Possible Effect of Anthropogenic Aerosol Deposition on Snow Albedo Reduction at Shinjo, Japan

Transcription

1 Journal of the Meteorological Society of Japan, Vol. 83A, pp , Possible Effect of Anthropogenic Aerosol Deposition on Snow Albedo Reduction at Shinjo, Japan Hiroki MOTOYOSHI Space Service Co., Ltd., Tokyo, Japan Teruo AOKI Meteorological Research Institute, Tsukuba, Japan Masahiro HORI Earth Observation Research and Application Center, Japan Aerospace Exploration Agency, Tokyo, Japan Osamu ABE and Shigeto MOCHIZUKI Shinjo Branch of Nagaoka Institute of Snow and Ice Studies, National Research Institute for Earth Science and Disaster Prevention, Shinjo, Japan (Manuscript received 27 September 2004, in final form 9 February 2005) Abstract We observed broadband snow albedos in the visible and the near infrared spectral regions with snow pit works of several-day intervals, during the winters of 2001/2002 and 2002/2003 at Shinjo, Japan. We examined the dependence of albedos on snow grain size and on concentration of snow impurities, comparing observations and theoretical calculations using a radiative transfer model for atmosphere-snow system. The comparisons revealed that the snow was contaminated by strong absorptive impurities such as soot additional to moderate absorptive impurities such as mineral dust. Snow albedo reduction after snowfalls (snow aging effect on albedo) observed in both spectral regions corresponded to the growth tendency of snow grains and the increasing concentration of snow impurities with elapsed time after snowfalls. Measurement of the atmospheric aerosols above the snow surface using a laser optical particle counter suggested that wet deposition of atmospheric aerosols caused snow impurities of more than 1 ppmw in mass concentration. 1. Introduction Snow albedo plays an important role in climate change in the cryosphere through the surface energy budget of snow cover. The value Corresponding author: Hiroki Motoyoshi, c/o Physical Meteorology Research Department. Meteorological Research Institute, 1-1 Nagamine, Tsukuba , Japan. hmotoyos@mri-jma.go.jp ( 2005, Meteorological Society of Japan of snow albedo, which is relatively high in the visible spectral region and low in the near infrared region, changes according to various snow, atmosphere, and solar conditions (Warren 1982). For a flat surface on sufficiently deep snow, the albedo essentially depends on the snow grain size and the concentration and optical properties of the snow impurities. The albedo in the visible spectral region is reduced by increased impurities and the reduction is enhanced by increased grain size, while the reduction of the albedo in the near infrared re-

2 138 Journal of the Meteorological Society of Japan Vol. 83A gion is mainly caused by increased grain size (Wiscombe and Warren 1980; Warren and Wiscombe 1980). The phenomenon of snow albedo reduction with elapsed time after snowfall is generally known as snow aging effect on snow albedo (Dirmhirn et al. 1975; Baker et al. 1990; Sydor et al. 1979). Such albedo reduction is essentially caused by the changes of snow physical parameters through the following processes. The snow grain size varies with metamorphosis of the snow grains by sintering and melting after snowfall. The impurities are supplied from atmospheric aerosols through wet deposition during snowfall, including rainout and washout by falling snow, and are deposited through dry deposition after snowfall. As absorptive snow impurities, the predominantly observed component is mineral dust and it contributes to the albedo reduction in the visible region because of its comparatively high absorptivity (Warren and Wiscombe 1980). The most absorptive component among the atmospheric aerosols is soot, which is generally anthropogenic aerosol, and thus the soot incorporated into snow has a marked effect on the snow albedo reduction even in small concentration (Warren and Wiscombe 1980; Warren and Clarke 1986). This effect is important in studies of climate change, because the reduced snow albedo caused by soot particles will have a positive climate forcing. Hansen and Nazarenko (2004) estimated the climate forcing by soot through snow and ice albedos to be þ0.3 W m 2 in the Northern Hemisphere, adopting plausible estimates for the effect of soot on albedos in their global climate model simulation. Recent observations of Asian dust aerosol reported the increased absorption of mineral dust implies the pollution with soot (e.g., Kinne and Pueschel 2001; Chou et al. 2003). For accurate climate simulation, it is very important to construct a model that can precisely estimate variations in snow albedo. Many current land surface models or climate models implicitly estimate the effects of grain size and impurities through empirical snow albedo models (e.g., Dai et al. 2003; Yamazaki 2001). The physically based albedo model by Marshall and Oglesby (1994) treats snow grain size and impurity concentration as explicit parameters for snow albedo. Aoki et al. (2003) pointed out that such a model should be connected with the