Surface energy balance of snow cover in the Japanese Alps is similar to that in continental Alpine climates

The Japanese Alps are in central Japan (figure 1), and consist of several mountains with peaks of approximately 3000 m above sea level (a.s.l.) as part of the highest elevation range in Japan. There is a huge amount of snowfall in this region due to climatic and topographic factors. One is that, the Japanese Alps are part of the maritime climate because they are approximately 60 km from the coast of the Japan Sea, with a humid air mass quickly traveling to the mountains. Another is that the steep and high elevation terrain extends from the coast of the Japan Sea to the Japanese Alps. These characteristics make the Japanese Alps a unique landmass, as they feature glaciers and perennial snow patches despite lower elevation and in a more temperate climate than similar locations in the world that also have glaciers and snow patches (Fukui et al 2018, Arie et al 2019).

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The mountainous members of the Northern Japanese Alps, Kamikochi, the Norikura highland, and Nishi-Hodaka were selected as study areas. Kamikochi is a generic term for the mountain area located in the southern part of the Northern Japanese Alps. Kamikochi is in the highland valley basin of the Azusagawa River at an elevation of 1500 m a.s.l. and is surrounded by approximately 3000 m a.s.l. high mountains in the Northern Japan Alps. In the Kamkochi region, annual cumulative snowfall over 600 mm was often observed (Suzuki 2017) from 1981 to 2012 due to the strong winter monsoon, and seasonal snow cover formed every year. The Norikura highland is 10 km south of Kamikochi. Mt. Nishi-Hodaka-dake (2909 m a.s.l.) is one of the peaks of the Hodaka Mountain Range (including Mt. Kita-Hodaka-dake, Mt. Oku-Hodaka-dake and Mt. Mae-Hodaka-dake) extending from north to south. Nishi-Hodaka is approximately 3.8 km north of Kamikochi and tops the Kamikochi Valley ridge.

Shinshu University conducted meteorological observations at an automatic weather station (AWS) in Kamikochi (AWS-K; 1490 m a.s.l.), Norikura highland (AWS-N; 1590 m a.s.l.) and Nishi-Hodaka (AWS-H; 2355 m a.s.l.) (figure 1). All sites were in open locations. The AWS-K site was selected in the forest clearing location, and in AWS-H, there are fewer trees because AWS-H is located at an elevation of the tree line; therefore, the effect of weakening wind speed is minimal in those two sites. AWS-N is in open space, but there is forest surrounding (within 5–10 m away from) the site (Nishimura et al 2018, 2019).

The instrumental specifications of the AWSs are listed in table 1. Precipitation observations were not conducted in AWS-K and AWS-N; therefore, this study used the precipitation data from the nearest observation site (Kamikochi) of the Japan Meteorological Agency as AWS-K ('P-Site_K' in figure 1), and Nrk-St. that were operated by Shinshu University ('P-Site_N' in figure 1). The authors used the observed air temperature data of Kmk-St. that was also another AWS site operated by Shinshu University (figure 1) to calculate annual meteorological statistics. Only spring season precipitation data in AWS-H were used because no observations took place during winter in this location. Precipitation, whether rain or snow was classified using the threshold air temperature of 1.7 °C (Ogawa and Nogami 1994). Observed meteorological elements (see table 2) include air temperature (${T}_{z}$) [°C], relative humidity (RH), wind speed ($U$) [m s⁻¹], wind direction (WD) [degree], atmospheric pressure (P) [hPa], incoming and reflected shortwave radiation ($S{W}_{in}$ and $S{W}_{ref}$) [W m⁻²], incoming and upward longwave radiation ($L{W}_{in}$ and $L{W}_{up}$) [W m⁻²], and snow depth (d) [m]. Every observation data was recorded in 10 min intervals. The snow depths in AWS-N and AWS-H were recorded every 60 min. The meteorological datasets were only analyzed for the snow-covered periods in 2016/17 winter, from 6 December 2016 to 4 May 2017 (AWS-K), from 24 November 2016 to 4 May 2017 (AWS-N), from 29 October 2016 to 2 July 2017 (AWS-H).

