- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications

Journal cover
Journal topic
**The Cryosphere**
An interactive open-access journal of the European Geosciences Union

Journal topic

- About
- Editorial board
- Articles
- Special issues
- Highlight articles
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
- EGU publications

**Brief communication**
19 Dec 2018

**Brief communication** | 19 Dec 2018

Brief communication: An ice surface melt scheme including the diurnal cycle of solar radiation

- Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

- Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

**Correspondence**: Uta Krebs-Kanzow (uta.krebs-kanzow@awi.de)

**Correspondence**: Uta Krebs-Kanzow (uta.krebs-kanzow@awi.de)

Abstract

Back to toptop
We propose a surface melt scheme for glaciated land surfaces, which only requires monthly mean short-wave radiation and temperature as inputs, yet implicitly accounts for the diurnal cycle of short-wave radiation. The scheme is deduced from the energy balance of a daily melt period, which is defined by a minimum solar elevation angle. The scheme yields a better spatial representation of melting than common empirical schemes when applied to the Greenland Ice Sheet, using a 1948–2016 regional climate and snowpack simulation as a reference. The scheme is physically constrained and can be adapted to other regions or time periods.

How to cite

Back to top
top
How to cite.

Krebs-Kanzow, U., Gierz, P., and Lohmann, G.: Brief communication: An ice surface melt scheme including the diurnal cycle of solar radiation, The Cryosphere, 12, 3923–3930, https://doi.org/10.5194/tc-12-3923-2018, 2018.

1 Introduction

Back to toptop
The surface melt of ice sheets, ice caps and glaciers results in freshwater
run-off, which represents an important freshwater source and directly
influences the sea level on centennial to glacial–interglacial timescales.
Surface melt rates can be determined from direct local measurements
(Ahlstrom et al., 2008; Falk et al., 2018). On a larger scale, melt rates can be
separated from integral observations such as the World Glacier Monitoring
Service (WGMS) (Zemp et al., 2015) or the changes in ice
mass detected by the Gravity Recovery and Climate Experiment (GRACE)
(Tapley et al., 2004; Wouters et al., 2014), which requires additional information about
other components of the mass balance, such as basal melting, accumulation,
sublimation and refreezing (Sasgen et al., 2012; Tedesco and Fettweis, 2012). In principal, the
surface melt rate can be deduced from the net heat flux in the surface layer,
as soon as the ice surface has been warmed to the melting point. For low
solar elevation angles, however, the net heat flux into the surface layer
usually becomes negative, the ice surface cools below the melting point and
melting ceases. Consequently, energy balance modelling provides reliable
surface melt rates only if sub-daily changes in ice surface temperature and
nocturnal freezing are taken into account. Where sub-daily energy balance
modelling is not feasible, surface melt is often estimated from empirical
schemes. A common approach is the positive degree-day method as formulated,
for example, in Reeh (1989). This particularly simple approach linearly
relates mean melt rates to positive degree days, PDD, in which PDD refers to
the temporal integral of near-surface temperatures (*T*) exceeding the
melting point. The PDD scheme is computationally inexpensive and requires
only seasonal or monthly near-surface air temperatures as input.
Consequently, it has been applied in the context of long climate simulations
(Charbit et al., 2013; Gierz et al., 2015; Heinemann et al., 2014; Roche et al., 2014; Ziemen et al., 2014) and
palaeo-temperature reconstructions (Box, 2013; Wilton et al., 2017).
Another empirical approach uses a linear function of solar radiation and
temperature to predict surface melt. This approach was originally used to
estimate ablation rates of glacial ice sheets (Pollard, 1980; Pollard et al., 1980). Related to this approach are the formally similar schemes ITM
and ETIM. ITM is for “insolation temperature melt equation” and is designed
to be used with monthly or seasonal forcing on long timescales with a
changing influence of insolation, e.g. van den Berg et al., 2008; Robinson et
al., 2010; de Boer et al., 2013. ETIM refers to the “enhanced temperature
index model” and usually is applied on regional scales and forced with
sub-daily observations from weather stations. This scheme is frequently
chosen for debris-covered glaciers, where surface albedo, and thereby the
effect of insolation, is partly independent of air temperature (e.g.
Pellicciotti et al., 2005; Carenzo et al., 2016). The empirical schemes,
however, incorporate parameters, which require a local calibration and which
are not necessarily valid under different climate conditions. Additionally,
Bauer and Ganopolski (2017) demonstrate that the PDD scheme fails to drive
glacial–interglacial ice volume changes as it cannot account for albedo
feedbacks. An alternative approach could be to modify and simplify energy
balance models in a way that reduces their data requirements and
computational costs. Krapp et al. (2017) have formulated a complete surface
mass balance model including accumulation, surface melt and refreezing
(SEMIC), which can be used with daily or monthly forcing. SEMIC predicts the
surface mass balance with a daily time step but implicitly accounts for the
sub-daily temperature variability in the surface layer of the ice to account
for diurnal freeze–melt cycles.

