2.1. Model framework and setup

The model simulates daily surface albedo, impurity loadings, snow thickness and SMB for a unit square metre on an ice sheet or glacier and requires the inputs listed in Table 1. In general, our model can be forced by regional climate model output, while in this study we use AWS data in order to optimise the parameters and test the model directly with measurements.

Table 1.

Required forcings for the model.

Our model framework consists of seven components shown in Figure 2 and the common parameters are listed in Table 2. The model is developed in Mathematica (version 10, Wolfram Research, Inc., 2014) using self-coded solvers for the differential equations, with a time step of 1 day. The following sections describe each component in advancing order of the flow chart.

Fig. 2.

Flow chart of the surface albedo and surface mass-balance model for a time step of 1 day.

Table 2.

Standard physical parameters.

2.2. Snowpack

Snow depth (d) is required for the final SMB, and in order to distinguish between snow and ice surface. It is calculated by a balance between the solid precipitation rate P _solid and the melt rate M _s (Robinson and others, Reference Robinson, Calov and Ganopolski2010):

(1) $$ \displaystyle{{\rm d} \over {\rm d}t} d = P_{\rm solid} - M_{\rm s}\comma \quad d \in \lpar 0\comma\; d_{\rm max}\rpar . $$

If the snow depth exceeds d _max = 5 m w.e. (Robinson and others, Reference Robinson, Calov and Ganopolski2010; Fitzgerald and others, Reference Fitzgerald, Bamber, Ridley and Rougier2012), the snow depth is reset to 5 m and the surplus amount is added to the ice thickness, which accounts for the snow to ice metamorphism in the accumulation zone. The melt rate is obtained by considering the surface energy balance and is described in Section 2.7. The solid precipitation rate P _solid in (1) is based on a temperature-dependent fraction f(T) and the total precipitation rate P (Robinson and others, Reference Robinson, Calov and Ganopolski2010):

(2) $$ P_{\rm solid} = P \times f\,(T)\comma $$

where the surface temperature-depending fraction f(T) is empirically based on data of Greenland (Calanca and others, Reference Calanca, Gilgen, Ekholm and Ohmura2000; Bales and others, Reference Bales2009) and states that below a minimum temperature (T _min = −7°C) all precipitation is snow and above a maximum temperature (T _max = +7°C) rain:

(3) $$\eqalign {& f\lpar T\rpar = \matrix{ 1\comma \hfill & T \lt T_{\rm min}\comma \cr 0\comma \hfill & T \gt T_{\rm max}\comma \cr \cos\left({\displaystyle{{ T-T_{\rm min}} \over {T_{\rm max} - T_{\rm min}}}}\right)\left({\displaystyle {\pi \over 2}}\right)\comma \hfill & T_{\rm min} \lt T \lt T_{\rm max}.} }$$

2.3. Impurity accumulation

Particulate impurities such as BC and mineral dust as well as microbial material have four different sources (Fig. 3):

• • Atmosphere – distant sources: (k _I) by dry or wet deposition,

• • Atmosphere – local sources: (k _II) by regionally transported material from the surrounding tundra,

• • Flow: (k _III) by transport from the accumulation zone to the ablation zone where impurities meltout,

• • Biological: (k _IV) by local biological production of organic material on the ice or snow surface.

Fig. 3.

Cross section through an ice sheet showing the four different sources of impurities in the ablation zone. ELA stands for equilibrium line altitude.

The magnitude of each source depends on the impurity species. The biological source k _IV is only relevant for organic matter associated with microbial production. Organic matter was found to contribute only ~5% to the impurity mass on the ablation zone of the GrIS (Bøggild and others, Reference Bøggild, Brandt, Brown and Warren2010; Wientjes and others, Reference Wientjes, van de Wal, Reichart, Sluijs and Oerlemans2011; Takeuchi and others, Reference Takeuchi, Nagatsuka, Uetake and Shimada2014). Due to its relative low concentration and unknown effect on absorption we omit the biological production for now. Nevertheless, we still prepare the impurity accumulation in a way that biological activity can be included in future versions of the model.

The atmospheric sources (k _I and k _II) contribute to BC and dust accumulation both at higher elevations of the ice sheet and at the margin, while the meltout of englacial impurities, source k _III, only contributes in the ablation zone and when ice melts. The following equations are valid for BC (n = BC) and dust (n = dust).

Both atmospheric sources contribute directly to the impurity mass inside the snowpack ι _*,n (ng m⁻²) as well as the local biological production. The vertical distribution inside the snowpack is not considered, which is not necessary since the effect of impurities on snow albedo is not included in the model.

