Influence of dust and black carbon on the snow albedo in the NASA Goddard Earth Observing System version 5 land surface model

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2010jd014861, 2011 Influence of dust and black carbon on the snow albedo in the NASA Goddard Earth Observing System version 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 Kodama 6 Received 4 August 2010; revised 25 October 2010; accepted 18 November 2010; published 27 January [1] Present day land surface models rarely account for the influence of both black carbon and dust in the snow on the snow albedo. Snow impurities increase the absorption of incoming shortwave radiation (particularly in the visible bands), whereby they have major consequences for the evolution of snowmelt and life cycles of snowpack. A new parameterization of these snow impurities was included in the catchment based land surface model used in the National Aeronautics and Space Administration Goddard Earth Observing System version 5. Validation tests against in situ observed data were performed for the winter of in Sapporo, Japan, for both the new snow albedo parameterization (which explicitly accounts for snow impurities) and the preexisting baseline albedo parameterization (which does not). Validation tests reveal that daily variations of snow depth and snow surface albedo are more realistically simulated with the new parameterization. Reasonable perturbations in the assigned snow impurity concentrations, as inferred from the observational data, produce significant changes in snowpack depth and radiative flux interactions. These findings illustrate the importance of parameterizing the influence of snow impurities on the snow surface albedo for proper simulation 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 surface model, J. Geophys. Res., 116,, doi: /2010jd Introduction [2] A large amount of water, roughly 24 million km 3,is stored in present day glaciers and snow packs [Oki and Kanae, 2006]. These reservoirs vary in size over the annual cycle, thereby affecting available water resources in many 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 and depth can also affect the surface fluxes that in turn modulate the atmospheric circulation and, accordingly, climate. [3] Snow albedo is a critical player in the growth and ablation of snowpack; a higher albedo implies less available energy for melting or sublimating snow. Several factors 1 NASA Goddard Space Flight Center, Greenbelt, Maryland, USA. 2 Also at Goddard Earth Science and Technology Center, University of Maryland Baltimore County, Baltimore, Maryland, USA. 3 Meteorological Research Institute, Tsukuba, Japan. 4 Department of Geophysics, Graduate School of Science, Tohoku University, Sendai, Japan. 5 Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Prevention, Nagaoka, Japan. 6 Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan. Copyright 2011 by the American Geophysical Union /11/2010JD work together to determine snow albedo, including snow grain size (branch width and length for dendrite snow cases), solar zenith angle (SZA), liquid water content, and snow impurities [Wiscombe and Warren, 1980; Warren and Wiscombe, 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 snow albedo 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 of solar radiation [e.g., Warren and Wiscombe, 1980, 1985; Aoki et al., 2000; Hansen and Nazarenko, 2004; Flanner et al., 2007; Aoki and Tanaka, 2008]. Through their radiative effects on snow [e.g., Lau et al., 2006, 2010; IPCC, 2007], these aerosols can, in turn, affect the life cycle of the snowpack, the surface heat budget, and the atmospheric circulation. The long range transport of BCD is well documented [e.g., Hadley et al., 2007; Yasunari et al., 2007; Yasunari and Yamazaki, 2009; Uno et al., 2009], implying that aerosol emission in one part of the globe can affect snowpack 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 people living in the Indo Gangetic Plains and eastern China, where meltwater runoff is a primary source of potable water and 1of15

2 where substantially large amounts of BC would be deposited on the snow owing to the proximity to heavily polluted regions [Ramanathan et al., 2007]. The IPCC [2007], citing studies by Hansen and Nazarenko [2004] and Hansen et al. [2005], noted the positive radiative forcing impact of BC on snow cover. Recent studies suggest that BC deposits over snow in Tibetan and Himalayan regions contribute to snow albedo reductions and that those reductions likely increase meltwater runoff from glaciers [Ming et al., 2009; Yasunari et al., 2010]. However, only very limited observational studies of BC suspended in the air or deposits in snow over these regions have been carried out [Xu et al., 2006, 2009a, 2009b; Ming et al., 2008, 2009; Cong et al., 2009, 2010]. As for dust, the timing of dust events and the amount of dust can affect runoff production and stagger the timing of snowmelt in 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 onto glacier and seasonal snow pack is thus critical for many water resources and climate change applications. Regional or global modeling studies have begun to address this issue, with inclusions of BC effects on snow albedo [e.g., Hansen and Nazarenko, 2004; Jacobson, 2004; Hansen et al., 2005; Koch et al., 2009; Qian et al., 2009] and the combined effects of BCD on albedo examined in a few studies [Flanner et al., 2009; Aoki and Tanaka, 2008; Watanabe et al., 2010; Qian et al., 2010]. Here we assess a new snow albedo parameterization in using the LSM component of the Goddard Earth Observing System version 5 (GEOS 5) Earth system model [Rienecker et al., 2008], developed by