In the following, we deduce a more simplified scheme from the energy balance, which is formally similar to the ETIM and ITM schemes but incorporates physically constrained parameters. This new scheme only requires monthly means of temperature and solar radiation as input but implicitly resolves the diurnal cycle of radiation. In a first application on the Greenland Ice Sheet (GrIS) we use a simulation of Greenland's climate of the years 1948 to 2016 with the state-of-the-art regional climate and snowpack model MAR (Fettweis et al., 2017; Kalnay et al., 1996) as a reference.

2 The daily melt period and its energy balance

Back to toptop
The temperature of a surface layer of ice *T*_{i} must rise to the melting
point *T*_{0} before the net energy uptake *Q* of a surface layer can result in
a positive surface melt rate *M*. In the following, we define background melt
conditions on a monthly scale and melt periods on a daily scale.

The near-surface air temperature *T*_{a} usually does not exceed *T*_{0} if
(after winter) the ice is still too cold to approach *T*_{0} during daytime, so
that, on a monthly scale, surface air temperatures ${\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}$ (with the
bar denoting monthly means hereafter) can serve as an indicator of
background melting conditions. In the following we assume that monthly mean
melt rates $\stackrel{\mathrm{\u203e}}{M}>\mathrm{0}$ only occur if ${\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}>{T}_{\mathrm{min}}$, where
*T*_{min} is a typical threshold temperature that allows melt.

The daily melt period shall be that part of a day during which *T*_{i}=*T*_{0} and
*Q*≥0. Here, this period is assumed to be centred around solar noon, so
that it is also defined by the period Δ*t*_{Φ}, during which the Sun
is above a certain elevation angle Φ (this minimum elevation angle will
be estimated at the end of this section). Further, *q*_{Φ} is the ratio
between the short-wave radiation at the surface averaged over the daily melt
period, SW_{Φ}, and the short-wave radiation at the surface averaged over
the whole day, SW_{0}, as

$$\begin{array}{}\text{(1)}& {q}_{\mathrm{\Phi}}={\displaystyle \frac{{\mathrm{SW}}_{\mathrm{\Phi}}}{{\mathrm{SW}}_{\mathrm{0}}}}.\end{array}$$

Both Δ*t*_{Φ} and *q*_{Φ} depend on the diurnal cycle of short-wave
radiation and can be expressed as functions of latitude and time for any
elevation angle Φ if we include parameters of the Earth's orbit around
the Sun. Δ*t*_{Φ} and *q*_{Φ} will be derived in Sect. 2.1.

During the melt period, *Q*_{Φ} provides energy for fusion and results in a
melt rate, which, averaged over a full day Δ*t*, amounts to

$$\begin{array}{}\text{(2)}& M={\displaystyle \frac{{Q}_{\mathrm{\Phi}}\mathrm{\Delta}{t}_{\mathrm{\Phi}}}{\mathrm{\Delta}t\mathit{\rho}{L}_{\mathrm{f}}}},\end{array}$$

with latent heat of fusion ${L}_{\mathrm{f}}=\mathrm{3.34}\times {\mathrm{10}}^{\mathrm{5}}$ J kg^{−1} and the
density of liquid water *ρ*=1000 kg m^{−3}. The
energy uptake of the surface layer is

$$\begin{array}{}\text{(3)}& {Q}_{\mathrm{\Phi}}=(\mathrm{1}-A){\mathrm{SW}}_{\mathrm{\Phi}}+{\mathit{\u03f5}}_{\mathrm{i}}\mathrm{LW}\downarrow -\mathrm{LW}\uparrow +R\end{array}$$

with surface albedo *A*, long-wave emissivity of ice *ϵ*_{i}=0.95, downward and upward long-wave radiation, LW↓ and
LW↑ respectively, and the sum of all non-radiative heat
fluxes *R*. By definition,

$$\begin{array}{}\text{(4)}& \mathrm{LW}\uparrow ={\mathit{\u03f5}}_{\mathrm{i}}\mathit{\sigma}{T}_{\mathrm{0}}^{\mathrm{4}}\end{array}$$

is valid during the melting period, with $\mathit{\sigma}=\mathrm{5.67}\times {\mathrm{10}}^{-\mathrm{8}}$ W m^{−2} K^{−4}
being the Stefan–Boltzmann constant. Further *T*_{a}−*T*_{0} will be small
relative to *T*_{0} so that LW↓ can be linearized to