We assume that impurities remain within the snowpack after deposition. Therefore, the impurity concentration inside the snowpack is described by

(4) $$\eqalign {\displaystyle{{{\rm d} \iota_{{\ast} \comma n}} \over {{\rm d}t}} & = \left\{ \matrix {k_{{\rm I}\comma n} \lpar t\rpar + k_{{\rm II}\comma n}\lpar t\rpar + k_{{\rm IV}\comma n}\lpar t\rpar \comma \hfill & d \gt 0 \comma \cr 0\comma \hfill & d = 0.}\right. }$$

As ice is exposed (i.e. at snow depth d = 0) englacial impurities and the atmospheric sources and local production all contribute to the impurity mass on the ice surface ι _{ice, n} (ng m⁻²). The impurity accumulation is counteracted by a reduction term (ν _ice(t) ι _{ice, n} ), which is the removal by liquid water and is assumed to be the same for dust and BC. Ideally, ν _ice(t) changes with time and includes removal by rain and meltwater, but it is kept constant in this study due the currently limited knowledge about the surface processes. We assume no resuspension of impurities to the atmosphere, because the surface on which they reside is either wet or frozen. Further, we assume that once the snow cover disappears all the impurities in the snowpack are instantly added to the impurity content of the ice surface and no upward impurity flux from ice to snow:

(5) $$\eqalign {\displaystyle{ {\rm d} \iota_{{\rm ice}\comma n} \over {\rm d}t} & = \left\{ \matrix{ 0\comma \hfill & d \gt 0 \comma \cr k_{{\rm I}\comma n}\lpar t\rpar + k_{{\rm II}\comma n}\lpar t\rpar + k_{{\rm III}\comma n}\lpar t\rpar \cr + k_{{\rm IV}\comma n}\lpar t\rpar - \nu_{{\rm ice}}\lpar t\rpar \iota_{{\rm ice}\comma n}\comma \hfill & d = 0.}\right. }$$

The contribution of meltout of englacial impurities k _{III, n} (t), originating from ice flow, depends on the melt rate of ice and the englacial impurity concentration [ι _englacial,n ] (the square brackets denote fractions of weight in ppmw). We assume that the impurity content in superimposed ice (h _s.ice) is neglectable, since observations indicate a very small impurity concentration in superimposed ice (Chandler and others, Reference Chandler, Alcock, Wadham, Mackie and Telling2015) and impurities only accumulate on the snow surface during melt:

(6) $$\eqalign {k_{{\rm III}\comma n}\lpar t\rpar & = \left\{ \matrix{ 1000 \, \left[ \iota_{{\rm englacial}\comma n}\right] \, \displaystyle {{\rm d} h_{\rm ice} \over {\rm d} t} \rho_{\rm ice}\comma \hfill & h_{{\rm s}.{\rm ice}} = 0 \comma \cr 0\comma \hfill & h_{{\rm s}.{\rm ice}} \gt 0.} \right. }$$

where k _III,n (t) is in ng m⁻², h _ice is the thickness of ice (see 2.8) and ρ _ice is the density of ice in kg m⁻³.

2.4. Ice albedo

The albedo of ice depends on its specific surface area $\hat {S}$ , the effects of impurities (d α _c), the zenith angle of the sun (dα _θz ) and clouds (dα _clouds). Gardner and Sharp (Reference Gardner and Sharp2010) developed a parameterisation based on experiments with a radiative transfer model of snow and ice coupled to a similar model of the atmosphere:

(7) $$\eqalign { \alpha_{\rm ice}= \alpha_{\hat {S}} + {\rm d} \alpha_{\rm c} + {\rm d} \alpha_{\theta_z} + {\rm d} \alpha_{\rm clouds}\comma }$$

where $\alpha _{\hat {S}}$ is the albedo of clean ice with a zenith angle of 0 and no clouds. It is determined by the specific surface area $\hat {S}$ of ice in cm² g⁻¹, which depends on the size and distribution of air bubbles and cracks (Gardner and Sharp, Reference Gardner and Sharp2010)

(8) $$\alpha_{\hat {S}} = 1.48 - {\hat {S}}^{- 0.07}. $$

In this study, we use a specific surface area of 1.6 cm² g⁻¹ for ice with a density of 894 kg m⁻³ (Dadic and others, Reference Dadic, Mullen, Schneebeli, Brandt and Warren2013). The albedo reduction due to BC is modelled by the equation (Gardner and Sharp, Reference Gardner and Sharp2010)