the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO). Our study is unique in that our model uses ice plate scattering theory, which is different conceptually from the Mie scattering theories of Wiscombe and Warren [1980], Flanner et al. [2007], Aoki et al. [1999], and Aoki and Tanaka [2008]. Mie scattering theory requires size distributions of snow grain size, which are hard to estimate accurately. With our approach, the relevant snow physical property is snow density, an easily diagnosed variable in the LSM that can be estimated relatively accurately, and the snow density is used to calculate snow specific surface area (SSA) relevant to optical properties of snow. Also, compared to previous studies, this study is unique in that it provides a detailed validation for one winter, against observations, of BCD impacts on the temporal (hourly and daily) 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] coupled to a catchment based treatment of land surface hydrology [Koster et al., 2000; Ducharne et al., 2000]. We upgraded the snow albedo module to include the influence of BCD on snow albedo, taking account of snow impurity optical parameters and the associated changes in multiple reflections. We then validated the offline simulations with the upgraded module against meteorological observations taken during the winter of in Sapporo, Japan. Our overall goal is to improve the simulation of snow physics, with an eye toward eventually examining, with the coupled Earth system model, the global impacts of BCD deposition on snow: impacts such as the potentially accelerated retreat of Himalayan and Tibetan glaciers. 2. Model Description 2.1. The Original GEOS 5 Snow Module [7] The GEOS 5 snowpack module uses three prognostic variables (heat content, snow water equivalent, and snow depth) for each of three vertically stacked layers [Stieglitz et al., 2001]. The model explicitly parameterizes melting and refreezing of snow, snow compaction, liquid water retention, and the impact of snow density on the thermal conductivity and albedo of snow. Fractional snow cover is treated by imposing a minimum snow water equivalent (SWE) of 13 mm in the vertical dimension; if the areally averaged snow amount in the grid element decreases below this minimum value, the areal fraction of the snowpack decreases so as to maintain this minimum SWE where the snow does exist. Snow albedo in the original snow module uses different reflectances for the visible (VIS) and nearinfrared (NIR) radiation bands, with reductions in albedo imposed by both vegetation masking and (in effect) fractional 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 shown that it produces reasonable SWE and snow depths, fractional snow coverage, snow density, and surface temperature at some locations. Nevertheless, this snow model ignores the impacts of BCD on snow albedo. This is rectified with the modifications described below Upgrades to the Snow Model in the GEOS 5 LSM [9] Our modifications follow the snow albedo scheme of Kondo et al. [1988] and Yamazaki et al. [1991, 1993]. The scheme, which was validated against observations at several Japanese sites by Kondo et al. [1988] and Yamazaki et al. [1993], utilizes the two stream approximation; in their studies, snow albedo is the net result of multiple reflections of broadband shortwave (SW) radiation under the assumption that snowpack consists of ice plates and air layers. The snow albedo, A s, in the Yamazaki et al. [1991, 1993] scheme is given by where D i ¼ D n ¼ 0; i ¼ ð A s ¼ r I þ 1 r IÞ 2 ð 1 D 1 þ 1 Þ 1 þ D 1 r I ð 1 D 1 þ 1 Þ ; ð1þ ð i iþ1 ÞD iþ1 expð2idz iþ1 Þþ i iþ1 ð iþ1 i ÞD iþ1 expð2idz iþ1 Þþ iþ1 i expð 2iDz i Þ ði ¼ 1;...; n 1Þ; pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A 2 i B 2 i ; i ¼ A i i ; B i i ¼ A i þ i ; B i A i ¼ 1 T Ii B i ¼ R Ii l Ii dry;i ; I dry;i ; I l Ii 2of15

3 and ð R Ii ¼ r I þ 1 r IÞ 2 r I expð 2k I l Ii Þ 1 ½r I expð k I l Ii ÞŠ 2 ; ð2þ ð T Ii ¼ 1 r IÞ 2 expð k I l Ii Þ 1 ½r I expð k I l Ii ÞŠ 2 : ð3þ [10] In these equations i represents the snow layer (i =1 3 in this model). The parameter m i represents the extinction coefficient of solar radiation, R I is the reflectance of a single ice plate, T I is the transparency of a single ice plate, r I is the reflectivity of ice, l I is the effective ice thickness, r I is the density of ice (assumed to be 917 kg m 3 ), and k I is the absorption coefficient of ice. The vertical snow layer depth and dry snow density in each snow layer are represented by Dz and r dry,i, respectively. The effective ice thickness l Ii can be determined from l Ii ¼ 2 I S * i ; ð4þ where S* represents specific surface area (SSA, the area of the surface of the ice particles in unit mass of snow: m 2 kg 1 ), computed with log 10 S * 3 2 i ¼ 15:32 dry;i =1000 þ 16:65 dry;i =1000 7:30 dry;i =1000 þ 2:23: ð5þ [11] The empirical equation (5) for SSA is based on the observational data of Narita [1971]; r dry,i is expressed in units of kilograms per cubic meter. [12] Overall, although the concept of equal volume tosurface 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; Flanner and Zender, 2006], the relationship between SSA and snow density is sometimes not as robust, as discussed later. In addition, even under similar snow density conditions, SSA can change with changing temperature [Legagneux et al., 2004; Flanner and Zender, 2006]. Hence, to make the snow model applicable to a wider variety of snow conditions, future work will consider the effects of temperature and other factors on SSA. [13] The ice plate assumption requires that the snow albedo model use an optical ice plate thickness for the albedo calculation. Kondo et al. [1988] determined the effective ice thickness based on the expectation by Warren [1982] that if the SSA does not change, the ice plate is considered 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 idea derived from Warren [1982]. A numerator of 3 (for effective snow grain radius) must be used in equation (4) if spherical particles are assumed [e.g., Flanner et al., 2007; Picard et al., 2009]. When the snow temperature rises to 0 C (computationally above C), a meltwater effect is invoked, 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 covered by melt water have a decreased SSA, which is equivalent to an increased effective ice thickness or effective snow grain size [e.g., Wiscombe and Warren, 1980; Warren, 1982; Aoki and Tanaka, 2008]. This SSA effect, which is reflected in equation (4), is important for detailed snow albedo fluctuations, as shown in section 3.2. [14] In the present study we modify the Yamazaki et al. [1991, 1993] scheme by replacing k I with a total absorption coefficient (k all ), one that also accounts for BCD: k all ¼ Ma dust C dust þ Ma HyPhoBC C HyPhoBC þ Ma HyPhiBC C HyPhiBC I þ 1 C dust C HyPhoBC C HyPhiBC 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 currently included in the Goddard Chemistry Aerosol Radiation and Transport model (GOCART) [Chin et al., 2000, 2002; Ginoux et al., 2001; see also ext.gsfc.nasa.gov/ People/Chin/gocartinfo.html], a chemical transport model coupled to GEOS 5. Using realistic MAC values with mass concentrations in equation (6), together with snow density information, a first order representation of BCD on snow albedo can be estimated. Equation (6) is based on the snow impurity factor defined by Aoki and Tanaka [2008], modified here to work with our model. [15] The refractive index data of Warren [1984] ( omlc.ogi.edu/spectra/water/abs/index.html) were used to estimate the reflectivity of ice, r I,; these data were in turn used to determine the ice absorption coefficients k I at each wavelength using equation (2) of Picard et al. [2009]. The MACs used for dust, hydrophobic BC, and hydrophilic BC at 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 BC is assumed to be uncoated by any liquid aerosols, whereas hydrophilic BC is assumed to be coated with sulfate. These optical properties are identical to those applied by Flanner et al. [2009]. [16] Figure 1a shows the spectral snow albedos calculated by the new snow albedo model without snow impurities together with the snow albedos by a Mie scattering based snow albedo model, multilayer Snow, Ice, and Aerosol Radiative model (SNICAR), calculated with the online snow albedo simulator (SAS; based on Flanner et al. [2007]. Our preliminary calculation here assumed five 2 cm thick snow layers and a typical fresh snow density of 110 kg m 3, corresponding to an effective ice thickness of 54 mm (effective snow grain radius of 81 mm for the SAS calculation). The output of our model corresponds well with the Mie scattering based calculation at an SZA of 50. Hence, our snow albedo model well represents diffuse albedos under cloudy conditions, generally corresponding to an SZA of about 50 [Wiscombe and 3of15

4 Figure 1. Spectral snow albedos calculated by the new snow albedo model. (a) Comparison between our model and the Snow, Ice, and Aerosol Radiative (SNICAR) model of Flanner et al. [2007] in the case of no impurities. (b) Spectral snow albedo changes due to snow density changes without impurity. (c) Spectral snow albedo changes due to impurity changes for dust, hydrophobic black carbon (HYPHO_BC), and hydrophilic BC (HYPHI_BC) with constant snow density. Filled circles denote the data observed by Grenfell et al. [1994] over Antarctica, with very little BC, together with standard deviations. Warren, 1980; Warren, 1982]. For comparison, one example of the albedos observed over Antarctica (very clean snow) under cloudy conditions from Grenfell et al. [1994] is also shown in Figure 1a. Their measurements of snow density over Antarctica are not directly comparable to those in midlatitudes because of the higher surface snow density in Antarctica (more than 300 kg m 3 ) resulting from the unique environment of wind speed, lower temperature, and slope inclination [Endo and Fujiwara, 1973]. We will thus need to modify the snow density treatment in the LSM when we apply our snow albedo model to Antarctica in future studies. However, the optically effective snow grain radii of 81 mm, based on the snow density in the model, is close to the 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 indicating different SSAs in the absence of impurities (BCD). The snow density in the figure ranges from 110 to 600 kg m 3. These reductions in snow albedo with increasing snow density, particularly for the NIR, agree well with previous studies [e.g., Wiscombe and Warren, 1980; Aoki et al., 1999]. Note that the snow albedo changes are in fact physically due to SSA changes rather than to snow density changes; the apparent snow density effect is indirect, a result of the relationship in equation (5) between snow density and SSA. [18] Figure 1c shows, for a fresh snow density of 110 kg m 3, the spectral reflectances produced by the model under different impurity concentrations. For each curve we assumed the same impurity concentration in all the snow layers and the effective ice thickness used in equation (4) was 54 mm, corresponding to an effective grain radius of 81 mm. As mentioned in previous studies [e.g., Warren and Wiscombe, 1980; Flanner et al., 2007; Aoki and Tanaka, 2008], variations in the mass concentration of dust and BC affect the VIS albedo far more than the NIR albedo, which is instead more directly affected by SSA changes, which in turn are associated with snow density changes. [19] Overall, the characteristics of the curves