$$\begin{array}{}\text{(5)}& \mathrm{LW}\downarrow ={\mathit{\u03f5}}_{\mathrm{a}}\mathit{\sigma}{T}_{\mathrm{a}}^{\mathrm{4}}\approx {\mathit{\u03f5}}_{\mathrm{a}}\mathit{\sigma}({T}_{\mathrm{0}}^{\mathrm{4}}+\mathrm{4}{T}_{\mathrm{0}}^{\mathrm{3}}({T}_{\mathrm{a}}-{T}_{\mathrm{0}}\left)\right),\end{array}$$

with *ϵ*_{a}=0.76 being the emissivity of the near-surface
air layer if we neglect long-wave radiation from upper atmospheric layers.
Neglecting latent heat fluxes and heat fluxes to the subsurface and assuming
*R* to be dominated by the turbulent sensible heat flux, we parameterize $R=\mathit{\beta}({T}_{\mathrm{a}}-{T}_{\mathrm{0}})$, with the coefficient *β* representing the
temperature sensitivity of the sensible heat flux. The coefficient *β*
primarily is a function of wind speed *u* and according to
Braithwaite (2009) can be estimated as *β*=*α**u* with *α*≈4 W s m^{−3} K^{−1} at low altitudes. To find a
formulation that is based on monthly climate forcing we need to estimate the
mean melt period temperature from monthly mean temperatures. Near-surface air
temperature measurements from PROMICE stations on the GrIS reveal a good
agreement between monthly mean temperatures of the daily melt periods and the
PDD_{σ=3.5} approximated in Braithwaite (1985) from monthly mean
near-surface temperature ${\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}$ and a constant standard
deviation of *σ*=3.5 ^{∘}C (Fig. S1 in the Supplement).
Rewriting Eq. (3) for monthly means, we thus replace (*T*_{a}−*T*_{0})
with PDD${}_{\mathit{\sigma}=\mathrm{3.5}}\left({\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}\right)$. The above approximations
and assumptions then yield an implicitly diurnal energy balance model (dEBM),
which only requires monthly mean temperatures and solar radiation as
atmospheric forcing, while albedo may be parameterized as in common surface
mass balance schemes (Krapp et al., 2017):

$$\begin{array}{}\text{(6)}& \stackrel{\mathrm{\u203e}}{M}\approx \left({q}_{\mathrm{\Phi}}(\mathrm{1}-A){\stackrel{\mathrm{\u203e}}{\mathrm{SW}}}_{\mathrm{0}}+{c}_{\mathrm{1}}{\mathrm{PDD}}_{\mathit{\sigma}=\mathrm{3.5}}\left({\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}\right)+{c}_{\mathrm{2}}\right){\displaystyle \frac{\mathrm{\Delta}{t}_{\mathrm{\Phi}}}{\mathrm{\Delta}t\mathit{\rho}{L}_{\mathrm{f}}}},\end{array}$$

where

$$\begin{array}{ll}{\displaystyle}{c}_{\mathrm{1}}& {\displaystyle}={\mathit{\u03f5}}_{\mathrm{i}}{\mathit{\u03f5}}_{\mathrm{a}}\mathit{\sigma}\mathrm{4}{T}_{\mathrm{0}}^{\mathrm{3}}+\mathit{\beta}\\ {\displaystyle}& {\displaystyle}=\mathrm{3.5}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}\phantom{\rule{0.125em}{0ex}}{\mathrm{K}}^{-\mathrm{1}}+\mathit{\beta}\\ {\displaystyle}{c}_{\mathrm{2}}& {\displaystyle}=-{\mathit{\u03f5}}_{\mathrm{i}}\mathit{\sigma}{T}_{\mathrm{0}}^{\mathrm{4}}+{\mathit{\u03f5}}_{\mathrm{a}}{\mathit{\u03f5}}_{\mathrm{i}}\mathit{\sigma}\left({T}_{\mathrm{0}}^{\mathrm{4}}\right)\\ \text{(7)}& {\displaystyle}& {\displaystyle}=-\mathrm{71.9}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}\end{array}$$

for any month that complies with the background melting condition
${\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}>{T}_{\mathrm{min}}$. The sensitivity of the scheme to
the choices of *β* and to enhanced long-wave radiation due to cloud cover
or changed atmospheric composition is considered in Sect. 4.

Both *q*_{Φ} and Δ*t*_{Φ} strongly depend on latitude and month of
the year. Thus, a given combination of insolation and temperature forcing
yields different melt rates at different locations or seasons. The
sensitivity of the dEBM to latitude is further investigated in Sect. 4.

