Depositions of light-absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow-atmosphere interaction in the present-day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in-situ measurements. Here, we develop a new model chain NHM-Chem-SMAP, which is composed of a detailed regional meteorology-chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011–2012 winter. NHM-Chem-SMAP successfully reproduces the in-situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition.

Key Points

A model chain of a detailed aerosol chemical transport model and a multilayered physical snowpack model is evaluated at Sapporo, Japan
The model reproduces the measured seasonal evolution of the mass concentrations of light-absorbing particles in the surface snowpack
Light-absorbing particles from domestic and foreign sources could shorten the snow cover duration by a maximum of 5 and 10 days, respectively

Plain Language Summary

Light-absorbing particles (LAPs) such as black carbon (BC) and dust absorb sunlight. Therefore, BC and dust deposited on the snow surface can accelerate snow melting. However, their detailed qualitative and quantitative roles in the climate system have not yet been fully elucidated owing to the lack of in-situ measurements. In Sapporo, Japan, it has been demonstrated that the depositions of LAPs on the snow surface in recent years have the potential to shorten the snow cover duration by approximately two weeks compared to the completely pure snow situation; however, the relative contributions of LAPs from domestic and foreign sources on the snow cover duration are unknown. Here, we develop a chain of models composed of a detailed aerosol chemical transport model and a multilayered physical snowpack model. Using the model chain, we find that LAPs from domestic and foreign sources had the potential to shorten the snow cover duration in Sapporo, Japan during the 2011–2012 winter by 5 and 10 days, respectively compared to the completely pure snow condition.

1 Introduction

Light-absorbing particles (LAPs), such as black carbon (BC) and mineral dust (hereafter referred to as dust), within the snow cover modulate the snow-atmosphere energy exchanges by reducing the snow albedo. This modulation is particularly important at visible wavelengths (Warren & Wiscombe, 1980). When the absorption of shortwave radiant flux at the snow surface is enhanced by the reduction in the visible snow albedo and surface air temperature is sufficiently high, surface snow melting can be induced. The presence of meltwater accelerates snow grain growth via wet snow metamorphism (Brun, 1989), resulting in the reduction of the near-infrared snow albedo (Wiscombe & Warren, 1980). This implies that the presence of LAPs in snow cover plays a unique role in positive feedback that induces snow ablation and increase in surface air temperature, that is, the snow-albedo feedback (e.g., Budyko, 1969; Qu & Hall, 2007). The first detailed quantification of the impacts of LAPs within snow and ice on the terrestrial climate system was carried out by Hansen and Nazarenko (2004).

During the past two decades, efforts to consider the effects of LAPs on snow albedo explicitly in snow models have been made to provide more reliable quantitative estimates. Flanner and Zender (2005) developed the snow, ice, and aerosol radiative (SNICAR) model, which calculates radiative transfer in the snowpack, based on the studies by Wiscombe and Warren (1980) and Toon et al. (1989). Recently, SNICAR has been further enhanced to consider aerosol-snow internal mixing and non-spherical snow grain shapes (He et al., 2018) as well as the adding-doubling radiative transfer algorithm (Dang et al., 2019). The SNICAR model has been implemented into the Community Land Model (CLM; Oleson et al., 2013). Currently, CLM is available within the model framework of the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008; Zhao et al., 2014), and the variable-resolution Community Earth System Model (VR-CESM; Wu et al., 2018; Zarzycki & Jablonowski, 2014). However, compared to a physical snowpack model, CLM does not consider physical processes in snow in much detail (Krinner et al., 2018). Recently, Skiles and Painter (2019) developed a one-way interface between the SNICAR model and the physical snowpack model SNOWPACK (Lehning, Bartelt, Brown, Fierz, et al., 2002; Lehning, Bartelt, Brown, & Fierz, 2002) and presented the performance of the models forced by the in-situ meteorological and snow LAP data obtained from the San Juan Mountains, USA. Tuzet et al. (2017) successfully utilized the physical snowpack model Crocus (Brun et al., 1989, 1992; Carmagnola et al., 2014; Libois et al., 2015; Vionnet et al., 2012), which is two-way coupled with the Two-stream Analytical Radiative TransfEr in Snow (TARTES) model (Libois et al., 2013). Crocus was forced by in-situ meteorological data from the Col de Porte study site (Morin et al., 2012) in the French Alps, as well as data on point LAP deposition flux provided by ALADIN-Climate (Nabat et al., 2015). For the model evaluation, surface BC equivalent concentration data estimated from measured albedos with the method by Dumont et al. (2017) were used.