(9) $$\eqalign { {\rm d} \alpha_{\rm c} = & \max \left(0.04 - \alpha_{\hat {S}}\comma \displaystyle {-\left [ c \right ] ^{0.55}\over 0.16 + 0.6\, {\hat {S}}^{0.5} + 1.8\, \left [ c\right ] ^{0.6}\, {\hat {S}}^{-0.25}} \right)\comma }$$

which is designed for concentrations of BC (c in ppmw), and the effect of dust can be included by adding a BC equivalent term. We use a scaling factor of 1/200 to account for the lower absorption of dust (Gardner and Sharp, Reference Gardner and Sharp2010; Dang and others, Reference Dang, Brandt and Warren2015). We define an effective concentration of BC and dust, which accounts for both englacial impurities and impurities located on the ice surface:

(10) $$ \left [ c\right ] = \left [ \iota_{{\rm eff}\comma BC}\right ] + \left [ \iota_{{\rm eff}\comma {\rm dust}}\right ] \times 1/200\comma $$

where [ι _{eff, n} ] is the effective aerosol concentration in ppmw, which depends on the englacial concentration as well as the active component of the impurities located at the ice surface. A fraction of impurities can accumulate in cryoconite holes, and be shielded from the low-standing sun of the Arctic (Bøggild and others, Reference Bøggild, Brandt, Brown and Warren2010). This is expressed by the active fraction ${\cal {F}}$ , which is assumed to be the same for BC and dust, in the equation

(11) $$\left [ \iota_{{\rm eff}\comma n}\right ] = \left [ \iota_{{\rm englacial}\comma n}\right ] + \left [ \iota_{{\rm ice}\comma n}\right ] \times {\cal F}\comma $$

where only the active fraction ${\cal {F}}$ contributes to the ice albedo reduction. A value of one means that all impurities are dispersed on the ice surface.

The impurity accumulation on ice in (5) is calculated in ng per square metre, while (9) requires fractions of weight (ppmw). We therefore use the following equation to convert between the two quantities:

(12) $$\left [ \iota_{{\rm ice}\comma n}\right ] = {\displaystyle{{\iota_{{\rm ice}\comma n}\,} \over {\rho_{\rm ice}\, d_{\rm eff}}}}\, 10^{6}\comma $$

where d _eff is the effective depth (in metres). This assumes that impurities on the ice surface have an equivalent effect as the same amount of impurities uniformly distributed in the ice over the effective depth. The upper boundary of the effective depth is linked to the extinction coefficient of ice. At 5 m depth less than 0.1% of the original energy remains, based on an extinction coefficient for clear white ice of 1.6 m⁻¹ (Bøggild and others, Reference Bøggild, Winther, Sand and Elvehøy1995). In contrast, van den Broeke and others (Reference van den Broeke2008b) found that part of the shortwave radiation is absorbed below the surface and causes melting down to 0.45 m. Since the impurities accumulate on the ice surface we assume that the penetration depth is still lower and assume that the shortwave radiation is already absorbed in the first 10 cm. The influence of different effective depths is addressed in Section 3.4.

2.4.1. Clouds

Clouds cause a spectral shift in incident radiation, which increases the albedo with increasing cloud optical thickness τ. The change of albedo caused by clouds is obtained by (Gardner and Sharp, Reference Gardner and Sharp2010)

(13) $$ {\rm d} \alpha_{\rm clouds} = {\displaystyle{{0.1 \tau \lpar \alpha_{\hat {S}} + d \alpha_{\rm c}\rpar ^{1.3}} \over {\lpar 1 + 1.5 \tau \rpar ^{\alpha_{{\hat {S}}}} }}\comma}$$

which depends, besides the cloud optical thickness τ, on the specific surface area and the impurity content.

The cloud optical depth is derived from the incoming longwave radiation (E _L ^↓) (van den Broeke and others, Reference van den Broeke, van As, Reijmer and Van de Wal2004; Munneke and others, Reference Munneke2011):

(14) $$\tau = c_1 \times e^{c_2 N_{\epsilon}- 1}\comma $$

where the values of the coefficients c ₁ = 2.09 and c ₂ = 2.49 are based on data of the K-transect (Munneke and others, Reference Munneke2011, Table 2) and are the mean values of coefficients derived for three stations on similar elevations. The ‘longwave-equivalent cloudiness’ N _ε is based on the differences in emissivity of clear and cloudy conditions and is 1 for overcast and 0 for clear conditions. The longwave-equivalent cloudiness is obtained from daily values of incoming longwave radiation and temperature of all stations (N = 6320, period 2009–15 for KAN_M and KAN_L, and 2011–15 for KAN_U).