snow layer model, which can accurately predict snow grain size, and with the transport model, which can simulate the transport and deposition of atmospheric aerosols on the snow. They had continuously observed the broadband albedo with snow pit works in the winters of 1999/2000 and 2000/ 2001 at Kitami, a dry snow area in Japan. More direct comparisons of snow albedos with the physical parameters will be required to construct such a physically based snow albedo model. We continuously observed the broadband albedo and made snow pit works frequently to monitor the snow physical parameters in the winters of 2001/2002 and 2002/2003 at Shinjo, a wet snow area in Japan. One objective of this study is to investigate the effect of dust and soot impurities on the snow albedo through continuous measurement of the albedos and direct observation of the snow physical parameters at the observation site that may be influenced by anthropogenic aerosols. Another objective is to discover the relationship between the snow impurities and their source in atmospheric aerosols comparing the number of atmospheric aerosol particles above the snow surface measured using an optical particle counter (OPC) and the concentration of impurities obtained by snow pit works. An additional objective is comparing the tendency of albedo reduction after snowfall of our observations at Shinjo, with those observations at Kitami by Aoki et al. (2003). 2. Observation site and measurement We took all field measurements during the winters of 2001/2002 and 2002/2003 at the meteorological observation field ( N, E, 127 m above sea level) of the Shinjo Branch of the Nagaoka Institute of Snow and Ice Studies (National Research Institute for Earth Science and Disaster Prevention) in Yamagata, Japan. The observation site is in a suburb about 3 km from the downtown area of Shinjo City. The city has a population of about 42,000 and is located in the Shinjo Basin, a wet snow area with comparatively heavy snowfall. Clouds developed over the Sea of Japan produce the frequent snowfalls (Nakamura and Abe 1998). We measured both upward and downward

3 March 2005 H. MOTOYOSHI et al. 139 components of radiant flux densities using pyranometers (CM-21, Kipp & Zonen) in the shortwave region (l ¼ mm) and in the near infrared region (l ¼ mm). We calculated the flux densities in the visible region (l ¼ mm) as the differences between the shortwave and the near infrared flux densities. We calculated the broadband albedos for each spectral region by dividing the upward flux density by the downward flux density. In this analysis, we used the 30-minute average of the albedos data measured each 10 second from 11:30 to 12:00 LT near the local solar noon. The albedo data for snow less than 30 cm in depth were excluded to avoid the influence of withered grass on the underlying surface. The measured meteorological components included air temperature, precipitation with a rain gauge, snowfall intensity with a snowfall sensor, and snow depth with a laser snow gauge. We also measured the number densities of the atmospheric aerosol particles above the snow surface using a laser OPC (TD- 100, SIGMATEC) with six channels corresponding to aerosol particles larger than 0.3,, 1, 2, 3, and 5 mm in diameter. The instrument, installed in the observation hut, sampled air with a flow rate of m 3 min 1 about 2.5 m above the ground. Snow pit works at the same observation field were conducted as frequently as one or two days per week, 47 times in total. The snow type and the grain size for each layer were measured at snow pits. The term snow grain size refers to the optically equivalent snow grain radius r 2 defined by Aoki et al. (1998, 2000), which is defined as the half width of the branch for dendrites, or of the narrower portion for broken crystals, or of each grain for the aggregate granular grains. The snow samples were taken at the snow pits from the surface to a depth of 5 cm. We used a two-stage filtrating system with filter pores of 5.0 mm and 0.2 mm successively (Nuclepore Track-Etched Membrane) to measure the concentration of impurities in the snow samples. The concentration of snow impurities were calculated as the mass fraction of the water-insoluble snow impurities to the filtered melted snow. Figures 1a and 1b present the daily variations of air temperature and solar zenith angle at 11:45 LT during the winters of 2001/2002 and 2002/2003. The solar zenith angle at 11:45 LT varied from 34.7 to 62.3 between December 1 and March 31. Although the observation site was covered by snow for at least three of these four months, the maximum air temperature was rarely negative and rainfall was recorded even in snowfall season. Figures 1c and 1d plot the variations of the snow depth and the broadband albedos in the visible and the near infrared spectral regions. The decreasing trends around the end of February in both winters indicate snowmelt seasons. We defined the period from December to February as the snowfall season and March as the snowmelt season in this study. The steep rises in snow depth indicate