The surface energy balance (SEB) [W m⁻²] was determined using equation (1). The turbulent energy fluxes [W m⁻²] were calculated using the bulk aerodynamic method from equations (4) and (5). The rainfall energy flux (${Q}_{r}$) [W m⁻²] was calculated using equation (6).

$$\begin{eqnarray}&&SEB={R}_{net}+H+E+{Q}_{r}\end{eqnarray} \tag{ 1 }$$

$$\begin{eqnarray}&&{R}_{net}=S{W}_{in}-S{W}_{ref}+L{W}_{in}-L{W}_{up}\end{eqnarray} \tag{ 2 }$$

$$\begin{eqnarray}&&=S{W}_{in}\left(1-\alpha \right)+\varepsilon L{W}_{in}-\sigma \varepsilon {{T}_{s}}^{4}\end{eqnarray} \tag{ 3 }$$

$$\begin{eqnarray}&&H={\rho }_{a}{C}_{p}{C}_{H}U\left({\theta }_{z}-{\theta }_{s}\right)\end{eqnarray} \tag{ 4 }$$

$$\begin{eqnarray}&&E={\rho }_{a}{L}_{V}{C}_{E}U\left({q}_{z}-{q}_{s}\right)\end{eqnarray} \tag{ 5 }$$

$$\begin{eqnarray}&&{Q}_{r}={P}_{r}{\rho }_{w}{c}_{w}\left({T}_{r}-{T}_{s}\right)\end{eqnarray} \tag{ 6 }$$

where ${R}_{net}$ is the net radiation, and $H$ and $E$ are the turbulent sensible and latent heat fluxes, respectively. ${R}_{net}$ was calculated in equations (2) and (3), where $S{W}_{in}$ is the incoming shortwave radiation, $S{W}_{ref}$ is the reflected shortwave radiation, $\alpha$ is the surface albedo, $L{W}_{in}$ is the incoming longwave radiation, $L{W}_{up}$ (=$\left(1-\varepsilon \right)\,L{W}_{in}+L{W}_{out}$) is the upward longwave radiation, $L{W}_{out}$ is the outgoing longwave radiation from the snow surface, $\sigma$ (= 5.67×10⁻⁸ [W m⁻² K⁻⁴]) is the Stefan-Boltzmann's constant, and ε (= 0.98) is the emissivity of the snow surface. In this study, surface temperature (${T}_{s}$) [°C] was determined by outgoing longwave radiation using Stefan-Boltzmann's law. ${\theta }_{z}$ and ${\theta }_{s}$ is the potential temperature of the air and the snow surface, respectively. All energy fluxes toward the surface were defined as positive.

Turbulent energy fluxes were calculated using the bulk aerodynamic method (equations (4) and (5)). ${C}_{H}$ and ${C}_{E}$ are the bulk exchange coefficients for the sensible heat and latent heat fluxes, respectively, and ${\rho }_{a}$ [kg m⁻³] is the moist air density. ${{\rm{C}}}_{{\rm{p}}}$ (= 1.005 [kJ K⁻¹ kg⁻¹]) is the atmospheric specific heat at constant pressure, and ${L}_{V}$ [J kg⁻¹] is the latent heat of evaporation or sublimation. When ${T}_{s}=0$ °C, this study used Lv as the latent heat of evaporation (${{\rm{Lv}}}_{{\rm{water}}}=2505$ [kJ kg⁻¹]), and when ${T}_{s}\lt 0$ °C, this study used Lv as the latent heat of sublimation (${{\rm{Lv}}}_{{\rm{ice}}}=2838$ [kJ kg⁻¹]). ${q}_{z}$ and ${q}_{s}$ [g kg⁻¹] are the atmospheric and surface-specific humidity, respectively. The bulk coefficients (${C}_{H}$ and ${C}_{E}$) were calculated in a following procedure, considering the atmospheric stability by the Monin-Obukhov stability length ($L$).