2.4.2. Zenith angle of the sun

Albedo increases with increasing zenith angle because light is less likely to penetrate deep into ice or snow. Hence, the path length is shorter and the albedo is higher. This is parameterised by (Gardner and Sharp, Reference Gardner and Sharp2010)

(15) $${\rm d} \alpha_{\theta_z} = 0.53 \alpha_{\hat {S}} \lpar 1 - \lpar \alpha_{\hat {S}} + {\rm d} \alpha_{\rm c}\rpar \rpar \lpar 1 -\cos \theta_z\rpar ^{1.2}\comma $$

where θ _z is the zenith angle. Since we use a time step of 1 day we use an insolation-weighted zenith angle for the daily average (e.g. Hartmann, Reference Hartmann2016).

2.5. Snow albedo

We keep snow albedo unaffected by impurities in this study, since the concentrations are low and the effect on surface albedo in the ablation zone is short (see also Fig. S1 in the supplements). The same equations as for ice are used and the effect of clouds and the zenith angle (13 and 15) are still captured:

(16) $$\alpha_{\ast} = \alpha_{{\hat {S}}\comma \ast} + {\rm d} \alpha_{\theta_z} + {\rm d} \alpha_{\rm clouds}\comma $$

where $\alpha _{{\hat {S}},\,{\ast} }$ is the albedo of snow based on the specific surface area of snow ${\hat {S}},\,\ast $ which changes due to snow metamorphosis. An accurate evolution of ${\hat {S}},\,{\ast} $ requires a multi-layer snowpack model and a time step smaller than 1 day. Since we want to keep the snow component as simple as possible, we parametrise the evolution of $\alpha _{{\hat {S}},\,\ast }$ instead. The parameterisation is based on Douville and others (Reference Douville, Royer and Mahfouf1995). During conditions of melt (M _s > 0) the snow albedo decreases exponentially:

(17) $$\eqalign {\alpha_{\hat {S}\comma \ast}\lpar t\rpar = \lpar \alpha_{\hat{S}\comma \ast}\lpar t-\Delta t\rpar -\alpha_{\hat{S}\comma \ast\comma {\rm min}}\rpar e^{\tau_{{\rm f}}} + \Delta \alpha_{\hat{S}\comma {\rm fresh}\ast} \comma }$$

while during cold days α _* decreases linearly:

(18) $$\eqalign { \alpha_{\hat{S}\comma \ast}\lpar t\rpar = \alpha_{\hat{S}\comma \ast}\lpar t-\Delta t\rpar - \tau_a + \Delta \alpha_{\hat{S}\comma {\rm fresh}\ast} . }$$

Fresh snow has a higher specific surface area and increases $\alpha _{\hat {S},\,\ast }$ :

(19) $$\eqalign { \Delta \alpha_{\hat{S}\comma {\rm fresh}\ast} & = \left \{\matrix{ 0 \hfill & P_{{\rm solid}}=0 \comma \cr \displaystyle {P_{{\rm solid}} \Delta t\over w_{{\rm crn}}} \lpar \alpha_{\hat{S}\comma \ast\comma {\rm max}}-\alpha_{\hat{S}\comma \ast\comma {\rm min}}\rpar \hfill & P_{{\rm solid}} \gt0.} \right. }$$

$\alpha _{\hat {S},\,\ast }$ is limited between the minimum value $\alpha _{\hat {S},\,\ast ,\,{\rm min}}$ and the maximum value $\alpha _{\hat {S},\,\ast ,\,{\rm max}}$ . The decay parameters τ _f and τ _a influence the albedo reduction since the last snowfall. The parameter w _crn (in m w.e.) determines the increase during snowfall.

2.6. Surface albedo

The albedo at a geographical point on a glacier or ice sheet is determined by the surface type, clouds, the solar zenith angle and impurities. If the snow depth is below a critical mark d _crit the surface albedo is still influenced by the darker underlaying ice (Fig. 4). Above the critical snow depth d _crit the surface albedo is equivalent to the albedo of snow α _* (based on (van den Berg and others, Reference van den Berg, van de Wal and Oerlemans2008; Robinson and others, Reference Robinson, Calov and Ganopolski2010)):

(20) $$\eqalign { \alpha_{{\rm s}} & = \left \{ \matrix{ \alpha_{{\rm ice}}\comma \hfill & d = 0\comma \cr \alpha_{{\rm ice}} + \displaystyle {{d}\over {d}_{{\rm crit}}}\lpar \alpha_{\ast}-\alpha_{{\rm ice}}\rpar \comma \hfill & 0 \lt d< d_{{\rm crit}}\comma \cr \alpha_{\ast}\comma \hfill & d \ge d_{{\rm crit}}. } \right. }$$

This equation allows the snow albedo to be lower than the ice albedo, which could be the case, although rare, when debris-rich wet snow covers clean ice.