snowfalls, which were seen mostly in snowfall seasons with a few in snowmelt seasons. The albedos in both spectral regions tended to decrease with elapsed time after snowfalls (snow aging effect on the albedos). Figure 2 depicts the snow physical parameters obtained at the snow pits. The snow grain size shown in Fig. 2 is essentially the measured value for the first (top) layer except when the first layer was less than cm or classified as surface crust or ice layer. In those cases, we plotted the snow grain size measured in the second layer. In such layers at the snow pits, we only observed two snow types, new snow and granular snow. The grain size of new snow tended to be smaller than that of granular snow. In snowmelt seasons, both the snow grain size and the concentration of snow impurities tended to increase until the end of the season. 3. Snow physical parameters and broadband albedos In this section, we examine the effects of the snow physical parameters on the broadband albedo by comparing the observed albedos with the theoretically calculated ones, by using the multiple scattering model of the radiative transfer for the atmosphere-snow system (Aoki et al. 1999; Aoki et al. 2000). The atmospheric model includes mid-latitude winter with 15 atmospheric layers (Anderson et al. 1986), urban aerosols with an optical thickness of 0.2 at l ¼ 0:5 mm, and water clouds with an optical thickness of 40 at l ¼ 0:5 mm for cloudy conditions. The snow layer model was parameterized by the effective radius of snow particles ðr eff Þ

4 140 Journal of the Meteorological Society of Japan Vol. 83A Air temperature (deg) θ 0 Shinjo, 2001/2002 T max T mean T min (a) θ 0 Shinjo, 2002/2003 T max T mean T min (b) Solar zenith angle (deg) Broadband albedo Snow depth VIS albedo NIR albedo Dec Jan Feb Mar Date (c) Snow depth VIS albedo NIR albedo Dec Jan Feb Mar Date (d) Snow depth (cm) Fig. 1. Variations of air temperature in daily mean ðt mean Þ, maximum ðt max Þ, and minimum ðt min Þ values, and solar zenith angle y 0 at 11:45 LT for (a) 2001/2002 winter, and (b) 2002/2003 winter. Variations of 30-minute average of the broadband snow albedos near the local solar noon for the visible (VIS) and the near infrared (NIR) regions, and snow depth at 12:00 LT for (c) 2001/2002 winter and (d) 2002/2003 winter. The invalid data of the albedo due to frost or snow on the grass domes of the pyranometers are excluded from (c) and (d). and the mass concentration of snow impurities ðcþ. We assumed the snow particles to be spheres and also assumed Mie scattering for the single scattering of radiation by a snow particle. For simplicity, we assumed a singlelayer structure for the snowpack and set the large snow water path to represent sufficiently deep snow. The fundamental model parameters to be compared with the observation were r eff, c, the solar zenith angle ðy 0 Þ, and the sky condition (clear or cloudy). We adopted the mineral dust aerosol model of the coagulation mode with a mode radius of 1.9 mm (Hess et al. 1998) to represent the optical properties of moderate absorptive snow impurities, including both distant and locally emitted aerosol particles (Aoki et al. 2003). We used the soot model of Shettle and Fenn (1979) for the optical properties of soot impurities as additional strongly absorptive component in the snow impurity model in addition to the mineral dust. To determine the existence of soot contamination in the observed snow impurities, we calculated the broadband albedos using three types of snow impurity models, differing in the mixture of soot and mineral dust impurities. (1) The Dust-Only model assumes the ice particles were pure and the mineral aerosol particles were externally mixed. Snows that were more contaminated by soot impurities were simulated by (2) the DustþSoot0.2ppmw model and (3) the DustþSootppmw model. Both assumed that ice particles with internally mixed soot impurities of 0.2 ppmw and ppmw in mass concentration, and externally mixed dust par-

5 March 2005 H. MOTOYOSHI et al. 141 Snow grain size r 2 (µm) Snow grain size r 2 (µm) New snow Granular snow Shinjo, 2001/2002 New snow Granular snow Shinjo, 2002/2003 Dec Jan Feb Mar Date Fig. 2. Snow grain size ðr 2 Þ and concentration of snow impurities ðcþ measured in snow pits for (a) 2001/2002 winter and (b) 2002/2003 winter. For snow grain size, horizontal error bars show the range of measured maximum and minimum values. The averages of snow grain sizes are denoted with crosses for new snow and circles for granular snow. ticles as in the Dust-Only model. For the refractive indices of soot-contaminated ice particles, we averaged the refractive indices for ice and soot with the weight of the volume fraction of each component (Choudhury et al. 1981; Leroux et al. 1999). The soot aerosol particles, which have very small terminal velocity due to their small mean radius and density, seem rarely to contribute to the dry deposition through the gravitational settling and thus should be brought into the snow mainly through the wet deposition during snowfall. We used the internal