Bulk coefficients for neutral atmospheric conditions (${C}_{HN}$ and ${C}_{EN}$) were calculated using equations (7) and (8).

$$\begin{eqnarray}&&{C}_{HN}=\displaystyle \frac{{\kappa }^{2}}{\left[\mathrm{ln}\left({z}_{v}/{z}_{0v}\right)\mathrm{ln}\left({z}_{t}/{z}_{0t}\right)\right]}\end{eqnarray} \tag{ 7 }$$

$$\begin{eqnarray}&&{C}_{EN}=\displaystyle \frac{{\kappa }^{2}}{\left[\mathrm{ln}\left({z}_{v}/{z}_{0v}\right)\mathrm{ln}\left({z}_{e}/{z}_{0e}\right)\right]}\end{eqnarray} \tag{ 8 }$$

where κ (=0.41) is the von Karman constant, and ${z}_{v},$ ${z}_{t},$ and ${z}_{e}$ are the measurement height (corrected by the snow depth (d)) of wind speed, air temperature, and relative humidity (${z}_{t}={z}_{e};$ because of using the same sensor) at each AWS site, and ${z}_{0v},$ ${z}_{0t}$ and ${z}_{0e}$ are the roughness length for momentum, temperature, and moisture, respectively. This study was set ${z}_{0v}=2.3\times {10}^{-4}$ [m] (Kondo and Yamazawa 1986) as a constant value. ${z}_{0t}$ and ${z}_{0e}$ are calculated in equations (9) and (10), following Andreas (1987):

$$\begin{eqnarray}&&\mathrm{ln}\left(\displaystyle \frac{{z}_{0t}}{{z}_{0v}}\right)={a}_{t}+{b}_{t}\,\mathrm{ln}\,Re+{c}_{t}{\left(\mathrm{ln}Re\right)}^{2}\end{eqnarray} \tag{ 9 }$$

$$\begin{eqnarray}&&\mathrm{ln}\left(\displaystyle \frac{{z}_{0e}}{{z}_{0v}}\right)={a}_{e}+{b}_{e}\,\mathrm{ln}\,Re+{c}_{e}{\left(\mathrm{ln}Re\right)}^{2}\end{eqnarray} \tag{ 10 }$$

$$\begin{eqnarray}&&Re=\displaystyle \frac{{u}_{* }{z}_{0v}}{\nu }\end{eqnarray} \tag{ 11 }$$

$$\begin{eqnarray}&&{u}_{* }=\displaystyle \frac{\kappa \,U}{\mathrm{ln}\left({z}_{v}/{z}_{0v}\right)}\end{eqnarray} \tag{ 12 }$$

where $Re$ is the roughness Reynolds number, ${a}_{t},$ ${b}_{t},$ ${c}_{t},$ ${a}_{e},$ ${b}_{e},$ and ${c}_{e}$ are the numeric constants, ${u}_{* }$ [m s⁻¹] is the air shear velocity, and $\nu$ [m² s⁻¹] is the kinematic viscosity of air. Those numeric constants are given by Re, and then, ${C}_{HN}$ and ${C}_{EN}$ were calculated.

This study adjusted the bulk coefficients for atmospheric stability using the Monin-Obukhov stability length, as $L$ [m] (van den Broeke et al 2005) shown in equation (13):

$$\begin{eqnarray}&&L=\displaystyle \frac{{{u}_{* }}^{2}}{\kappa \displaystyle \frac{{q}_{z}}{{\theta }_{z}}\left({\theta }_{* }+0.62{\theta }_{z}{q}_{* }\right)}\end{eqnarray} \tag{ 13 }$$

$$\begin{eqnarray}&&{\theta }_{* }=\displaystyle \frac{\kappa \left({\theta }_{z}-{\theta }_{s}\right)}{\mathrm{ln}\left({z}_{t}/{z}_{0t}\right)}\end{eqnarray} \tag{ 14 }$$

$$\begin{eqnarray}&&{q}_{* }=\displaystyle \frac{\kappa \left({q}_{z}-{q}_{s}\right)}{\mathrm{ln}\left({z}_{e}/{z}_{0e}\right)}\end{eqnarray} \tag{ 15 }$$

where ${\theta }_{* }$ [K] and ${q}_{* }$ [g kg⁻¹] are the turbulent scales of temperature and moisture, respectively. The relationships between the stability parameters $\zeta$ ($=z/L$) were determined, and the atmospheric stability was calculated as shown in (i) and (ii) (Dyer 1974). In the case of (i) or (ii), calculations were conducted using equations (16)–(18), and then, the stability functions (${{\rm{\Psi }}}_{M},$ ${{\rm{\Psi }}}_{T},$ and ${{\rm{\Psi }}}_{E}$) were calculated using $\zeta$ the calculated values for each observation height (${z}_{v},$ ${z}_{t},$ and ${z}_{e}$).