Fig. 4.

The relationship between ice albedo, snow albedo and impurities to surface albedo (α _s). The surface albedo is still influenced by the underlaying ice surface if the snow depth (d) is lower than the critical snow depth (d _crit).

2.7. Potential melt

We use an energy-balance model to derive the potential melt rate. Subsurface and heat flux from rain are both insignificant (van As and others, Reference van As2012) in the K-transect, which reduces the surface energy balance to

(21) $$\eqalign {& E_{{\rm M}}=E_{{\rm S}}^\downarrow \lpar 1- \alpha_{{\rm s}} \rpar + E_{{\rm L}}^\downarrow + E_{{\rm L}}^\uparrow + E_{{\rm H}} + E_{{\rm E}}\comma }$$

where fluxes are positive when they add energy to the surface. The incoming shortwave and longwave radiation E _S ^↓ and E _L ^↓ are external inputs. Outgoing longwave radiation E ^↑ _L is calculated by the Stefan–Boltzmann law with the surface temperature T _s. The surface temperature is obtained by balancing the right-hand side. Energy for melt E _M is available when the right-hand side can not be balanced, i.e. the surface temperature T _s is at the melting point. The turbulent heat fluxes of latent heat E _H and sensible heat E _E are calculated with different transfer coefficients C for snow and ice (see Table 2) (Cuffey and Paterson, Reference Cuffey and Paterson2010):

(22) $$ E_{{\rm H}}=0.0129 \, C\, p_{{\rm s}}\, \lpar T-T_{{\rm s}}\rpar \comma$$

(23) $$E_{{\rm E}} = 22.2 \, C\, u\, \lpar p_{{\rm w}} - p_{{\rm w\comma s}}\rpar \comma$$

where u is the wind speed, p _s is the surface pressure and p _w is the vapour pressure. The surface vapour pressure p _{w, s} is derived by assuming saturation, p _{w, s} = p_{w, sat}, with (WMO, 2014)

(24) $$\eqalign { p_{{\rm w\comma sat}} = 611.2\, e^{17.62\, T_{{\rm s}} / \lpar 243.12 + T_{{\rm s}}\rpar } \comma }$$

where p _{w, sat} is in Pa.

The potential melt rate M _s is derived from the available energy for melt E _M:

(25) $$M_{\rm s} = \displaystyle{{E_{{\rm M}} \over \rho_{\rm w}\, L_{\rm m}}\comma }$$

where ρ _w is the density of water, L _m is the latent heat of melting.

2.8. Surface mass balance

The change of ice thickness h _ice in m w.e. together with the snow depth forms the SMB component of the model. The equation accounting for refreezing reads (Robinson and others, Reference Robinson, Calov and Ganopolski2010)

(26) $$\eqalign { \displaystyle{{\rm d} \over {\rm d}t} h_{{\rm ice}} = \left \{\matrix{ M_{{\rm s}} r_{{\rm f}}\comma \hfill & d \gt 0 \comma \cr {\rm min}\, \lpar P_{{\rm solid}}-M_{{\rm s}}\comma 0\rpar \comma \hfill & d=0 \comma } \right. }$$

where r _f is the refreezing fraction within the snowpack as a function of the snow depth and the surface temperature T (Robinson and others, Reference Robinson, Calov and Ganopolski2010):

(27) $$\eqalign { r_{{\rm f}} & = \left \{ \matrix{ 0\comma \hfill & d = 0 \comma \cr r_{\rm max} f\lpar T\rpar \comma \hfill & 0 \lt d \le 1 \comma \cr r_{\rm max}+\left [ \lpar 1-r_{\rm max}\rpar \lpar d-1\rpar \right ] \comma \hfill & 1 \lt d \le 2 \comma \cr 1 \comma \hfill & d \gt 2 . } \right. }$$

When the snow depth is below 1 m the refreezing fraction is determined by the maximum fraction of refreezing r _max and the fraction of snow (3). Above 1 m, but below 2 m, the refreezing fraction increases linearly and reaches its maximum at 2 m. The thickness of superimposed ice h _s.ice is derived form the first part of (25).

2.9. Calibration and evaluation

We chose the free parameters based on data availability: the snow albedo parameters w _crn, $\alpha _{{\hat {S}},\,\ast ,\,{\rm min}}$ , $\alpha _{\hat {S},\,\ast ,\,{\rm max}}$ , τ _f, τ _a, critical snow depth d _crit, the active fraction of BC and dust on ice $\cal {F}$ and the reduction fraction on ice ν _ice. The ranges of parameters are shown in Figures 5, 6.