mixture of the soot impurities to represent the process of the wet deposition of the soot aerosol particles. We used the external mixture of the dust impurities to represent the process of the dry deposition of the dust aerosol (a) (b) Concentration of impurities (ppmw) Concentration of impurities (ppmw) particles, especially the locally emitted ones with comparatively large mean radius. The internal mixture model of impurities resulted in lower visible albedos than those calculated by the external model of impurities for the same concentration (Choudhury et al. 1981). Therefore, the visible albedo calculated using DustþSoot0.2ppmw or DustþSootppmw would yield the lowest estimation of the visible albedo for soot contaminated snow. For these snow impurity models, the total concentration of impurities c comparable to the observation is expressed as c ¼ c d þ c s (ppmw), where c d is the concentration of externally mixed mineral dust particles and c s is the concentration of internally mixed soot impurities. The value of c s is fixed as 0.0 ppmw for Dust-Only, 0.02 ppmw for DustþSoot0.2ppmw, and ppmw for DustþSootppmw, and c d is a tunable parameter in the comparison with the observed total concentration of impurities. Figure 3 illustrates the dependence of the broadband albedo on the concentration of snow impurities c. Figure 4 depicts its dependence on snow grain size r 2 for the observed value and r eff for the model calculation. The three theoretical curves indicate the calculations using Dust-Only (solid line), DustþSoot0.2ppmw (dotted line), and DustþSootppmw (dashed line) models. For each model, the region bounded by the upper and lower curves implies the range of the possible albedo values during the observation period estimated by the model calculations. Varying the input parameters for calculations within the range of the measured physical quantity during the observation period (i.e., clear or cloudy for sky condition, y 0 ¼ 36 to 63, l ¼ 0:71 to 138 ppmw, and r eff ¼ 15 to 750 mm) determines these minimum and maximum curves. For the visible spectral region, the calculated albedos depend on both c and r eff (Figs. 3a and 4a) as well as on the concentration of the soot mixture because of the strong absorptivity of soot particles. The differences in the calculated visible albedos among the three impurity models are more remarkable in the large grain region. The theoretical near infrared albedo depends on the snow grain size r eff (Fig. 4b), while it remains almost unaffected by the concentration of snow impurities (Fig. 3b). The relative contributions of dust and soot to the absorption decrease with wavelength due to

6 142 Journal of the Meteorological Society of Japan Vol. 83A Visible albedo Near infrared albedo r 2 50 m 50 m <r m 250 m <r m 500 m <r 2 Dust-Only Dust+Soot0.2ppmw Dust+Sootppmw r eff =15µm, θ 0 = 63, clear r eff = 750 µm, θ 0 =39,clear r eff =15µm, θ 0 = 63, cloudy r eff = 750 µm, θ 0 =39,clear (a) (b) 10 0 Concentration of snow impurities c (ppmw) Fig. 3. Relationship between concentration of snow impurities c and broadband snow albedos in (a) the visible region and (b) the near infrared region. The plotted data are the albedo near the local solar noon of the days on which snow pit works were made, and are classified by the range of observed snow grain size r 2. The theoretically calculated maximum and minimum albedo values for this observation were estimated from the range of the observed physical parameters, and are drawn as the upper and lower curves for each impurity model, Dust-Only (solid line), DustþSoot0.2ppmw (dotted line), and DustþSootppmw models (dashed line). The physical parameters varied in the determination of the maximums and minimums of the albedos are the effective radius r eff, the solar zenith angle y 0, and clear/cloudy sky conditions. Visible albedo Near infrared albedo Observation Dust-Only Dust+Soot0.2ppmw Dust+Sootppmw c =ppmw,θ 0 = 63, clear c =138ppmw,θ 0 = 39, clear c =ppmw,θ 0 = 63, cloudy c =138ppmw,θ 0 = 39, clear Snow grain size r 2, r eff ( m) Fig. 4. Relationship between snow grain size and broadband snow albedos in (a) the visible region and (b) the near infrared region. The horizontal axis represents snow grain size r 2 for the observed data and effective radius r eff of the snow particles for the theoretical curves. The meanings of the line types of the theoretical curves are as same as in Fig. 3, while the physical parameters varied in the determination of the maximums and minimums of the albedos are the concentration of snow impurities c, the solar zenith angle y 0, and clear/cloudy sky conditions. the rapid increase of the absorptivity of ice with wavelength. As a result, the effect of snow impurities on spectral albedo is very small in the near infrared region and becomes almost zero at l > 1:0 mm in our observations (Warren and Wiscombe 1980). The observed visible albedo in Fig. 3a tends to decrease with c. Most observed albedos are distributed below the possible range predicted by the Dust-Only model, and agree with the albedo ranges calculated by the DustþSoot0.2 (a) (b)