Niwano et al. (2012) developed the physical snowpack model Snow Metamorphism and Albedo Process (SMAP), where a physically based snow albedo model (PBSAM; Aoki et al., 2011) was incorporated to calculate snow broadband albedo and solar heating profile in the snowpack as a function of snow grain size, mass concentrations of LAPs, and solar illumination conditions. From numerical model sensitivity tests with SMAP forced by in-situ measured meteorological and snow LAP concentrations data at Sapporo, Niwano et al. (2012) highlight that the presence of LAPs in the snow cover at Sapporo played a role in shortening the snow cover duration by 19 and 16 days for the winters of 2007–2008 and 2008–2009, respectively compared to pure snow simulations. However, information regarding the sources of LAPs observed in Sapporo has not yet been clarified.

Here, we utilize the latest version of SMAP (Niwano et al., 2014, 2015) and the regional meteorology-chemistry model NHM-Chem (non-hydrostatic model coupled with chemistry; Kajino et al., 2019) to examine the sources of LAPs observed within the snowpack at Sapporo. For this purpose, we apply source-receptor analysis (e.g., Bartnicki, 1999; Sciare et al., 2003) to our new model chain NHM-Chem-SMAP. This addresses a new challenge for the snow physics community. In the present study, we conduct a direct comparison of the seasonal variations of LAPs in the surface snowpack from in-situ measurements (Kuchiki et al., 2015) and NHM-Chem-SMAP. In the above-mentioned studies, only the one by Tuzet et al. (2017) demonstrated the ability to reproduce the temporally high-resolution seasonal evolution of LAPs in a snowpack. We also present the model sensitivity to the scavenging ratio of BC in snow, because the vertical redistribution of LAPs affects the snow albedo and solar heating profile in the snowpack (e.g., Doherty et al., 2013; Sterle et al., 2013; Svensson et al., 2021) and the resultant snow cover duration. Then, we quantify the relative contributions of LAPs from domestic and foreign sources on the snow melt at Sapporo using NHM-Chem-SMAP. Quantification is performed in terms of snow cover duration and radiative forcing at the surface.

2 Data and Methods

2.1 Physical Snowpack Model SMAP

Basic information of the SMAP model is described in Text S1. A new process implemented in SMAP in this study is the vertical redistribution of LAPs forced by the presence of meltwater and/or rainfall within a snowpack. Here, we follow the approach of Flanner et al. (2007) and Tuzet et al. (2017):

$urn:x-wiley:00948276:media:grl62842:grl62842-math-0001$ (1)

where m is the absolute mass of LAPs for the type of LAPs i (BC or dust) in each layer l, M_scav is the scavenging ratio of LAPs within the snowpack, q is the mass flux of water (positive downward, and q₀ is 0) in layer l estimated from the Richards equation scheme implemented in SMAP, c is the mass concentration of LAP type i in layer l (c₀ is 0), and D is the deposition flux of LAPs at the surface (positive downward). Here, c is obtained by dividing m by the snow water equivalent at each layer l. Note that the vertical coordinate is taken to be positive in a downward direction from the snow surface. The Richards equation allows us to simulate upward vertical water movement in principle; however, in this study, we assume only downward movement of LAPs can occur within a snowpack. In addition, we assume that LAPs that reach the bottom of a snowpack remain continuously in the lowermost bottom layer until complete snow melting.

It has been recognized that the M_scav value strongly depends on the type of LAP. For BC, M_scav is often set to 0.2 (Doherty et al., 2013; Flanner et al., 2007; Tuzet et al., 2017; Yang et al., 2015). For dust, the M_scav has been set to 0 because of its relatively large size (Tuzet et al., 2017; Yang et al., 2015). Thus, following previous studies, M_scav is set to 0.2, and 0 for BC and dust, respectively, in the default configuration of SMAP. Tuzet et al. (2017) investigated the sensitivity of Crocus to changes in M_{scav, BC} and showed that a snow melting was completed slightly earlier if M_{scav, BC} was set to 0. In the present study, we also examine the sensitivity of SMAP to the choice of M_{scav, BC} (Section 3.2) by modulating the value between 0 and 0.4.