Fig. 5.

Albedo model results for station KAN_U from 2009 until the end of 2015 with missing data in 2014. Optimisation of snow parameters is performed on data of 01.01.2011 to 31.12.2012. The remaining period is used to test the albedo model at a location where ice is never exposed. Only data outside the grey areas are used during the optimisation, when radiation data are usually available. Model results are compared with AWS data (blue) and model results of MAR and downscaled results of RACMO. MAR and RACMO albedo are smoothed with a running average over 7 days to improve readability of the graph. The lower sections show the resulting parameters and the ranges used during the optimisation.

Fig. 6.

Model results of station KAN_M from 2009 until the end of 2015 with missing data in 2014. Panel (a) shows the resulting albedo compared with AWS data and simulations of MAR and RACMO. Panel (b) shows the results of the optimisation for nine scenarios of initial impurity loading and englacial concentration. Only data outside the grey areas are used during the optimisation, when AWS data are usually available. The lowest RMSD is obtained with the zero initial and high englacial concentration setting, and is marked by a red circle. The model run (orange) in (a and b) uses this parameter settings and the optimised snow parameters. Panel (c) shows surface height change compared with SMB measurements (blue squares).

Optimisation of the parameters in the equations for $\alpha _{\hat {S},\,\ast }$ is necessary since we apply them to $\alpha _{\hat {S},\,\ast }$ rather than the final snow albedo. Therefore, literature values of w _crn, $\alpha _{\hat {S},\,\ast ,\,{\rm min}}$ , $\alpha _{\hat {S},\,\ast ,\,{\rm max}}$ , τ _f, τ _a cannot be used.

The critical snow depth determines the rate of change from the summer to the winter surface albedo as well as the change from wet snow to bare ice and is found via the optimisation.

The active fraction $\cal {F}$ bundles all surface processes together and is a free parameter, which accounts for impurity dynamics on the ice surface and cryoconite hole formation. During optimisation $\cal {F}$ is allowed to vary over the range of 0.6–0.8, which means to 60–80% of the impurities are actively darkening the ice surface. This range of $\cal {F}$ values is based on unmanned aircraft observations at the K-Transect (Ryan and others, Reference Ryan, Hubbard, Stibal and Box2016) where ~60–80% of the surface was distributed impurities east of station KAN_M. Although this is not directly linked to our meaning of the active fraction, it still is related and reduces the range of expected values for the active fraction.

The runoff fraction on ice is also a free parameter, since it is currently not observed and its range is estimated from observed impurity masses on the GrIS ablation zone (Section 4.2).

The optimisation is done by minimising the RMS deviation (RMSD) between the modelled and observed albedo during the period of 20 March until 20 September when AWS albedo data are available. Optimisation is performed with the Mathematica function ‘NMinimize’, which automatically chooses an appropriate method such as Nelder–Mead, differential-evolution, simulated annealing and random search (Wolfram Research, Inc., 2014), and the process is stopped after a maximum of 1000 iterations.

The optimisation of the parameters is performed in two stages. First, the snow albedo parameters are optimised on data of KAN_U where ice is never exposed. In the second stage, the ice parameters are optimised on data of KAN_M, already using the optimised snow parameters. In the first stage, the parameters w _crn, $\alpha _{\hat {S},\,\ast ,\,{\rm min}}$ , $\alpha _{\hat {S},\,\ast ,\,{\rm max}}$ , τ _f and τ _a are optimised, and in the second stage at KAN_M the critical snow depth d_crit and the active fraction of BC and dust on ice $\cal {F}$ , the reduction fraction on ice ν _ice.

During optimisation we use the data of 2011/12 at KAN_U and 2010–12 at KAN_M, which leaves the rest of the data, and all the data of station KAN_L, for model evaluation. The simulated albedo is compared with AWS data and simulations of the regional climate models MAR and RACMO. MAR (v3.5.2) uses NCEP-NCARv1 reanalysis forcings (Fettweis and others, Reference Fettweis2017) and gives results on a 20 km grid, which we bilinearly interpolated to the AWS locations. RACMO uses ERA-40 and ERA-Interim forcings, and the native 11 km grid has been downscaled to 1 km (v2.3, Noël and others, Reference Noël2016) by using the values of the nearest grid point. In addition, SMB is compared with observations (Machguth and others, Reference Machguth2016) and simulations of MAR and RACMO.