7 March 2005 H. MOTOYOSHI et al. 143 Concentration of impurities (ppmw) Snow grain size r 2 (µm) (a) (b) Dec-Feb Mar Dec-Feb Mar Elapsed time after snowfall t e (hour) Fig. 5. (a) Concentration of snow impurities and (b) snow grain size as functions of the elapsed time after snowfall ðt e Þ. White circles represent the data from December to February and crosses represent the data in March. For snow grain size, vertical error bars indicate the range of measured maximum and minimum values; and the in situ estimated average sizes of snow grains are denoted with white circles or crosses. t e are determined based on the time when the snow pit works were conducted. The data at the time whose t e is not defined are excluded. ppmw or the DustþSootppmw models. The dependence of the observed visible albedo on the snow grain size in Fig. 4a also falls within the range between the upper and lower curves of the DustþSoot0.2ppmw model, especially in the large snow grain size region. This result clearly shows that the snow contamination contained more absorptive impurities such as soot, additional to dust impurities. For the near infrared spectral region, the observed albedos fall well into the ranges of the calculated albedos for all three impurity models as seen in Figs. 3b and 4b. The observed near infrared albedo in Fig. 3b seems only slightly dependent on c. However, such dependence of near infrared albedo on c was actually caused by the relationship between r 2 and c that r 2 tended to increase with the increase of c as seen in Figs. 2a and 2b, especially in snowmelt seasons. The visible albedo data with small r 2 in Fig. 3a, denoted by plusses, typically corresponds to the snow type of new snow (Fig. 2). The visible albedo with these range of r 2 should be located just below the upper curve for a model that represents the maximum value of the albedo for c within essentially the observed range of the grain size. The observed visible albedos for fresh snow in Fig. 3a are located well below the upper curve for the Dust-Only model. This implies the possibility of the soot contamination in the newly fallen snow was deposited during the snowfall. 4. Broadband albedos and elapsed time after snowfall To understand the variation of the snow physical parameters and the reductions of the broadband albedos after snowfall, we defined the elapsed time after snowfall ðt e Þ using the definition from Aoki et al. (2003): the elapsed time is the period beginning when the snow depth reaches a local maximum depth after exceeding the last local minimum depth by 3 cm. The elapsed time after a snowfall is measured until the beginning of the next increase of snow depth exceeding 3 cm over the local minimum depth. Figure 5a depicts the concentration of snow impurities c as a function of t e. For the snowfall season from December to February, c increased from about 1 ppmw to about 10 ppmw with t e. The increase of c is mainly due to both dry deposition of atmospheric aerosols and the concentration of snow impurities due to sublimation or melting of the snow in the top layer. The measured values of c exceeded than 1 ppmw even just after a snowfall, implying the existence of wet deposition of atmospheric aerosols. In the snowmelt season, c exceeded 10 ppmw just after a snowfall and increased with t e. We will discuss the contribution of wet and dry depositions of atmospheric aerosols again in

8 144 Journal of the Meteorological Society of Japan Vol. 83A Broadband albedo 0.4 VIS NIR Broadband albedo VIS NIR Elapsed time after snowfall t e (hour) Dec Jan Date Feb Mar Fig. 6. Broadband snow albedos in the visible (VIS) and near infrared (NIR) regions as a function of elapsed time after snowfall t e. The plotted data are the albedos near the local noon of the days during each runs of the elapsed time after snowfall. Fig. 7. Variations of broadband albedos in the visible and the near infrared regions observed within 12 hours elapsed time after snowfall. These data are from the region of t e a 12 hours in Fig. 6. the next section using OPC data. Figure 5b shows the snow grain size r 2 plotted as a function of t e. The increasing tendency of r 2 is noticeable, especially for t e from 0 to 50 or 100 hours. The increased snow grain size observed in this measurement was caused by the snow metamorphism from new snow to compacted or granular snow through the sintering or melting process. Figure 6 illustrates the relationship between the broadband albedos and the elapsed time after snowfall. Figure 6 plots all measured values of the albedos during each run. For both spectral regions, the reduction of the albedos after snowfall is an overall trend. The remarkable reduction of the near infrared albedo for t e region between 0 and 50 or 100 hours is consistent with the increase of r 2 in Fig. 5b. The reduced visible albedo is also consistent with the increasing trend of c in Fig. 5a. The vertical spreads of the albedo shown in Fig. 6 are mainly due to the variation of the physical snow parameters. However, the reduction could also be affected by