• (i)  
  $\zeta \gt 0$ (stable atmospheric condition)

  $$\begin{eqnarray}&&{{\rm{\Psi }}}_{M}={{\rm{\Psi }}}_{T}={{\rm{\Psi }}}_{E}=1+5\,\zeta \end{eqnarray} \tag{ 16 }$$
• (ii)  
  $\zeta \lt 0$ (unstable atmospheric condition)

$$\begin{eqnarray}&&{{\rm{\Psi }}}_{M}={\left(1-16\zeta \right)}^{-\displaystyle \frac{1}{4}}\end{eqnarray} \tag{ 17 }$$

$$\begin{eqnarray}&&{{\rm{\Psi }}}_{T}={{\rm{\Psi }}}_{E}={\left(1-16\zeta \right)}^{-\displaystyle \frac{1}{2}}\end{eqnarray} \tag{ 18 }$$

This study defined that when $\zeta$ is positive, the boundary layer is stable, and when $\zeta$ is negative, the boundary layer is unstable, and the neutral condition is defined $\zeta \lt \left|0.01\right|.$ In the case of $\zeta \to$ ∞, which means $L\to 0,$ this study assumed no turbulence occurred in each time step.

${u}_{* },$ ${\theta }_{* },$ and ${q}_{* }$ considering atmospheric stability were recalculated using following equations (19)–(21).

$$\begin{eqnarray}&&{u}_{* }=\displaystyle \frac{\kappa \,U}{\left[\mathrm{ln}\left({z}_{v}/{z}_{0v}\right)-{{\rm{\Psi }}}_{M}\right]}\end{eqnarray} \tag{ 19 }$$

$$\begin{eqnarray}&&{\theta }_{* }=\displaystyle \frac{\kappa \left({\theta }_{z}-{\theta }_{s}\right)}{\left[\mathrm{ln}\left({z}_{t}/{z}_{0t}\right)-{{\rm{\Psi }}}_{T}\right]}\end{eqnarray} \tag{ 20 }$$

$$\begin{eqnarray}&&{q}_{* }=\displaystyle \frac{\kappa \left({q}_{z}-{q}_{s}\right)}{\left[\mathrm{ln}\left({z}_{e}/{z}_{0e}\right)-{{\rm{\Psi }}}_{E}\right]}\end{eqnarray} \tag{ 21 }$$

Because $L$ is a function of ${u}_{* },$ ${\theta }_{* },$ and ${q}_{* },$ this study conducted an iteration loop between equations (13) and (16)–(21) until $L$ was converged to within 0.001% of the previous value of $L.$ Using the converged $L,$ calculations in equations (16)–(18) were conducted. The values of ${C}_{H},$ ${C}_{E},$ $H,$ and $E$ were calculated using equations (4), (5), (22), and (23).

$$\begin{eqnarray}&&{C}_{H}=\displaystyle \frac{{\kappa }^{2}}{\left\{\left[\mathrm{ln}\left({z}_{v}/{z}_{0v}\right)-{{\rm{\Psi }}}_{M}\right]\left[\mathrm{ln}\left({z}_{t}/{z}_{0t}\right)-{{\rm{\Psi }}}_{T}\right]\right\}}\end{eqnarray} \tag{ 22 }$$

$$\begin{eqnarray}&&{C}_{E}=\displaystyle \frac{{\kappa }^{2}}{\left\{\left[\mathrm{ln}\left({z}_{v}/{z}_{0v}\right)-{{\rm{\Psi }}}_{M}\right]\left[\mathrm{ln}\left({z}_{e}/{z}_{0e}\right)-{{\rm{\Psi }}}_{E}\right]\right\}}\end{eqnarray} \tag{ 23 }$$

Rainfall energy flux is the sensible heat supplied by raindrops. ${Q}_{r}$ was calculated using equation (6). ${P}_{r}$ [m s⁻¹] is the rainfall intensity, ${\rho }_{{\rm{w}}}$ (=1000 [kg m⁻³]) is the density of the water, ${{\rm{c}}}_{{\rm{w}}}$ (= 4.21 [kJ K⁻¹ kg⁻¹]) is the specific heat capacity of the water, and ${T}_{r}$ is the temperature of a rain drop, which is considered equal to ${T}_{z}$ in this study. This study neglected the subsurface energy flux because its contribution to the surface energy balance is small (e.g. Andreassen et al 2008, Sicart et al 2008). In addition, this assumption is applied widely during the ablation period, because the entire snowpack is assumed to be 0 °C in that period. Therefore, this study discussed the results of surface energy balance analysis during the ablation period (in table 5 and figure 4).