2.2 Regional Meteorology-Chemistry Model NHM-Chem

Likewise, the SMAP model, basic information of the regional NHM-Chem model (Kajino et al., 2019) is provided in Text S2. During the model development works for this study, we tested several types of below-cloud scavenging schemes implemented in NHM-Chem, and found that a simple representation of below-cloud scavenging (Draxler & Hess, 1998) performs best for the prediction of LAP concentrations within the snowpack at Sapporo. To discriminate the errors of NHM-Chem-SMAP toward the errors caused by NHM-Chem and SMAP, respectively, we conduct a model simulation with SMAP, where in-situ meteorological and snow LAP data are used to drive the model as performed by Niwano et al. (2012, 2014). The difference between NHM-Chem-SMAP and the sensitivity run is that deposition fluxes of LAPs are given from NHM-Chem to SMAP internally in the former configuration, whereas in-situ measured mass concentrations of LAPs in the surface snowpack at Sapporo (Kuchiki et al., 2015) are used to force SMAP in the latter configuration (vertical redistributions of LAPs are not simulated).

To derive LAPs from domestic and foreign sources, a source-receptor analysis (e.g., Bartnicki, 1999; Sciare et al., 2003) is performed using emission sensitivity tests, described as follows:

$urn:x-wiley:00948276:media:grl62842:grl62842-math-0002$ (2)

where $urn:x-wiley:00948276:media:grl62842:grl62842-math-0003$ is the simulated deposition flux of component i originating from area k. $urn:x-wiley:00948276:media:grl62842:grl62842-math-0004$ , and $urn:x-wiley:00948276:media:grl62842:grl62842-math-0005$ indicate the simulated total deposition of i (a control run), and simulated deposition of i where the emissions of i from k is reduced to 80% (an emission sensitivity run), respectively. In this study, k is set as outside of Japan, and i is set as BC and dust. The domestic source is derived by $urn:x-wiley:00948276:media:grl62842:grl62842-math-0006$ . Using this information, we conduct a SMAP offline numerical sensitivity test, where LAPs only from domestic sources are used to drive SMAP. In addition to the test, we also conduct another sensitivity test, where LAPs are artificially removed (pure snow condition), as performed by Niwano et al. (2012). To discuss the effects of LAPs, we estimate the surface radiative forcing (Hansen & Nazarenko, 2004) of LAPs by diagnosing the difference in net shortwave radiant flux at the snow surface between these different scenarios (Section 3.3).

2.3 In-Situ Meteorological and Snow Data at Sapporo

Model evaluation is performed at an automated weather station (AWS) installed at the Institute of Low Temperature Science, Hokkaido University (43°04′56″N, 141°20′30″E, 15 m.a.s.l.), which is located in an urban area of Sapporo, Hokkaido, Japan (Aoki et al., 2011; Ménard et al., 2019; Niwano et al., 2012, 2020;). To force and evaluate SMAP, we use in-situ surface meteorological and snow data from the AWS following the approach of Tuzet et al. (2017). The 2011–2012 winter (November–April) is chosen as the study period here, because seasonal variations of the measured LAPs in the surface snowpack show a clear typical pattern during the winter, which is relatively low during the accumulation period, and relatively high during the ablation period (Kuchiki et al., 2015).