other conditions such as the atmospheric conditions and the solar zenith angle. The vertical spreads are remarkable in the starting point of each run, even for t e near 0 hours. Figure 7 plots the broadband albedos measured within 12 hours after snowfall to enable us to understand the seasonal variation of albedos for such fresh snow surfaces. The albedos in both spectral regions tend to decrease toward the end of the snow season. The decreased near infrared albedos are caused by the rapid metamorphic growth of the snow grain size due to the comparatively high temperature in snowmelt season. Every day in Nakazato, a heavy snowfall area in Japan similar to Shinjo, Kanda et al. (2002) observed the increased wet and dry deposition of water-insoluble particles in the snowmelt season by directly collecting precipitation and dry deposition data. The increase of snow impurities by such increased wet deposition and the suppression of visible albedo due to the increased size of snow grains (Fig. 3a) probably caused the decreased visible albedo in Fig. 7. These seasonal changes in the physical parameters of the fresh snow surface correspond to the large values of snow grain size and concentration of snow impurities in the snowmelt season, as shown in Fig. 2. Table 1 summarizes the linear regression coefficients for the albedos of t e for the data from Fig. 6 to compare the tendency of the reduction of the albedos between this study at Shinjo and the study at Kitami from Aoki et al. (2003). In comparing the regression coefficients a in Table 1 for Shinjo with those for Kitami, our results are more similar to the wet snow season rather than to the dry snow season of Kitami in both

9 March 2005 H. MOTOYOSHI et al. 145 Table 1. Regression coefficients and correlation coefficients for the snow albedos in Fig. 5. The regression is expressed by the equation a ¼ at e þ b, where a is the snow albedo, t e is the elapsed time after snowfall (hours), and a and b are the regression coefficients. Spectral region Regression Data used coefficients in regression a (hour 1 ) b Correlation coefficient VIS all snow season 9: NIR all snow season 1: VIS dry snow season 1: NIR dry snow season 2: VIS wet snow season 9: NIR wet snow season 1: Figure or reference Fig. 6 Aoki et al. (2003) spectral regions. This similarity in the albedo reduction rate, which was observed during all snow season at Shinjo and during the wet snow season at Kitami, is due to the similarities in air temperature and predominant snow type in each observation. 5. Number density of atmospheric aerosols and concentration of snow impurities We investigated the effect of wet and dry deposition of atmospheric aerosols in snow by measuring the number density of the atmospheric aerosol particles above the snow surface using an OPC. For this purpose, we examined the relationship between the concentration of snow impurities obtained by snow sampling and the accumulated number of aerosol particles measured with the OPC. First we defined the time interval Dt for each snow sample as the period that the sampled snow had been in the snowpack. In our analysis, we adjusted and determined the value of Dt (for each snow sample) in such a way that the accumulated precipitation during Dt would be equal to the water equivalent of the snow sample. We calculated the accumulated aerosol particle count N during Dt as N ¼ X n OPC ðt i Þ for t s Dt a t i < t s ; i where t s is the snow sampling time, t i is the time that OPC data is recorded every minute and i is its index, n OPC ðt i Þ is the accumulated number of aerosol particles per minute at t i. The value of N offers a measure of the amount of the atmospheric aerosols to which the sampled snow has been exposed. Figure 8 presents the relationships between c and N for each snow sample. Here, c is the total concentration of snow impurities obtained by filtering, and N L in Fig. 8a denotes the accumulated particle count for large particles with a diameter over 5.0 mm and N S in Fig. 8b for small particles with a diameter between 0.3 mm and mm. Some rainfall events were recorded, however we distinguished snowfall events from rainfall events using the data from the snowfall sensor, and only used the precipitation of snowfall for estimating Dt. In Fig. 8a, c remains at approximately 1 ppmw for N L less than and increases significantly for N L greater than If the dry deposition of atmospheric aerosol particles on the snow surface were mainly due to the gravitational settling, then the snow impurities on the snow surface would increase according to the number density of the atmospheric aerosols and the length of the period that the snow surface has been exposed to the aerosol. We interpret the increase of c for large N L to be the contribution of dry deposition of atmospheric aerosols because a large value of N L occurs with the higher aerosol density or longer exposure of the sampled snow to the atmosphere. We believe the wet deposition is the main contributor to c in the low N L region because the contribution from dry deposition is less in that N L region.