The annual mean air temperature and the annual amount of precipitation in Kamikochi, Norikura, and Nishi-Hodaka are shown in table 3. However, annual precipitation was not calculated because all year precipitation observations in AWS-H were not conducted. The annual mean air temperatures in Kamikochi, Norikura, and Nishi-Hodaka were 5.8 °C, 5.5 °C, and 1.3 °C, respectively. The annual ranges of air temperature in Kamikochi, Norikura, and Nishi-Hodaka were 25.7 °C, 24.7 °C, and 26.0 °C, respectively. The annual cumulative precipitation in Kamikochi and Norikura were 2692 mm and 1946 mm, respectively.

Meteorological observation data during the snow-covered period of 2016/17 in AWS-K, AWS-N, and AWS-H are shown in figure 2. The air temperature difference between AWS-K and AWS-N was small, and a similar fluctuation of air temperature in AWS-K and AWS-N was observed. The air temperature in AWS-H was lower than that in the other two sites, reflecting a difference in the elevation of the AWS location. The positive air temperature was observed after early April in AWS-K and AWS-N, and after late April AWS-H. No noticeable differences in specific humidity were found. However, the specific humidity in the AWS-H was slightly higher than that in the other both sites. The fluctuation in specific humidity at all sites was similar. The wind speed at AWS-H was higher than that in the other two sites, and the wind speed in AWS-N was slightly lower than that in AWS-K. The mean wind speed during the snow-covered period in AWS-H was over 2.5 times higher than that in the other two sites. The snow depth in AWS-H was much larger than that in the other two sites. The maximum snow depths in 2016/17 in AWS-H, AWS-K, and AWS-N were 676 cm, 165 cm and 159 cm, respectively. Decreasing snow depth was observed in all sites after early April.

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All locations showed distinct surface energy balance properties, those were (1) net shortwave radiation controlled SEB variation and (2) an energy loss due to negative latent heat flux. The analysis of the surface energy balance during the ablation period is shown in table 4, and the energy flux in figure 3. The figures showing each energy balance component's contribution ratio in the text and table 4 were calculated by dividing an energy flux (e.g., net shortwave radiation, net longwave radiation) by the total energy flux (SEB). Net shortwave radiation is the most dominant energy source at all sites (over 100%), and the second major energy flux was the sensible heat flux. Net longwave radiation was the largest source of energy loss at all sites. Latent heat flux was the second source of energy loss at AWS-K and AWS-N, but it was the third largest source of positive energy flux at AWS-H. Rainfall energy flux hardly contributed to the snow ablation at all sites. This resulted in a radiation component that controlled the surface energy balance variation and snow ablation process.

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The trends observed at each site can be summarized as follows: The surface energy balance was controlled by net shortwave radiation in AWS-K, which is 155.3% against SEB. The other significant energy flux was the sensible heat flux (30.8%). The net longwave radiation and latent heat flux were the energy sink and cooled the snow surface at −70.6% and −19.9%, respectively. The energy loss due to longwave radiation emission and latent heat flux in AWS-K was more extensive than that of the other two sites. The rainfall energy flux was a small source of energy (1.9%).

The surface energy balance property in AWS-N was similar to that in AWS-K. The largest energy source was net shortwave radiation (131.3%), and the second was a sensible heat flux (15.9%). In contrast, the greatest energy sink was net longwave radiation (−46.6%), followed by the latent heat flux (−3.3%). The rainfall energy flux was also a small positive energy source (0.6%).

The surface energy balance property at AWS-H was also similar to those in AWS-K and AWS-N. However, the amount of net shortwave radiation (108.6%) and the sensible heat flux (9.9%) were larger than those of AWS-K and AWS-N. Latent heat flux was negative in March and April (slightly positive in February), and then it turned to positive between May and July.

We evaluated the surface energy balance properties for the Northern Japanese Alps: (1) net radiation that controlled the surface energy balance variation, and (2) negative latent heat flux (sublimation) in the mid-winter season. Net radiation contributed significantly (over 80%) to snow ablation in the Northern Japanese Alps, due to high net shortwave radiation (over 100%). The ablation-dominating shortwave radiation (e.g. Greuell and Smeets 2001) has been reported on by many previous studies. Therefore, a special snow ablation mechanism could not be recognized in the Northern Japanese Alps.