across the spectrum in Figure 1 are consistent with those found in previous studies [Wiscombe and Warren, 1980; Warren and Wiscombe, 1980; Grenfell et al., 1994; Aoki et al., 1999; Tanikawa et al., 2006; Flanner et al., 2007; Aoki and Tanaka, 2008]. Hence, we infer that our snow albedo model fairly well captures the response of snow albedo to variations in BCD. [20] Currently, for computational expediency, the GEOS 5 LSM uses only two spectral bands: VIS and NIR. We modify our snow albedo formulation accordingly, using ice reflectivities, ice absorption coefficients, and MACs for dust, hydrophobic BC, and hydrophilic BC averaged over the VIS ( nm) and NIR ( nm) bands (see Table 1). The spectrally weighted mean MAC of BCD for VIS and NIR was assumed to be representative. The MACs of dust for four size bins were averaged. The reflectivities of ice r I for VIS and NIR were estimated to be and 0.017, respectively, based on the data of Warren [1984]. The ice absorption coefficients show large variability over the VIS NIR range [e.g., Grenfell and Perovich, 1981; Warren et al., 2006]. Here we use the data of Warren [1984] to calculate the mean absorption coefficient of ice for the VIS band; some more recent technical updates to these data are available [Warren and Brandt, 2008], but the net effect of these updates is found to be small given the emphasis in our studies on heavily contaminated snow. Given the computed VIS ice absorption coefficient, we determine the NIR ice absorption coefficient that would produce a 4of15

5 Table 1. Absorption Coefficient for Ice (m 1 ) and Mass Absorption Coefficient (m 2 g 1 ) for VIS and NIR VIS NIR Ice Dust Hydrophobic BC Hydrophilic BC broadband SW value of 10 m 1, the broadband value given by Kondo et al. [1988], required to explain observed snow albedos. [21] Note that in the LSM the thicknesses for the three layers differ, with the maximum depth of the top layer being 8 cm. Hence, for implementation of the snow albedo model in the LSM, the D i calculation in equation (2) is modified to allow 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 not include the influence of SZA variations on albedo, instead representing the snow albedos at a SZA of 50 as shown in Figure 1a. However, it is well established that the SZA affects the snow albedo [e.g., Wiscombe and Warren, 1980; Warren, 1982; Aoki et al., 1999]. To account for this, we use some parts of the SZA formulation in equations (6) and (7) of Marks and Dozier [1992]. VIS and NIR snow albedos (Alb VIS and Alb NIR, respectively) are computed as follows: p Alb VIS ¼ Alb cal VIS ffiffiffiffiffiffi r eff 1: ð 1 cos 50 Þ p þ ffiffiffiffiffiffi r eff 1: cos ð Þ; ð7þ pffiffiffiffiffiffi Alb NIR ¼ Alb cal NIR r eff 2: þ 0:1 ð 1 cos 50 Þ pffiffiffiffiffiffi þ 2: þ 0:1 ð1 cos Þ; ð8þ r eff where Alb cal_vis and Alb cal_nir are the VIS and NIR snow albedos calculated with equations (1) (3), r eff is the effective grain radius (mm) calculated with the modified equation (4) (multiplied by 1.5 to change effective ice thickness to effective grain radius), 50 is for an SZA of 50, and is the SZA at the given time step. SZA is calculated with the formulations used in the NCAR LSM [Bonan, 1996]. The diffuse component of snow albedo is calculated assuming = 50 in equations (7) and (8), based on the work of Wiscombe and Warren [1980] and Warren [1982]. If the incoming SW radiation at the surface is less than 30% that at the top of the atmosphere, we assume that the sky is totally covered by clouds, and for such a fully cloudy sky, the corresponding snow albedos are calculated using = 50 (namely equal to Alb cal_vis and Alb cal_nir ). (Note that the snow albedo under cloudy skies can sometimes be larger than the snow albedo computed with an SZA of 50 because, under cloudy skies, more of the surface incident flux 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 for broadband SW is assumed to consist of 80% direct beam radiation and 20% diffuse radiation, in analogy with Melloh et al. [2002] Meteorological and Black Carbon and Dust Data Applied: Sapporo Winter [23] We validated model performance using a series of meteorological 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) at Hokkaido University, Sapporo, the total mass concentrations of the filtered snow samples in the top 0 2 and 0 10 cm of snow were measured, along with the snow albedos for the various wave bands (VIS, NIR, and SW), some meteorological components, and various snow physical parameters by snow pit works [Aoki et al., 2006, 2007]. For the winter, Aoki and Tanaka [2008] measured mass concentrations of elemental (black) carbon (EC or BC) and organic carbon (OC) for the top 0 2 cm of snow samples using a DRI2001 OC/EC Carbon Analyzer and estimated the mass concentration of dust by subtracting the concentrations of EC and OC from the total mass concentration estimated with Nuclepore filters. The observed albedos and snow depths at ILTS were directly compared to the albedos and snow depths simulated by our model. The daily snow depth data at ILTS were linearly interpolated into 1 hourly data. [24] For forcing the LSM a complete set of hourly meteorological measurements was needed. With the exception of downwelling longwave radiation and specific humidity, the hourly meteorological data used to force the model were taken from an Automated Meteorological Station (AWS) at the Sapporo District Meteorological Observatory maintained by the Japan Meteorological Agency (JMA) (hereafter, AWS/JMA), which is approximately 2.7 km away from the ILTS site. The specific humidity was estimated from atmospheric pressure and water vapour pressure. The downwelling longwave radiation, not available from AWS/ JMA, was estimated from