2.10. Boundary conditions and initial conditions

The model requires initial values of snow depth and impurity surface concentration, in addition to the boundary conditions mentioned in Table 1. The initial snow depth is derived from sonic ranger data and are: at KAN_L 0.2 m w.e. on 01.01.2009, at KAN_M 0.5 m w.e. on 01.01.2009 and 0.25 m w.e. on 01.01.2010. For KAN_U, close to the equilibrium line, the initial snow depth is arbitrary set to 2 m, which does not effect the result, since ice is never exposed there.

Since direct observations of surface concentrations are lacking, we use three scenarios of initial surface concentrations during optimisation; one each for high, medium and no initial impurity loadings, respectively. In all scenarios, the initial snow impurity concentrations are zero, since model snow albedo is unaffected by impurities and impurity loadings on snow are comparably low.

The ‘high’ initial scenario corresponds to 200 g m⁻² dust and 1 g m⁻² BC, and the ‘medium’ scenario corresponds to 50 g m⁻² dust and 0.1 g m⁻² BC.

All the atmospheric boundary conditions in Table 1 are provided by the AWS, besides the precipitation which comes from MAR. The atmospheric input rate from distant sources (k _I) of BC is 0.001 g m⁻² a⁻¹ obtained from ice cores (Lee and others, Reference Lee2013). For dust the atmospheric input rate is 0.01 g m⁻² a⁻¹, based on model simulation (Mahowald and others, Reference Mahowald, Albani, Engelstaedter, Winckler and Goman2011) and ice core analysis (Albani and others, Reference Albani2015). The local atmospheric input (k _II) for both dust and BC is set to zero, since we do not distinguish between those two sources at the moment.

Similar to the three scenarios for initial conditions, the optimisation at KAN_M is performed with three scenarios of englacial concentrations based on the available data of BC (ice samples at S1 and S8, Wientjes and others, Reference Wientjes2012) and dust (Ruth, Reference Ruth2007). The scenario with high englacial concentrations uses 4 ng g⁻¹ BC and 3000 ng g⁻¹ dust, the medium concentrations scenario uses 2 ng g⁻¹ BC and 1000 ng g⁻¹ dust and the low concentration scenario 0.5 ng g⁻¹ BC and 10 ng g⁻¹ dust. These three englacial concentrations scenarios are applied to each initial condition scenario.

At KAN_L the BC concentration is 1 ng g⁻¹, based on the nearby S1 core (Wientjes and others, Reference Wientjes2012). The dust concentration is 200 ng g⁻¹ at KAN_L, based on NGRIP (Ruth, Reference Ruth2007) and the location outside the dark area.

3.1. Calibration

Figure 5 presents the station KAN_U with the results of the optimised snow albedo parameters and the corresponding simulated albedo (for SMB see Fig. S2 in the supplements). The obtained values are 0.506 for $\alpha _{\hat {S},\,\ast ,\,{\rm min}}$ , 0.660 for $\alpha _{\hat {S},\,\ast ,\,{\rm max}}$ , 0.215 for τ _f, 0.0034 m w.e. for w _crn and a reduction of $\alpha _{\hat {S},\,\ast }$ during dry conditions (τ _a) of 0.003 day⁻¹.

Figure 6 shows the optimisation result of the ice albedo parameters. The scenario with no initial impurities and high englacial concentrations gives the lowest deviation from albedo measurements. Therefore, this scenario delivers the best estimates of the parameters, which are 0.009 m w.e. for the critical snow depth d_crit, 0.028 day⁻¹ for the reduction fraction on ice ν _ice and 0.792 for the active fraction $\cal {F}$ .

3.2. Evaluation

The model is evaluated at all stations. At KAN_U (Fig. 5) modelled albedo is in good agreement with data, also outside the period, which was used for optimisation. The observed SMB during the period 2011–2014 is slightly negative, while all the models, MAR and RACMO and ours similarly produced a slightly positive SMB (Fig. S2 in the supplements).

At station KAN_M in Figure 6 (panel c) the simulated SMB is lower than the observed one and is between MAR and RACOMO. The simulated albedo follows the AWS data during summer when ice is exposed, while MAR and RACMO display a 0.2 too high ice albedo of ~ 0.6.

At lower elevations at KAN_L (Fig. 7) the albedo of ice is ~ 0.5 each summer. All models succeeded in a similar way to reproduce the ice albedo in summer, while all models failed to catch the variations in spring, which are lager than at KAN_M and likely caused by drifting snow. The resulting SMB at KAN_L of our model is between the other models, but lower than the observations (Fig. S3).

Fig. 7.