The surface energy balance properties of (1) and (2) described above were similar to continental climate regions (Willis et al 2002), suggesting that the snow cover was ablates in relatively dry atmospheric conditions during the winter season. Shortwave radiation increases due to less cloud formation (Abermann et al 2019) and energy sink in sublimation from snowpack when it is dry (Sicart et al 2005). The Japanese climate is considered maritime; therefore, there are reports on surface energy balance analyses for maritime climates (e.g. Matsumoto et al 2010). However, the surface energy balance property in continental climate region noted in Sicart et al (2005) was also confirmed in this study region in the mid-winter season.

This study reviewed previous reports and classified the surface energy balance according to continental or maritime climate (partly referring to Willis et al (2002) and Giesen et al (2008)). The results of the review are shown in table 5 and figure 4. The surface energy balance analysis data of this study listed in table 5 are only for the ablation period. Radiation elements are generally dominant (e.g. Sicart et al 2005) in continental climate region, and turbulent energy flux, in contrast, dominates the surface energy balance (e.g. Gillett and Cullen 2011). Data in this study listed in table 5 are similar to the surface energy balance property one expects of a continental climate region. Accumulated and ablated snow cover in relatively dry atmospheric, continental climate, conditions in this study are a new description, because the Japanese seasonal snow cover accumulation and ablation was generally thought to be governed by the maritime climate.

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Table 5. Data used from the previous study in figure 4: study location, latitude, altitude, climate classification, observation period, and the surface energy balance.