10 146 Journal of the Meteorological Society of Japan Vol. 83A Mass concentration c (ppmw) Mass concentration c (ppmw) (a) 10 0 Dec-Feb Mar (b) Total particle count N L Dec-Feb Mar Total particle count N S Fig. 8. Relationship between the concentration of snow impurities and total particle count (a) N L with a diameter over 5.0 mm; and (b) N S with a diameter between 0.3 mm and mm. White circles represent the data from December to February and crosses, the data in March. The value of c at a sufficiently low N L in Fig. 8a implies wet deposition contributed about 1 ppmw to the concentration of snow impurities. In comparing Fig. 8a and 8b, the data of c in Fig. 8b are vertically scattered for the large N S region greater than and the relationship between c and N is not as clear as in Fig. 8a. A contributing factor in this difference could be the difference in the terminal velocity of large and small particles and in the contributions to the dry deposition of them. Other factor could be that OPC counts both water-insoluble and water-soluble particles. The difference in the contributions of large and small particles were supported by the result from the filtration that the main concentration of snow impurities comes from the impurities measured by the filters with a pore size of 5 mm. The mass fractions of snow impurities measured by the filter with a pore size of 5 mm to the total concentration were 76.7% to 99.7% for samples with concentration greater than 3 ppmw but were 42.7% to 91.3% for other samples. 6. Conclusions Broadband snow albedos were continuously observed with snow pit works for several-day intervals in the winters of 2001/2002 and 2002/ 2003 at Shinjo, Japan. The snow albedos observed in the visible and near infrared regions were compared with the possible albedo range calculated theoretically using a radiative transfer model for the atmosphere-snow system. We compared the observed visible albedo data with the theoretical range calculated from the snow impurity model including 0.2 ppmw of soot contamination in addition to dust impurities, which was more accurate than models including 0.0 or ppmw of soot contamination. The comparison also implied the possibility that even the newly fallen snow were contained strong absorptive impurities such as soot particles in addition to mineral dust impurities. We observed both a tendency of snow grain size and concentration of snow impurities to increase with elapsed time after snowfall. Corresponding to the variations in snow physical parameters, the reduction of the broadband albedos after snowfall was observed. The albedo just after snowfall decreased toward the end of snow season in each winter during the observation period. This was caused by the rapid growth of snow grain size and the increased atmospheric aerosols in the snowmelt season. Comparing the observed snow aging effects with the results of Aoki et al. (2003) reveals that the snow aging effect on albedo at Shinjo was similar to that of the wet snow season at Kitami. This was due to the wet snow condition at Shinjo throughout the winter. The effects of wet and dry deposition of snow impurities were examined based on data obtained by OPC. Comparing the accumulated atmospheric aerosol particle count and the concentration of snow impurities revealed the possibility that the snow surface just after snowfall

