ghan (2009), Effects ofsoot‐induced snow albedo change on snowpack and hydrological cyclein western United States based on Weather Research and Forecastingchemistry and regional climate simulations, J. Geophys. Res., 114,D03108, doi:10.1029/2008JD011039.

Qian, Y., M. G. Flanner, L. R. Leung, and W. Wang (2010), Sensitivitystudies on the impacts of Tibetan Plateau snowpack pollution on theAsian hydrological cycle and monsoon climate, Atmos. Chem. Phys. Dis-cuss., 10, 22855–22903, doi:10.5194/acpd-10-22855-2010.

Ramanathan, V., F. Li,M. V. Ramana, P. S. Praveen, D. Kim, C. E. Corrigan,H. Nguyen, E. A. Stone, J. J. Schauer, G. R. Carmichael, B. Adhikary, andS. C. Yoon (2007), Atmospheric brown clouds: Hemispherical andregional variations in long‐range transport, absorption, and radiative forc-ing, J. Geophys. Res., 112, D22S21, doi:10.1029/2006JD008124.

Rienecker, M. M., M. J. Suarez, R. Todling, J. Bacmeister, L. Takacs,H.‐C. Liu, W. Gu, M. Sienkiewicz, R.D. Koster, R. Gelaro, I. Stajner,and J. E. Nielsen (2008), The GEOS‐5 Data Assimilation System—Documentation of versions 5.0.1, 5.1.0, and 5.2.0, Technical ReportSeries on Global Modeling and Data Assimilation, 27 (available athttp://gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf).

Shiraiwa, T., K. Goto‐Azuma, S. Matoba, T. Yamasaki, T. Segawa,S. Kanamori, K. Matsuoka, and Y. Fujii (2003), Ice core drilling at KingCol, Mount Logan 2002, Bull. Glaciol. Res., 20, 57–63.

Shiraiwa, T., S. Kanamori, C. S. Benson, D. Solie, and Y. D. Muravyev(2004), Shallow ice‐core drilling at Mount Wrangell, Alaska, Bull. Gla-ciol. Res., 21, 71–77.

Steltzer, H., C. C. Landry, T. H. Painter, J. Anderson, and E. Ayres (2009),Biological consequences of earlier snowmelt from desert dust deposition inalpine landscapes, Proc. Natl. Acad. Sci. U.S.A., 106(28), 11,629–11,634,doi:10.1073/pnas.0900758106.

Stieglitz, M., A. Ducharne, R. D. Koster, and M. J. Suarez (2001), Theimpact of detailed snow physics on the simulation of snow cover and sub-surface thermodynamics at continental scales, J. Hydromet., 2, 228–242.

Tanikawa, T., T. Aoki, M. Hori, A. Hachikubo, O. Abe, and M. Aniya(2006), Monte Carlo simulations of spectral albedo for artificial snow-packs composed of spherical and nonspherical particles, Appl. Opt.,45, 5310–5319.

Tanikawa, T., K. Stamnes, T. Aoki, K. Kuchiki, A. Hachikubo, andK. Sugiura (2009), Effect of snow impurities and vertical profile onsnow albedo and reflectance, Eos Trans. AGU, 90(52), Fall Meet.Suppl., Abstract C33C–0519.

Uno, I., K. Eguchi, K. Yumimoto, T. Takemura, A. Shimizu, M. Uematsu,Z. Liu, Z. Wang, Y. Hara, and N. Sugimoto (2009), Asian dust trans-ported one full circuit around the globe, Nat. Geosci., 2, 557–560,doi:10.1038/NGEO583.

Warren, S. G. (1982), Optical properties of snow, Rev. Geophys. SpacePhys., 20, 67–89.

Warren, S. G. (1984), Optical constants of ice from the ultraviolet to themicrowave, Appl. Opt., 23, 1206–1225.

Warren, S. G., and R. E. Brandt (2008), Optical constants of ice from theultraviolet to the microwave: A revised compilation, J. Geophys. Res.,113, D14220, doi:10.1029/2007JD009744.

Warren, S. G., and W. J. Wiscombe (1980), A model for the spectral albedoof snow: II. Snow containing atmospheric aerosols, J. Atmos. Sci., 37(12),2734–2745.

Warren, S. G., and W. J. Wiscombe (1985), Dirty snow after nuclear war,Nature, 313, 467–470.