Location					Observation Period						Surface Energy Flux (%)			References
Site	Country	Latitude	Altitude (m)	Climate							Rnet	H	E
Kamikochi	Japan	36° N	1490		Apr.	2017	—	May	2017		85	16	−4	this study
Norikura	Japan	36° N	1590		Feb.	2017	—	May	2017		92	10	−3	this study
Nishi-Hodaka	Japan	36° N	2355		Mar.	2017	—	Jul.	2017		83	11	5	this study
Peyto Glacier, Alberta	Canada	51° N	2500	Continental	Jun.	1988	—	Jul.	1988		51	42	7	Munro (1990)
Peyto Glacier, Alberta	Canada	51° N	2300	Continental	Jun.	1988	—	Jul.	1988		65	34	1	Munro (1990)
Peyto Glacier, Alberta	Canada	51° N	2510	Continental	Jul.	1970					44	48	8	Föhn (1973)
Hintereisferner	Austria	46° N	2500	Continental	Jul.	1986					91	10	−2	van de Wal et al (1992)
Hintereisferner	Austria	46° N	2630	Continental	Jul.	1989					93	20	−13	van de Wal et al (1992)
Niwot Ridge, CO	USA	40° N	3517	Continental	Apr.	1994	—	Jun.	1994		75	56	−31	Cline (1997)
Valee Blanche	France	45° N	3550	Continental	Jul.	1968					99	24	−23	De la Casiniere (1974)
St Sorline Glacier	France		2712	Continental	Aug.		—	Sep.	1969	and 1970	61	46	−1	Martin (1975)
Storglaciären	Sweden	68° N	1370	Maritime	Jul.	1994	—	Aug.	1994		66	30	5	Hock and Holmgren (1996)
Qamanârssp sermia	Greenland	64° N	790	Maritime	Jun.	1980	—	Aug.	1986		67	38	−5	Braithwaite and Olesen (1990)
Ecology Glacier, King George Island	Antarctica	62° S	100	Maritime	Dec.	1990	—	Jan.	1991		64	29	7	Bintanja (1995)
Worthington Glacier, AK	USA	61° N		Maritime	Jul.	1967	—	Aug.	1967		51	29	21	Streten and Wendler (1968)
Worthington Glacier	Alaska	61° N	820	Maritime	Jul.	1967	—	Aug.	1967		51	29	20	Streten and Wendler (1968)
Qamanârssp sermia	Greenland	61° N	880	Maritime	Jun.	1979	—	Aug.	1983		70	28	2	Braithwaite and Olesen (1990)
Lemon Creek Glacier	Alaska	58° N	1200	Maritime	Aug.	1968	—	Aug.	1968		48	43	9	Wendler and Streten (1969)
Kryoto Glacier	Russia	55° N	810	Maritime	Aug.	2000	—	Sep.	2000		33	44	23	Konya et al (2004)
Hodges Glacier	South Georgia	54° S	375	Maritime	Nov.	1973	—	Apr.	1974		55	48	−3	Hogg et al (1982)
Glacier Lengua	Chile	53° S	450	Maritime	Feb.	2000	—	Apr.	2000		35	54	7	Schneider et al (2007)
Tyndall Glacier	Chile	51° S	700	Maritime	Dec.	1993					51	42	7	Takeuchi et al (1995a, 1995b)
Moreno Glacier	Chile	50° S	330	Maritime	Nov.	1993					54	49	−4	Takeuchi et al (1995a, 1995b)
Ampere Glacier	Kerguelen Island	49° S		Maritime	Jan.	1972	—	Mar.	1972		58	25	16	Poggi (1977)
Soler Glacier	Chile	46° S	378	Maritime	Nov.	1985					57	43	−1	Fukami and Naruse (1987)
Brewster Glacier	New Zealand	44° S	1770	Maritime	Dec.	2007	—	Mar.	2008		52	25	20	Gillett and Cullen (2011)
Brewster Glacier	New Zealand	44° S	1760	Maritime	Oct.	2010	—	Sep.	2012		64	23	11	Cullen and Conway (2015)
Brewster Glacier	New Zealand	44° S	1760	Maritime	Oct.	2010	—	Sep.	2012		37	53	3	Conway and Cullen (2016)
Franz Josef Glacier	New Zealand	43° S		Maritime	Feb.	1990					21	55	25	Ishikawa et al (1992)
Mount Cook National Park	New Zealand	43° S		Maritime	Oct.	1995	—	Nov.	1995		63	27	4	Neale and Fitzharris (1997)
Temple Basin	New Zealand	42° S	1450	Maritime	Oct.	1982	—	Nov.	1982		16	57	25	Moore and Owens (1984)
Niigata	Japan	37° N	360	Maritime	Dec.	2007	—	Apr.	2008		80	16	3	Matsumoto et al (2010)
Blue Glacier, WA	USA			Maritime	Jul.	1958	—	Aug.	1958		69	25	6	La Chapelle (1959)
Location						Observation Period					Surface Energy Flux (%)			Reference
Site	Country	Latitude	Altitude (m)	Climate							Rnet	H	E
Berkner Island	Antarctica	79° S	886	Others	Feb.	1995	—	Dec.	1997	−91	108	−17		Reijmer et al (1999)
McCall Glacier	Alaska	69° N	1715	Others	Jun.	2004	—	Aug.	2004	74	25	5		Klok et al (2005)
Storglaciären	Sweden	67° N	1370	Others	Jul.	2000	—	Sep.	2000	55	32	13		Sicart et al (2008)
Vestari Hagafellsjökull	Iceland	64° N	500	Others	Jun.	2001	—	Aug.	2007	63	27	10		Matthews et al (2015)
Vestari Hagafellsjökull	Iceland	64° N	1100	Others	Jun.	2001	—	Aug.	2009	74	20	6		Matthews et al (2015)
West Gulkana Glacier	Alaska	63° N	1520	Others	Jun.	1986	—	Jul.	1986	57	35	8		Brazel et al (1992)
Storbreen	Norway	62° N	1600	Others	Jul.	1955	—	Sep.	1955	56	31	13		Liestøl (1967)
Storbreen	Norway	62° N	1570	Others	Sep.	2001	—	Sep.	2006	76	17	8		Giesen et al (2009)
Nordbogletscher	Greenland	61° N	880	Others	Jun.	1979	—	Aug.	1983	71	29	2		Braithwaite and Olesen (1990)
Omnsbreen	Norway	60° N	1540	Others	Jun.	1968	—	Sep.	1969	52	32	15		Messel (1971)
Midtdalsbreen	Norway	60° N	1450	Others	Oct.	2000	—	Sep.	2006	67	24	10		Giesen et al (2008)
Midtdalsbreen	Norway	60° N	1450	Others	Sep.	2001	—	Sep.	2006	66	25	11		Giesen et al (2009)
Pasterze	Austria	47° N	2205	Others	Jun.	1994	—	Aug.	1994	77	20	4		Greuell and Smeets (2001)
Pasterze	Austria	47° N	2310	Others	Jun.	1994	—	Aug.	1994	73	22	5		Greuell and Smeets (2001)
Pasterze	Austria	47° N	2420	Others	Jun.	1994	—	Aug.	1994	72	24	4		Greuell and Smeets (2001)
Pasterze	Austria	47° N	2945	Others	Jun.	1994	—	Aug.	1994	77	19	4		Greuell and Smeets (2001)
Pasterze	Austria	47° N	3225	Others	Jun.	1994	—	Aug.	1994	79	20	1		Greuell and Smeets (2001)
St Sorlin	France	45° N	2760	Others	Jul.	2006	—	Aug.	2006	84	22	−5		Sicart et al (2008)
Indian Himalaya	India	32° N	3050	Others	Jan.	2005	—	Apr.	2005	98	3	−1		Datt et al (2008)
Zongo Glacier	Bolivia	16° S	5050	Others	Nov.	1999	—	Dec.	1999	103	24	−27		Sicart et al (2008)