air temperature using an emissivity (0.849) derived from temporally available measurements at ILTS. At AWS/JMA, wind measurements were carried out at 59.5 m above ground level; these were converted to 10 m wind values using the following equation [Kajikawa et al., 2004]: U 10 ¼ U 59:5 lnð10=z 0 Þ= lnð59:5=z 0 Þ; ð9þ where z 0 is the surface roughness of a flat snow surface ( m[kondo, 1994]). [25] Thus, in essence, we used the meteorological data collected at the AWS/JMA site to force the LSM, and we compared the simulated snow variables to those measured at the nearby ILTS site, for which snow albedo measurements and impurity information were available. We thus implicitly assume for this study that the relevant meteorological forcing variables at these nearby sites are similar. The meteorological conditions, wind direction, and snow depths at ILTS and AWS/JMA are shown in Figures 2 and 3. In the winter of strong northwest winds dominated, though weak southeast winds were also seen (Figure 3a). Figure 3b shows that the snow depth was similar at ILTS and AWS/JMA during the accumulation period, though 5of15

6 [27] For the first impurity data set (hereafter referred to as the original impurity data and shown in Figure 4a), dust and elemental carbon (BC) values (as total amount) were directly read from Figure 5 of Aoki and Tanaka [2008]. We assumed that in the snow accumulation season (roughly before 9 March), the total BC is composed of only hydrophobic BC because frequent snowfall occurs and the BC has little time to age by liquid aerosols such as sulfate. During the melting season (i.e., after March 9), in contrast, fewer precipitation events occur, and the snow depth decreases rapidly with time [Aoki et al., 2007]; we can therefore assume more aged BC (i.e., hydrophilic BC) during this period. We assume that 40% and 60% of the total BC in each layer is composed of hydrophobic BC and hydrophilic BC, respectively, during the melting season. In addition, a look at Figure 5 of Aoki and Tanaka [2008] shows that BC concentrations during the melting period are almost constant, despite presumably continuous BC deposition; this suggests that some portion of BC is flushed away from the snow surface during the melting season, an idea consistent with the results of Conway et al. [1996], who showed that most hydrophilic soot flushes through the snow via meltwater 10 days after particles are first introduced. Figure 2. Meteorological conditions in winter , Sapporo. All data except for the incoming longwave radiation were observed at an Automated Meteorological Station (AWS) at the Sapporo District Meteorological Observatory maintained by the Japan Meteorological Agency (JMA; AWS/JMA). Long wave radiation is estimated values as mentioned 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 (solid gray line) and relative humidity (solid black line). snow depths at ILTS were greater during the melting season. Snow disappeared completely at ILTS on 4 April 4 and at AWS/JMA on 3 April. Nevertheless, the snow depth characteristics 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 this winter at ILTS. Samplings at 0 2 and 0 10 cm were taken about twice a week together with snow pit works, and the total masses for both 0 2 and 0 10 cm and BCD concentrations at 0 2 cm in those snow samples were also measured [Aoki et al., 2006, 2007; Aoki and Tanaka, 2008]. Note that continuous 1 hourly impurity data are necessary to force the LSM runs in this study, but an assumption of stepwise impurity concentrations (i.e., assuming constant impurity concentrations between the observation times) is not realistic. Dry deposition, wet deposition, new snowfall, and some impurity flushing due to snow melting and rain are expected and may affect snow impurity concentrations between the observation times. Hence, we use a specific interpolation procedure to estimate the concentrations between the observation times. Here we especially considered the effects of snowfall and rain on the impurity data. Although this interpolation process is subject to its own assumptions, we believe that it is better than the stepwise assumption. Figure 3. (a) Wind chart at AWS/JMA and (b) observed snow depths at AWS/JMA and Institute of Low Temperature Science (ILTS) in the winter of For ILTS data, daily data were linearly interpolated into 1 hourly data. 6of15

7 Figure 4a. Original snow impurity data prescribed for run 7 in the land surface model (LSM). (a) Dust, (b) hydrophobic BC, and (c) hydrophilic BC concentrations for three snow layers in the model estimated from data observed by Aoki and Tanaka [2008]. [28] The second and third snow layers were assumed to contain 60% and 40% of the dust and BC concentrations at the top layer, respectively, since the top layer is more contaminated owing to its direct contact with the atmosphere in the case of no precipitation [Aoki et al., 2000; Tanikawa et al., 2009]. For the dust data following a heavy Asian dust deposition on March [Aoki et al., 2006], we assigned 5% and 3% of the dust concentrations in the top layer to the second and third layers, respectively, because the Asian dust should have been deposited on the top snow layer (surface) only. In effect, all of these percentages are tuning parameters that allow us to ensure that the concentrations in the lower layers were lower than the surface concentrations and were not modified much by the dust event, based on the previous studies [Aoki et al., 2000; Tanikawa et al., 2009]. We applied the concentration values from Aoki and Tanaka [2008] during the hours of on the snow pit days when morning measurements were taken. We retained the concentrations on the days of snow pit work until the next day of precipitation