Simulated and observed albedo at KAN_L with the model parameters from the optimisation at KAN_M and KAN_U and the englacial concentration of BC of 1 and 200 ng g⁻¹ dust.

3.3. Station KAN_M in 2010

Figure 8 displays the simulations of the KAN_M station in the year 2010 in more detail, where the initial snow depth is set to 0.25 m w.e. (panel e) based on sonic ranger data and simulated snow depth is converted to metres with a snow density of 650 kg m⁻³. While in Figure 6 the snow depth on 01.01.2010 is a result of the snow depth evolution in 2009 (0.34 m w.e.). All initial impurity concentrations are zero, according to the best scenario in Figure 6.

Fig. 8.

Detailed results of station KAN_M in 2010. (a) Near-surface temperature from AWS, (b) daily precipitation from regional climate model MAR (c and d) show the evolution of dust (red) and BC (black) inside the snowpack and on the ice surface. Panel (e) shows snow depth evolution of the models and derived from sonic ranger data. (f) Surface type; s.i. ice stands for superimposed ice. (g) Surface albedo evolution of data and simulations. The AWS data in thick blue, MAR and RACMO simulations are smoothed (over 7 days). Panel (h) shows the albedo of snow and ice due to changes in the specific surface area. Panels (i–k) show components of the albedo for both snow and ice.

The amount of dust and BC inside the snowpack (panel (c)) builds up until all the snow has melted in late June (panel (e)). All the impurities from the snowpack are deposited on the ice surface, causing a jump of BC concentration in (d), while the input of dust from snow is comparably small and is not visible in (d).

When ice is exposed there is an influx of impurities from meltout and the constant atmospheric deposition. In late September, the amount of dust decreases because the amount of impurity loss exceeds gain by meltout and atmospheric deposition.

Panels (i–k) show the evolution of the albedo components of ice and snow. Panel (i) shows the increasing darkening effect of impurities on the albedo of ice caused by accumulating dust and BC. The effect of clouds on ice is smaller than that on snow due to the lower specific surface area of ice. One significant summer snowfall event is visible in late August, which the model captures, although with a too high albedo.

The simulated maximum amount of BC on the ice surface is 0.006 g m⁻² and of dust is 1.7 g m⁻². Hence, the mass of dust on the ice surface is 270 times larger than BC. The maximum amount in the snowpack is 0.31 mg m⁻² BC and 3.12 mg m⁻² dust.

3.4. Sensitivity

Figure 9 shows surface height change relative to the optimised setting. The boundary conditions are from KAN_M at 2010 repeated over 5 years. The sensitivity experiments are compared with the settings of KAN_M at 2010, described above. Corresponding plots of albedo and impurity loading can be found in Figure S5 in the supplements.

Fig. 9.

Simulations of surface height change relative to the optimised settings at KAN_M 2010 (orange) in (a). Panel (b) shows a zoom in on (a).

Atmospheric input has only a small effect on the final SMB, as can be seen in the experiment when atmospheric input is turned off (brown line). On the other hand, if meltout of impurities is prohibited the loss is about 4 m w.e. lower than the reference. Similarly changes in englacial concentrations have a significant effect on the SMB.

The upper limit of the effective depth d _eff of 5 m causes less mass loss after 5 years, since it influences the effect of impurities on the albedo of ice. Next, the specific surface area of ice has big effects on SMB. A 0.6 cm² g⁻¹ lower specific surface area of ice causes 1.8 m w.e. more ice loss. A much higher specific surface area $ \hat {S} $ of 10 cm² g⁻¹, i.e. ice with more air bubbles, causes a brighter surface and a smaller effect of impurities. Therefore, the specific surface of 10 cm² g⁻¹ causes more than 8 m w.e. less ice loss, compared with the reference.

A higher reduction fraction ν _ice of 0.05 causes impurities to runoff faster and therefore causes about 1 m w.e. less ice loss after 5 years due to a higher albedo. Similarly, a smaller ν _ice of 0.001, or 0.1% impurity mass loss per day, causes impurities to build up and an albedo decrease of 0.2 (Fig. S5).

In the next test, the surface temperature is increased by 2 and 5 K, with and without meltout of impurities. The effect of temperature on mass loss is negative and more negative when meltout is considered. This is a result of longer ice exposure and additional meltout caused by the higher temperatures (Fig. S5). At 5 K higher temperatures the difference between prohibited and activate meltout is 10 m w.e.

Finally, a different conversion factor between dust and BC is investigated. The lower factor of 1/100 causes a higher mass loss of ~1.8 m w.e., while a factor of 1/300 causes 0.8 m w.e. less mass loss.