The high humidity air mass is advected to Japan caused by the East Asian monsoon, and steep elevation gradients in the region from the coast of the Sea of Japan to the Japanese Alps formed the dry atmospheric conditions (see figure 5). A warm and humid air mass from the Eurasian continent is supplied to the coastal area of the Sea of Japan (Magono et al 1966) due to three factors:

• (1)  
  Siberian anticyclone formation on the East Eurasian continent in winter
• (2)  
  Cyclones in the Pacific Ocean
• (3)  
  Large amounts of heat and vapor from warm superficial Tsushima currents in the Sea of Japan (Kurooka 1957, Ninomiya 1968)

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This mechanism is known as the representative Japanese winter monsoon. It yields heavy snowfall in the coastal areas because the humid air mass originating from the winter monsoon is forced to lift over the Northern Japanese Alps (Estoque and Ninomiya 1976). Precipitation rarely occurs in the island area due to typical meteorological conditions. There is an amount of precipitation gradient between the windward (north west) area and the lee side (south east) area of the Northern Japanese Alps. A precipitation gradient similar to that in the Northern Japanese Alps was reported by Viale et al (2019). It showed that an East-West precipitation gradient in Patagonia, Chile made a distinction in climate between the windward area and the lee side area in the Andes Mountains. Moreover, Ikeda et al (2009) also showed that the climate condition in the lee side of the Northern Japanese Alps is dry, it is based on the snowpack observation of physical properties. The specificity of our surface energy balance analysis was established in addition to Ikeda et al (2009), the mechanism of snowfall in Japan, and the environment of seasonal snow cover.

The topographic condition, surrounding vegetation, and elevation difference are specific surface energy balance properties in AWS-K, AWS-N, and AWS-H. AWS-K is located at the bottom of a valley terrain; a cold air pool is typically formed. When a cold air pool is formed, a stable stratification in the boundary layer and radiation cooling occurs, and sublimation of the snowpack further cooled the snow surface further. Snowpack cooling frequently occurs in the AWS-K. The surface energy balance property in AWS-N resembled that of in AWS-K. There is no distinct difference in air temperature and atmospheric specific humidity with AWS-K and AWS-N. However, the turbulent energy flux in AWS-N was smaller than that of in AWS-K. Surrounding vegetation allowed the wind speed to decrease, resulting in the increased formation of a stable boundary layer and restraining turbulence.

The differences in air temperature due to the atmospheric pressure and the amount of snowfall among in AWS-K, AWS-N, and AWS-H make the characteristic of surface energy balance in AWS-H. The major meteorological differences among AWS-K and AWS-N against AWS-H are as follows: in AWS-H, (1) a period in which the daily mean air temperature turns to be positive is later and (2) the snow-covered duration is longer than those in the AWS-K and AWS-N. Thus, significant snow ablation in AWS-H begins later and stronger snow melt occurred in the late ablation period than those in the other two sites. In addition, incoming shortwave radiation, air temperature, and atmospheric humidity increase with time. This consideration, therefore, reveals the characteristics of AWS-H, that is, net shortwave radiation and turbulent heat flux large in the late ablation period, and rapid snow ablation is occurred, as well as the difference in the surface energy balance properties among the three AWS sites located in the same climate region.