before the next observation. On rain days the dust, hydrophobic BC, and hydrophilic BC concentrations were forced to decrease by 10%, 10%, and 50%, respectively, to reflect the enhanced efficiency of flushing of the hydrophilic BC, which has a higher mobility according to very limited studies [Conway et al., 1996; Flanner et al., 2007]. Note that these percentages are also tuning parameters; future analysis should give us better values. On snow days the dust, hydrophobic BC, and hydrophilic BC concentrations in the top two snow layers were forced to decrease to their minimal concentrations ( fresh snow ) during the winter. The third layer was assumed to remain unaffected by rain or snow. Impurity concentrations between a rain or snow event and the following next impurity measurement 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 top snow layer depth of the LSM was mostly 8 cm as a maximum value. This suggests the need for some adjustments to the impurity concentrations. For the second impurity data set we adjusted the concentrations of BCD in the first data set by observation based factors. We first computed the mean ratio of total mass concentrations in 0 10 cm to that in 0 2 cm from Aoki et al. [2006], excluding periods (e.g., after new snowfall) for which the ratio exceeded 1, and we then multiplied this ratio by 1.25 (= 10 cm/8 cm) to yield, for dust, a factor of for the period prior to 9 March and of after 9 March. The lower ratio for the melting period is due to the heavy Asian dust deposition on March [Aoki et al., 2006]. However, the BC concentrations did not change as much from the Asian dust event [Aoki and Tanaka, 2008], and thus a single factor of was applied to BC. The adjusted snow impurity data for 0 8 cm is hereafter referred to as the reduced impurity data as shown in Figure 4b, and it is presumably the best 1 hourly impurity data set available to force the LSM Sensitivity Tests [30] For consistency with the ILTS site the GEOS 5 Catchment LSM, modified with our new snow albedo formulation, was run with ground cover vegetation parameters. The available hourly forcing was used at each 20 min simulation time step within the hour. In the simulations the allowable snow density range in the model simulation was set to kg m 3 to maintain stability in model energy balance calculations; this snow density range is realistic for most glacial and seasonal snow surfaces and even for ice sheets [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 the ILTS Sapporo site during the winter of (Table 2). Runs 0 and 1 were performed with the original snow albedo model of Stieglitz et al. [2001]. In run 0 the default maximum snow albedos for VIS and NIR were 0.7 and 0.5, respectively, and the default minimum values were 0.5 and 0.3 for VIS and NIR, respectively. In run 1 the maximum snow albedos for VIS and NIR were artificially increased to 1.0 and 0.8, respectively. For both runs a fixed SZA of 60 was used, but only for the calculation of vegetation albedos; the original snow formulation does not include an SZA effect. Both runs assumed clear sky conditions, with (for snow) 7of15

8 Figure 4b. Same as Figure 4a, but for the reduced snow impurity data prescribed for runs 3 6 in the LSM. equal albedos assumed for the diffuse and direct radiation components. [32] Runs 2 and 3 used the new snow albedo formulations, including the impact of SZA variations and assuming that 80% of the incoming radiation is direct for clear sky conditions. Cloudy sky conditions were calculated with an SZA of 50. Run 2 assumed no BCD impurities. Run 3 used the reduced impurity data set (Figure 4b), our best estimate from the field site. Accordingly, run 3 represents our best hope for an accurate snow albedo calculation. [33] Runs 4 7 examine the response of albedo to various modifications in the new formulation. In run 4 the effect of snow melting on SSA was disabled. In run 5 the effect of vegetation on snow albedo was disabled. In run 6 the dust concentrations in snow were set to 0 mg kg 1 to examine how BC by itself affects snow albedos. Finally, in run 7 the original mass concentration data in Figure 4a were used order to investigate a case of greater snow contamination. (Again, the original mass concentration data set is more contaminated because the scaling process described in the previous section, which converts the high mass concentrations for 0 2 cm to corresponding Table 2. Model Settings for Sensitivity Test a Run Albedo Model Impurity in Snow Solar Zenith Angle Sky Condition Direct and Diffuse Albedos Melt Effect on Snow Albedo Additional Note on Settings 0 Original None Only VG (60 fixed) All clear sky Equal None Maximal albedos for snow: VIS = 0.7, NIR = Original None Only VG (60 fixed) All clear sky Equal None Maximal albedos for snow: VIS = 1.0, NIR = New None Both SN and VG All including cloudy 80% dir. and 20% diff. 0.6*SSA Same as 3, but no impurity 3 New Reduced impurity data Both SN and VG All including cloudy 80% dir. and 20% diff. 0.6*SSA 4 New Reduced impurity data Both SN and VG All including cloudy 80% dir. and 20% diff. None 5 New Reduced impurity data Both SN and VG All including cloudy 80% dir. and 20% diff. 0.6*SSA No vegetation effect on snow albedo 6 New Reduced impurity data Both SN and VG All including cloudy 80% dir. and 20% diff. 0.6*SSA No dust case (C dust =0) 7 New Original impurity data Both SN and VG All including cloudy 80% dir. and 20% diff. 0.6*SSA a SN, snow; SSA, specific surface area; VG, vegetation. 8of15