11 March 2005 H. MOTOYOSHI et al. 147 was polluted by impurities of about 1 ppmw due to wet deposition during the snowfall. To construct a physically based snow albedo model, we must formulate the process of wet and dry deposition to predict the amount of snow impurities. Our results provide valuable information for future studies. In addition to OPC measurement with snow pit works, direct sampling of falling snow and its analysis are necessary for more detailed studies of snow impurities. It is also very important to analyze the fraction of soot in snow impurities because of the significant effect on snow albedo in the visible region. Acknowledgments We would like to thank Katsuhiko Suzuki and Toyohisa Suzuki for their help in the snow observation, Kazunori Inei, Harunobu Koga, Shouji Watanabe and Tomoaki Tsuji for their support in setting up and maintaining the observation instruments, Naomi Akiho for her support with data processing, and Reiko Watanabe for her help in checking the English of the manuscript. This work was conducted as a part of the ADEOS II/GLI Cal/ Val experiment, supported by the Japan Aerospace Exploration Agency and a cooperative study on the optical properties of snow between the Meteorological Research Institute and the Shinjo Branch of the Nagaoka Institute of Snow and Ice Studies. The ADEC Project (Aeolian Dust Experiment on Climate Impact) sponsored by the Ministry of Education, Culture, Sports, Science, and Technology of the Japanese Government also supported part of this study. References Anderson, G.P., S.A. Clough, F.X. Kneizys, J.H. Chetwynd, and E.P. Shettle, 1986: AFGL atmospheric constituent profiles (0 120 km). AFGL-TR , Air Force Geophysics Laboratory, Hanscom, MA. Aoki, Te., Ta. Aoki, M. Fukabori, Y. Tachibana, Y. Zaizen, F. Nishio, and T. Oishi, 1998: Spectral albedo observation on the snow field at Barrow, Alaska. Polar Meteor. Glaciol., 12, 1 9.,,, and A. Uchiyama, 1999: Numerical simulation of the atmospheric effects on snow albedo with a multiple scattering radiative transfer model for the atmospheresnow system. J. Meteor. Soc. Japan, 77, ,,, A. Hachikubo, Y. Tachibana, and F. Nishio, 2000: Effects of snow physical parameters on spectral albedo and bidirectional reflectance of snow surface. J. Geophys. Res., 105, 10,219 10,236., A. Hachikubo, and M. Hori, 2003: Effects of snow physical parameters on shortwave broadband albedos. J. Geophys. Res., 108, 4616, doi: /2003jd Baker, D.G., D.L. Ruschy, and D.B. Wall, 1990: The albedo decay of prairie snows. J. Appl. Meteor., 29, Chou, C.C.-K., T.-K. Chen, S.-H. Huang, and S.C. Liu, 2003: Radiative absorption capability of Asian dust with black carbon contamination. Geophys. Res. Lett., 30, 1616, doi: / 2003GL Choudhury, B.J., T. Mo., J.R. Wang, and A.T.C. Chang, 1981: Albedo and flux extinction coefficients of impure snow for diffuse short-wave radiation. Cold Reg. Sci. Tech., 5, Dai, Y., X. Zeng, R.E. Dickinson, I. Baker, G.B. Bonan, M.G. Bosilovich, A.S. Denning, P.A. Dirmeyer, P.R. Houser, G. Niu, K.W. Oleson, C.A. Schlosser, and Z.-L. Yang, 2003: The common land model, Bull. Amer. Meteor. Soc., 84, Dirmhirn, I. and F.D. Eaton, 1975: Some characteristics of the albedo of snow. J. Appl. Meteor., 14, Hansen, J. and L. Nazarenko, 2004: Soot climate forcing via snow and ice albedos. Proc. Natl. Acad. Sci. USA, 101, doi: / pnas Hess, M., P. Koepke, and I. Schult, 1998: Optical properties of aerosols and clouds: The software package OPAC. Bull. Am. Meteor. Soc., 79, Kanda, H., K. Goto-Azuma, M. Nakawo, N. Miyazaki, and M. Shimizu, 2002: Temporal changes of micro-particle concentration in snow cover during snowmelt. Seppyo, J. Japanese Soc. Snow and Ice, 64, (in Japanese with English abstract). Kinne, S. and R. Pueshel, 2001: Aerosol radiative forcing for Asian continental outflow. Atmos. Environ., 35, Leroux, C., J. Lenoble, G. Brogniez, J.W. Hovenier, and J.F. De Haan, 1999: A Model for the bidirectional polarized reflectance of snow. J. Quant. Spectrosc. Radiant. Transfer, 61, Marshall, S. and R.J. Oglesby, 1994: An improved snow hydrology for GCMs. Part 1: snow cover fraction, albedo, grain size, and age. Clim. Dyn., 10, Nakamura, T. and O. Abe, 1998: Variation in amount of snow, winter precipitation and win-

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