Warren, S. G., R. E. Brandt, and T. C. Grenfell (2006), Visible and near‐ultraviolet absorption spectrum of ice from transmission of solar radiationinto snow, Appl. Opt., 45(21), 5320–5334.

Watanabe, M., et al. (2010), Improved climate simulation by MIROC5:Mean states, variability, and climate sensitivity, J. Clim., in press.

Wiscombe, W. J., and S. G. Warren (1980), A model for the spectral albedoof snow, I: Pure snow, J. Atmos. Sci., 37(12), 2712–2733.

Xu, B., T. Yao, X. Liu, and N. Wang (2006), Elemental and organic carbonmeasurements with a two‐step heating‐gas chromatography system insnow samples from the Tibetan Plateau, Ann. Glaciol., 43, 257–262.

Xu, B., M. Wang, D. R. Joswiak, J. Cao, T. Yao, G. Wu, W. Yang, andH. Zhao (2009a), Deposition of anthropogenic aerosols in a southeasternTibetan glacier, J. Geophys. Res., 114, D17209, doi:10.1029/2008JD011510.

Xu, B., J. Cao, J. Hansen, T. Yao, D. R. Joswiak, N. Wang, G. Wu,M. Wang, H. Zhao, W. Yang, X. Liu, and J. He (2009b), Black sootand the survival of Tibetan glaciers, Proc. Natl. Acad. Sci. U.S.A., 106(52), 22,114–22,118, doi:10.1073/pnas.0910444106.

Yamazaki T. Sakuraoka, T. Nakamura, and T. J. Kondo (1991), A study ofsnow metamorphism: I Model description (in Japanese with Englishabstract), J. Jpn. Soc. Snow Ice, 53, 115–123.

Yamazaki, T., J. Kondo, T. Sakuraoka, and T. Nakamura (1993), A one‐dimensional model of the evolution of snow‐cover characteristics, Ann.Glaciol., 18, 22–26.

Yao, T., Y. Wang, S. Liu, J. Pu, Y. Shen, and A. Lu (2004), Recent glacialretreat in High Asia in China and its impact on water resource in North-west China, Sci. China Ser. D Earth Sci., 47(12), 1065–1075.

Yasunari, T. J., and K. Yamazaki (2009), Impacts of Asian dust storm asso-ciated with the stratosphere‐to‐troposphere transport in the spring of2001 and 2002 on dust and tritium variations in Mount Wrangell ice core,Alaska, Atmos. Environ., 43, 2582–2590, doi:10.1016/j.atmosenv.2009.02.025.

Yasunari, T. J., T. Shiraiwa, S. Kanamori, Y. Fujii, M. Igarashi, K. Yamazaki,C. S. Benson, and T. Hondoh (2007), Intra‐annual variations in atmo-spheric dust and tritium in the North Pacific region detected from an icecore from Mount Wrangell, Alaska, J. Geophys. Res., 112, D10208,doi:10.1029/2006JD008121.

Yasunari, T. J., P. Bonasoni, P. Laj, K. Fujita, E. Vuillermoz, A. Marinoni,P. Cristofanelli, R. Duchi, G. Tartari, and K.‐M. Lau (2010), Estimatedimpact of black carbon deposition during pre‐monsoon season fromNepal Climate Observatory—Pyramid data and snow albedo changes

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

14 of 15

over Himalayan glaciers, Atmos. Chem. Phys., 10, 6603–6615,doi:10.5194/acp-10-6603-2010.

Zhang, Y., T. Li, and B. Wang (2004), Decadal change of the spring snowdepth over the Tibetan Plateau: The associated circulation and influenceon the East Asian summer monsoon, J. Clim., 17, 2780–2793.

T. Aoki, Meteorological Research Institute, Tsukuba, 305‐0052 Japan.Y. Kodama, Institute of Low Temperature Science, Hokkaido University,

Sapporo, 060‐0819 Japan.

R. D. Koster, K.‐M. Lau, Y. C. Sud, and T. J. Yasunari, NASA GoddardSpace Flight Center, Greenbelt, MD 20771, USA. (teppei.j.yasunari@nasa.gov)H. Motoyoshi, Snow and Ice Research Center, National Research

Institute for Earth Science and Disaster Prevention, Nagaoka, 940‐0821Japan.T. Yamazaki, Department of Geophysics, Graduate School of Science,

Tohoku University, Sendai, 980‐8578 Japan.

YASUNARI ET AL.: DUST AND BLACK CARBON IN THE NASA LSM D02210D02210

15 of 15