The surface meteorological properties that are used as model input were obtained from the AWS with a time interval of 30 min, and they are the same as those described by Niwano et al. (2012); they are as follows: precipitation (Figure S1a), air pressure, wind speed, air temperature, relative humidity, downward ultraviolet (UV)-visible and near-infrared radiant fluxes, diffuse components of downward UV-visible and near-infrared radiant fluxes, downward longwave radiant flux, and ground heat flux at the surface. The data are averaged every 30 min or integrated every 30 min (precipitation only; Niwano et al., 2012, 2020). At the Sapporo AWS, the GEONOR rain gauge (Geonor Inc., Oslo, Norway) has been operational since the 2009–2010 winter. To correct the effects of the gauge undercatch (e.g., Nitu et al., 2018), the correction technique by Førland et al. (1996), which was also followed by Morin et al. (2012), is used. In this technique, because the correction equations for snowfall and rainfall are slightly different from each other, it is necessary to classify the phase of precipitation into solid and liquid phases. Thus, here we use the equation by Yamazaki (2001) as a function of the surface wet-bulb temperature to perform the classification. Other sensor specifications are described in Niwano et al. (2012, 2020).

For the model evaluation, we use the data on mass concentrations of LAPs (BC and dust) within the top 2 and 10 cm snow layers (c_{bc, 2 cm}, c_{dust, 2 cm}, c_{bc, 10 cm}, c_{dust, 10 cm}), which were measured in-situ. The data were obtained from snow samples collected twice a week using the thermal optical method and filter weighing (Kuchiki et al., 2015). In addition, half-hourly UV-visible and shortwave albedos, and snow depth measured with the Sapporo AWS, as well as column-integrated snow water equivalent data from in-situ snow pit works (Aoki et al., 2011; Niwano et al., 2012) are also utilized. In the following section, the dates and times are indicated as per the Japan Standard Time.

3 Results

3.1 Mass Concentrations of LAPs in the Surface Snowpack

NHM-Chem-SMAP simulates structures of internal snow layering in detail for BC (Figure 1a) and dust (Figure 1b). Figures 1c and 1d, as well as the coefficients of determination (R²) of the log of half-hourly c_{bc, 2 cm} and c_{dust, 2 cm} (0.53 and 0.59 for BC and dust, respectively), show that the model chain is generally able to estimate measured seasonal variations for these properties. The values of root mean square deviation (RMSD) are slightly high: 0.33 ppmw (parts per million weight) (91% of the average observed value) and 31.32 ppmw (172% of the average observed value) for c_{bc, 2 cm} and c_{dust, 2 cm}, respectively. The R² values for c_{bc, 2 cm} and c_{dust, 2 cm} are 0.36 and 0.17, respectively, which are slightly low. Mean deviation (i.e.,; the average difference between simulated and observed values) is 0.04 ppmw and −12.55 ppmw for c_{bc, 2 cm} and c_dust, respectively.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Evaluation of NHM-Chem-SMAP simulating c_{bc, 2 cm} and c_{dust, 2 cm}. Temporal evolution of simulated profiles for (a) black carbon (BC) and (b) dust mass concentrations. Comparisons of the observed and simulated (c) c_{bc, 2 cm} and (d) c_{dust, 2 cm}.

We also evaluate the model performance during the accumulation period (November–February) and ablation period (March and April) (Table S1). Compared to the accumulation period, better performances in terms of normalized RMSD are obtained during the ablation period, although the R² values are worse. During the ablation period, the mean deviation is better for c_{bc, 2 cm}, whereas it is worse for c_{dust, 2 cm} compared to the accumulation period. Comparisons in terms of the half-hourly c_{bc, 10 cm}, and c_{dust, 10 cm} are also made, and slightly better agreements are obtained (Figure S2 and Table S1). In general, it is more difficult for a physical snowpack model to reproduce pinpoint snow internal properties compared to bulk properties.

The total masses of BC and dust deposited on the surface during the study period, as simulated by the model (Figures S1b and S1c), are $urn:x-wiley:00948276:media:grl62842:grl62842-math-0007$ and $urn:x-wiley:00948276:media:grl62842:grl62842-math-0008$ kg m⁻², respectively. For BC, 2% and 97% of the total deposition amount are attributed to dry and wet depositions, respectively (Figure S3). For dust, the relative contributions are 24% and 76%, respectively (Figure S3). Note that the relatively small residual contributions are attributed to deposition through fog and the subgrid-scale convective deposition considered in the model. The source-receptor analysis indicates that 18% ( $urn:x-wiley:00948276:media:grl62842:grl62842-math-0009$ kg m⁻²) and 6% ( $urn:x-wiley:00948276:media:grl62842:grl62842-math-0010$ kg m⁻²) of the total depositions are attributed to BC and dust from domestic sources, respectively.