9 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 lines denote the observed snow depth at ILTS. Pink shading denotes the snow depth calculated in the LSM. lower values for 0 8 cm, was not performed for this data set.) 3. Results and Discussion 3.1. General Characteristics [34] Table 2 outlines the simulations performed with the original 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 snow albedo, which was directly measured at noon at ILTS. First, however, a word of caution: the Catchment LSM characterizes snow cover across an area, and thus the simulated albedos reflect both vegetation masking and partial snow cover. During early winter and the melting season these effects tend to decrease simulated snow albedo in a way that cannot be captured by highly localized radiation measurements. Thus, in the following analyses, we focus on albedos and snow depth generated after 14 January (Figures 6 9 and Table 3), when these effects are mitigated (Figure 7): by this date the Catchment LSM is effectively well covered with snow. We, nevertheless, look at albedos during the snowmelt season because snow behavior during this period is strongly affected by impurities; for this season the potential scale mismatch must be kept in mind. In Figures 6 8 and Table 3 we also do not consider nighttime hours or periods for which, presumably owing to measurement error, the observed snow albedos are greater than 1 or less than 0 or the 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 albedo of fresh snow. In run 1 (Figure 5b) the maximum snow albedos were artificially increased to 1.0 and 0.8, respectively, allowing the original model to capture the general behavior of snow albedos during the accumulation season. However, these artificial changes introduced a strong bias in the melting season, with overly high snow albedos that force the snowpack to remain too long. (Compare the solid pink shading, representing simulated snow depth, to the sky blue line, 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 are assumed to be absent. The most striking result from this simulation is the emergence of an ability to generate real- 9of15

10 Figure 6. Effect of snow melting on 1 hourly NIR snow albedos after 14 January Circles denote areas of large difference between runs 3 and 4. istic looking variations in NIR snow albedo, variations associated with snow density and snow particle size. Note that hourly fluctuations in snow albedos (not shown) are also seen in run 2, owing to variations in SZA, an effect not captured in the original snow model. Despite these improvements, however, snow albedos for VIS are much too high in the melting season. Correspondingly, snow depth, while well simulated during the accumulation season, remains too large during the snowmelt season (i.e., after 9 March), with the snowpack surviving too long. With the addition of impurities, these melting season biases may be reduced, as discussed next. [37] In run 3 (Figure 5d) we used our best estimates for the reduced BCD concentrations in the new snow albedo model. The resulting melt season snow albedo and snow depth are considerably improved. Although small amounts of snow still exist in the last time step, run 3 shows a more reasonable timing of snow disappearance than runs 0 2. The simulated VIS snow albedos in the snow accumulation periods are improved as well. The drastic snow albedo reduction after the Asian dust deposition on March [Aoki et al., 2006] is also well reproduced. Nevertheless, the calculated albedos and snow depths during the last few days of the melt season still differ from the observations, possibly owing to errors in the estimation of BCD concentrations, snow physical properties at ILTS (see section 3.3.), or the aforementioned disconnect in spatial scale between the model s representation of snow and the localized observation site. [38] Run 4 (Figure 6) shows the impact of disabling the effect of snow melting on SSA. The increase in the NIR albedos during the melt season is clear. The snow depth accordingly increases in the melting season relative to run 3 (not shown). From run 5 we find that the effect of disabling vegetation masking on the VIS snow albedo is largest in the initial snow accumulation period (not shown) and is smaller after that (Figure 7). During fully snow covered periods the vegetation effect was smaller for the NIR albedos (not shown). During the melting period the vegetation effect on the VIS snow albedo in Figure 7 was generally small and outweighed by the effects of snow impurities (Figures 5c and 5d), suggesting that our new impurity formulation is especially important in the melting season. In our final two experiments we examined the BC effect in isolation (run 6, Figure 8) and a more contaminated snow case (run 7; Figure 9). In general, these effects on VIS albedos during the melting period were larger than those of vegetation masking. [39] The root mean square errors (RMSEs) between the observed (at ILTS) and simulated data were computed for the SW, VIS, and NIR snow albedos and for snow depths (Table 3). In general, the smaller RMSEs were obtained with the new snow albedo model using impurity data. This reaffirms the need for including the effect of snow impurities together with SSA and SZA effects in the new snow albedo model. Using the reduced impurity data along with our best estimates of the model parameters (run 3), the RMSEs for SW, VIS, and NIR albedos are 6.2%, 6.9%, and 7.7%, respectively. For snow depths the estimated RMSEs in runs 3 7 were less than 12.1 cm for ILTS, with run 3 producing an RMSE of 9.3 cm. Run 7 produced a slightly better snow depth RMSE (8.2 cm), which may suggest some estimation error for the snow impurity concentrations, but the difference in RMSEs between runs 3 and 7 are small, and run 3 outperforms run 7 in terms of lower snow albedo biases. [40] The RMSE differences between runs 3 and 7 were largest for VIS albedo, and those between runs 3 and 4 were largest for NIR albedo. This suggests that VIS albedo is more strongly affected by variations in BCD mass concentration than by vegetation or snow physical properties (here, SSA), whereas NIR albedo is more strongly affected 10 of 15

11 Figure 7. Effect of vegetation (run 3) and no vegetation (run 5) on 1 hourly VIS snow albedos after 14 January by variations in the snow physical properties, consistent with 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 as strongly affected by snow impurity changes 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 decreases the SSA and increases the optically effective snow grain size. Kondo et al. [1988] compared calculated and observed snow albedos in Shinjo, Japan. They found that their calculated albedos during the melting period could not match the observed values without multiplication of the SSA by 0.6, even if they added an absorption effect associated with snow darkening. Accordingly, in our calculations we instantly multiply the calculated SSA by 0.6 when the snow temperature 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 too high (Figure 6). The snow albedos for NIR are largely affected by snow physical parameters (SSA through snow density 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; Aoki et al., 2000; Aoki and Tanaka, 2008]. The results from run 3 compared with those from run 4 suggest that the new snow model captures the albedo related physics of snow melting (Figure 6). [43] The impact of snow melting on the VIS snow albedos was small during the snow accumulation period but relatively large in the melting season (not shown). This could be easily 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 impurity data in Figure 4b). The product of the total absorption coefficient and the effective ice thickness then becomes much larger than that in the snow accumulation period, leading to a higher rate of decrease in the exponential function. In terms of snow physics this means that snow melting accelerates the snow aging process and reduces snow albedos Impact of Impurity Mass Concentration Changes on 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 on NIR snow albedos were seen to a small extent in the melting period because of the small absorptions of BCD for NIR (Table 1). We were fortunate to have observations [Aoki et al., 2006] of total mass concentrations in 0 2 and 0 10 cm snow at ILTS, which included information about a heavy Asian dust deposition event on March, and we also had access to read the figure from a paper on direct measurements of BCD concentrations [Aoki and Tanaka, 2008]. However, snow was sampled only about twice a week, together with snow pit works [Aoki et al., 2006, 2007]. Although we took into account as best we could the impacts of snowfall and rain on BCD concentrations, impurity concentration variations at other times between snow samplings and after 26 March are still unknown. This is a limitation of this study. The difference in RMSEs between runs 3 and 7 for VIS albedos (Table 3) suggests a potential error associated with uncertainty in impurity concentration. Even with these uncertain concentration estimates, however, the snow albedo variations during the melting season are better reproduced with the new 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 11 of 15

12 Figure 8. Comparison between run 3 (reduced impurity) and run 6 (no dust) on VIS snow albedos after 14 January Circle denotes the notable decrease in VIS albedos discussed in the text. (Figure 9), approaching the observed values at ILTS. This suggests that our estimation of the reduced snow impurities for the latter part of the melting period in run 3 may be underestimated. However, this impurity difference by itself cannot fully explain the rapid decrease in snow depth in the last couple of days of the melting period (as encircled in Figure 9) together with the rapid decreases in VIS and NIR albedos (Figures 6 8). [46] Some potential explanations can be offered for the inability of the model to reproduce the rapid decreases in the observed snow depth and albedos at ILTS in the last couple of days of the melting season: (1) the inconsistency in scale between the LSM and the point observations, as manifested, for example, in the treatment of patch like melting; (2) the discrepancy between AWS/JMA and ILTS meteorology (wind velocity, snow drift, etc.); and (3) some other snow model deficiency (e.g., a clear submerged snow layer at the bottom of the snowpack, like a water pond, was observed at ILTS after the end of February (Figure 1a of Aoki et al. [2007]), whereas the LSM assumed vegetation under snowpack). At the present time, we cannot say which factor is most important. Future progress on modeling and more detailed observations will be necessary to address this. 4. Conclusions and Summary [47] In this study we have introduced a new snow albedo scheme that incorporates BCD effects into the snowpack model of Stieglitz et al. [2001], the baseline snow model used in the NASA GMAO land surface model in GEOS 5 [Ducharne et al., 2000; Koster et al., 2000]. Validation of the model was performed with observations taken at ILTS in Sapporo, Japan, during the winter of , with forcing meteorological data taken from a nearby AWS/JMA Table 3. RMSE Between Observed and Calculated Values in Each Sensitivity Test a Figure 9. Differences in snow depths at ILTS and the results of run 3 (reduced impurity) and run 7 (original impurity) after 14 January Circle denotes the same notable point as in Figure 8, discussed in the text. RMSE Run SW VIS NIR Snow Depth at ILTS (m) a ILTS, Institute of Low Temperature Science. Boldface numerals denote minimal RMSEs. 12 of 15