3.2 Snow Cover Duration

For half-hourly snow depth, the model's performance (Figure 2a) is acceptable if we compare the following statistics obtained in this study with the previously reported values at Sapporo (Niwano et al., 2012, 2014): Mean deviation is 0.04 m, RMSD is 0.11 m (33% of the average value during the entire winter), and R² is 0.86. In this control run, where the simulated timing of the complete snow melting agrees well with the measurement (Figure 2a), M_{scav, BC} is set to 0.2 (Section 2.1).

The model sensitivity tests modulating the scavenging ratio for BC M_{scav, BC} (Figure 2b) highlight that the BC scavenging within a snowpack at Sapporo accelerates the complete melting of snow. Simulated snow albedos decrease in March as the M_{scav, BC} increases: 0.648, 0.633, 0.627, 0.624, and 0.620 for M_{scav, BC} values of 0.0, 0.1, 0.2, 0.3, and 0.4 (Figure S4). This tendency is counterintuitive and opposite to the results of the study by Tuzet et al. (2017) obtained for the French Alps. Our model simulations suggest that subsurface solar heating within the snowpack, its resultant subsurface melt, and wet snow metamorphism (Brun, 1989) play an important role in the snow ablation at Sapporo during the 2011–2012 winter, although changes in M_{scav, BC} values between 0.1 and 0.4 do not affect the simulated snow cover duration at Sapporo (Figure 2b).

3.3 Relative Contributions of LAPs From Domestic and Foreign Sources

LAPs from domestic sources (18% and 6% of the total deposition for BC and dust) played a role in shortening the snow cover duration by 5 days (Figure 2a) compared to the completely pure snow situation, which is 33% of the total change in snow cover duration due to the presence of LAPs, based on the snow cover duration estimated from the pure snow scenario (15 days, Figure 2a). The result from the pure snow scenario is in line with the results of Niwano et al. (2012), who conducted SMAP model simulations forced by in-situ meteorological and LAP measurements and showed that snow cover durations at Sapporo during the 2007–2009 winters were shortened by 2 weeks compared to a situation where LAPs are entirely ignored.

Monthly radiative forcings of LAPs during the completely snow-covered months (December–March), as estimated by NHM-Chem-SMAP, are relatively low during the accumulation period (12 W m⁻²), whereas they are relatively high during the ablation period (30 W m⁻²) (Figure 3), which is also in line with the measurement-based estimation by Niwano et al. (2012) for the 2007–2009 winters at Sapporo.

During the accumulation period, 40% of the total radiative forcing of LAPs is attributed to LAPs from domestic sources (Figure 3). However, the fraction decreases twofold during the ablation period (20%; Figure 3). The ablation period is known to be the dominant outflow season where dust and air pollutants outflow to the North Pacific Ocean (Kaneyasu et al., 2020; Uematsu et al., 1983), suggesting that the LAPs from the Asian continent play an important role in controlling the duration of snow cover in deeply snow-covered areas in Japan.

4 Discussion

4.1 Errors in Simulating Mass Concentrations of LAPs and Their Impacts

It is difficult to identify the exact causes of discrepancies between in-situ measurements and model simulations in terms of c_{bc, 2 cm} and c_{dust, 2 cm} (Section 3.1); however, discrepancies can be mainly attributed to large uncertainties in wet scavenging efficiencies and emissions in the atmosphere. For BC, the below-cloud scavenging coefficients of particles in the submicron range can vary by two to three orders of magnitude, depending on experiments and theories (Wang et al., 2010; Zhang et al., 2013). For Asia, the uncertainty of BC emissions is estimated to be 176%–257% (Kurokawa et al., 2013). A chemical transport model intercomparison study (Itahashi et al., 2020) shows a difference of approximately one order of magnitude in the annual total deposition amount, even though the models use the same meteorology, model domain, and emission data sets. The uncertainty in dust emissions is even larger. The dust emission flux is usually formulated as a third to fourth power of the friction velocity. In addition to the errors in the simulated surface conditions, such as soil type, soil moisture, and snow cover, small errors in friction velocity can cause large differences in the simulated dust emission flux. Our NHM-Chem-SMAP simulation results are considered to be reasonable, as the normalized RMSD is 50%–200% for c_{bc, 2 cm} and c_{dust, 2 cm} (Table S1) despite the huge uncertainties in atmospheric deposition modeling. This argument is supported by the above-mentioned R² values of the log of the snow LAP mass concentrations (0.53 and 0.59 for BC and dust, respectively) as well as the same indicator from the SNICAR model (0.61; Flanner et al., 2007). Another possible cause is a large variety of local processes related to the transport and redistribution of LAPs around Sapporo.

In Sapporo, the model performance in terms of snow albedo values during the study period is closely related to the reproducibility of BC concentrations in the snowpack; that is, the simulated BC concentration is overestimated slightly, and the UV-visible and shortwave snow albedos tend to be underestimated (mean deviations are −0.15 and −0.12, respectively); whereas dust concentration is underestimated. This situation arises because of the sufficiently high total amount of BC transported to Sapporo, as well as the mass absorption cross-section (MAC) values considered in the model. The MAC values for BC and dust considered in SMAP are given by Aoki et al. (2011), and the ratio of MAC for BC to that for dust is above 100. RMSDs for snow albedos are 0.18 (20% of the average observed value) and 0.15 (19% of the average observed value), and the R² values are 0.44 and 0.46, for UV-visible and shortwave albedos, respectively. Mean deviation tendencies for snow albedos are the same for the accumulation and ablation periods (Table S1), although the overestimation tendency of c_{bc, 2 cm} cannot be found during the ablation period (Table S1). A possible cause for this is that the snow grain growth is overestimated in the simulated snowpack because of the continuous overestimation of BC mass concentration during the accumulation period. Normalized RMSD values for snow albedos are slightly worse during the ablation period than in the accumulation period, although R² values are improved during the ablation period. The worse normalized RMSD value for shortwave albedo during the ablation period worsens the model performance in terms of the snow water equivalent during the period (Table S1).

From the model simulation with SMAP, where in-situ meteorological and snow LAP concentration data are used to drive the model (Section 2.2), the R², RMSD, and mean deviation values for shortwave snow albedo throughout the winter of 0.67, 0.09, and −0.05, respectively are obtained. This implies that, in terms of RMSD, only 40% of the total error of NHM-Chem-SMAP in simulating shortwave snow albedo can be attributed to the atmospheric chemical process despite the relatively large uncertainties discussed above. Recently, it has been recognized that mixing states of LAPs within the snowpack (Flanner et al., 2012; He et al., 2014, 2019; Liou et al., 2014; Tanikawa et al., 2020) and optically equivalent snow grain shapes (He et al., 2014; Libois et al., 2013; Tanikawa et al., 2020) can control the snow albedos. Hence, it is necessary to consider these processes explicitly in the SMAP model to improve the model performance in the future.

4.2 Uncertainty of Estimated Radiative Forcings

The contrast of the simulated radiative forcings between the accumulation and ablation periods can be attributed to (a) the contrast of downward shortwave radiant flux, and (b) the difference in snow grain size (Niwano et al., 2012). Typically, the impacts of LAPs are much more enhanced when the snow grain size becomes larger (e.g., Aoki et al., 2003). However, the radiative forcings obtained in this study can be slightly overestimated, especially during the accumulation period. This is because c_{bc, 2 cm} is overestimated during the same period (Section 3.1). Meanwhile, He et al. (2019) present that internal mixing of dust within a snowpack enhances the magnitude of snow albedo reductions by 10%–30% (10%–230%) at the visible (near-infrared) band, implying that our model simulation results based on the external mixing assumption (Text S1) can be underestimated.

5 Conclusions

Our newly developed model chain NHM-Chem-SMAP is evaluated in Sapporo, Japan for the 2011–2012 winter period. The model chain reproduces the measured seasonal variations of LAPs in the surface snowpack; however, slight disagreements are often found. Despite the relatively large uncertainties, only 40% of the RMSD value of shortwave snow albedo from the NHM-Chem-SMAP run is attributed to atmospheric chemical processes. The model sensitivities to the scavenging ratio for BC buried within the snowpack show that BC scavenging within the snowpack at Sapporo accelerates complete melting of snow, highlighting the importance of subsurface solar heating in snow ablation at Sapporo. LAPs from domestic sources (18% and 6% of the total deposition for BC and dust, respectively during the entire winter) and foreign sources, which are estimated from source-receptor analysis, played a role in shortening the snow cover duration by 5 and 10 days, respectively compared to the completely pure snow situation. About 40% of the radiative forcing of LAPs at the snow surface is attributed to LAPs from domestic sources during the accumulation period; however, the fraction decreases twofold during the ablation period. Finally, we argue that more studies related to emissions of LAPs, wet scavenging efficiencies of LAPs in the atmosphere, local processes that affect the transport and redistribution of LAPs, and mixing states of LAPs within the snowpack are needed to increase the performance and credibility of snow-atmosphere coupled model simulations.

Acknowledgments

The authors thank the members of the Institute of Low Temperature Science, Hokkaido University, for performing snow pit work and snow sampling at Sapporo during the study period, and Masae Igosaki for analyzing the snow samples. This study was supported in part by (a) the Ministry of the Environment of Japan through the Experimental Research Fund for Global Environment Conservation MLIT1753; (b) the Grant for Joint Research Program (20G042 and 21G029), the Institute of Low Temperature Science, Hokkaido University; (c) the Japan Society for the Promotion of Science through Grants-in-Aid for Scientific Research numbers JP17KK0017, JP18H03363, and JP20H04982; and (d) the Arctic Challenge for Sustainability II (ArCS II), Program Grant number JPMXD1420318865. The authors thank Samuel Morin and an anonymous referee for providing constructive comments and suggestions, which improved the quality of this study.

Open Research

Data Availability Statement

The NHM-Chem-SMAP model simulation data used in this study are available from https://ads.nipr.ac.jp/data/meta/A20210517-001, https://ads.nipr.ac.jp/data/meta/A20210517-002, https://ads.nipr.ac.jp/data/meta/A20210517-003, https://ads.nipr.ac.jp/data/meta/A20210517-004. The snow model forcing data are available from https://doi.org/10.1594/PANGAEA.919800

Supporting Information

References

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Volume48, Issue16

28 August 2021

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Modeling in Glaciology

Quantifying Relative Contributions of Light-Absorbing Particles From Domestic and Foreign Sources on Snow Melt at Sapporo, Japan During the 2011–2012 Winter

Abstract

Key Points

Plain Language Summary

1 Introduction

2 Data and Methods

2.1 Physical Snowpack Model SMAP

2.2 Regional Meteorology-Chemistry Model NHM-Chem

2.3 In-Situ Meteorological and Snow Data at Sapporo

3 Results

3.1 Mass Concentrations of LAPs in the Surface Snowpack

3.2 Snow Cover Duration

3.3 Relative Contributions of LAPs From Domestic and Foreign Sources

4 Discussion

4.1 Errors in Simulating Mass Concentrations of LAPs and Their Impacts

4.2 Uncertainty of Estimated Radiative Forcings

5 Conclusions

Acknowledgments

Open Research

Data Availability Statement

Supporting Information

References

References From the Supporting Information

Citing Literature

Figures

References

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Quantifying Relative Contributions of Light-Absorbing Particles From Domestic and Foreign Sources on Snow Melt at Sapporo, Japan During the 2011–2012 Winter

Abstract

Key Points

Plain Language Summary

1 Introduction

2 Data and Methods

2.1 Physical Snowpack Model SMAP

2.2 Regional Meteorology-Chemistry Model NHM-Chem

2.3 In-Situ Meteorological and Snow Data at Sapporo

3 Results

3.1 Mass Concentrations of LAPs in the Surface Snowpack

3.2 Snow Cover Duration

3.3 Relative Contributions of LAPs From Domestic and Foreign Sources

4 Discussion

4.1 Errors in Simulating Mass Concentrations of LAPs and Their Impacts

4.2 Uncertainty of Estimated Radiative Forcings

5 Conclusions

Acknowledgments

Open Research

Data Availability Statement

Supporting Information

References

References From the Supporting Information

Citing Literature

Figures

References

Related

Information

RESOURCES

PUBLICATION INFO