Finally, we use that *M*=0 in the moment when the Sun passes Φ and
formulates the instantaneous energy balance analogously to Eq. (6) as

$$\begin{array}{}\text{(8)}& (\mathrm{1}-A)\mathit{\tau}\widehat{{S}_{\mathrm{r}}}\mathrm{sin}\mathrm{\Phi}+{c}_{\mathrm{1}}\left({T}_{\mathrm{a}}\right(\mathrm{\Phi})-{T}_{\mathrm{0}})+{c}_{\mathrm{2}}=\mathrm{0},\end{array}$$

with *τ* representing the transmissivity of the atmosphere over the
melting surface, ${\widehat{S}}_{\mathrm{0}}$ being the solar flux density at the top of
the atmosphere (TOA), and the instantaneous air temperature *T*_{a}(Φ). The
transmissivity *τ* strongly depends on cloud cover, while ${\widehat{S}}_{\mathrm{0}}$
only weakly varies seasonally due to the eccentricity of the orbit of the
Earth. Assuming that *T*_{a}(Φ)≈*T*_{0} and using one estimate of $\mathit{\tau}{\widehat{S}}_{r}$ for the melt season of the entire model domain, we can
estimate

$$\begin{array}{}\text{(9)}& \mathrm{\Phi}=\mathrm{arcsin}{\displaystyle \frac{-{c}_{\mathrm{2}}}{(\mathrm{1}-A)\mathit{\tau}{\widehat{S}}_{r}}}\end{array}$$

independently of time or location. The dEBM's sensitivity to the range of possible elevation angles is discussed in Sect. 4.

The derivation of Δ*t*_{Φ} and *q*_{Φ} is based on spherical trigonometry and
fundamental astronomic considerations which, for instance, are discussed in detail in Liou (2002).
The elevation angle *ϑ* of the Sun changes throughout a day according to

$$\begin{array}{}\text{(10)}& \mathrm{sin}\mathit{\vartheta}=\mathrm{sin}\mathit{\varphi}\mathrm{sin}\mathit{\delta}+\mathrm{cos}\mathit{\varphi}\mathrm{cos}\mathit{\delta}\mathrm{cos}h\left(\mathit{\vartheta}\right),\end{array}$$

with the latitude *ϕ*, the solar
inclination angle *δ* and the hour angle *h*. The time during which the Sun
is above an elevation angle *ϑ* then is

$$\begin{array}{}\text{(11)}& \mathrm{\Delta}{t}_{\mathit{\vartheta}}={\displaystyle \frac{\mathrm{\Delta}t}{\mathit{\pi}}}h\left(\mathit{\vartheta}\right)={\displaystyle \frac{\mathrm{\Delta}t}{\mathit{\pi}}}\mathrm{arccos}{\displaystyle \frac{\mathrm{sin}\mathit{\vartheta}-\mathrm{sin}\mathit{\varphi}\mathrm{sin}\mathit{\delta}}{\mathrm{cos}\mathit{\varphi}\mathrm{cos}\mathit{\delta}}}.\end{array}$$

We assume that surface solar
radiation is proportional to the TOA radiation ${\widehat{S}}_{\mathrm{r}}$
throughout a day (i.e. we neglect the fact that atmospheric transmissivity
*τ* is increasing with elevation angle and assume that cloud cover
does not exhibit a diurnal cycle). The solar radiation during the period in
which the Sun is above a certain elevation angle *ϑ* is then

$$\begin{array}{}\text{(12)}& {\mathrm{SW}}_{\mathit{\vartheta}}={\displaystyle \frac{\mathit{\tau}\widehat{{S}_{\mathrm{r}}}}{\mathit{\pi}\mathrm{\Delta}{t}_{\mathit{\vartheta}}}}\left(h\left(\mathit{\vartheta}\right)\mathrm{sin}\mathit{\varphi}\mathrm{sin}\mathit{\delta}+\left(\mathrm{cos}\mathit{\varphi}\mathrm{cos}\mathit{\delta}\mathrm{sin}h\right(\mathit{\vartheta}\left)\right)\right).\end{array}$$

Equation (12) also allows us to estimate $\mathit{\tau}\widehat{{S}_{\mathrm{r}}}$ from SW_{0}.
Furthermore we can calculate the ratio between the mean short-wave radiation during the melt
period SW_{Φ} and the mean daily downward short-wave radiation SW_{0} at the surface independently of $\mathit{\tau}{\widehat{S}}_{\mathrm{r}}$:

$$\begin{array}{}\text{(13)}& {q}_{\mathrm{\Phi}}={\displaystyle \frac{{\mathrm{SW}}_{\mathrm{\Phi}}}{{\mathrm{SW}}_{\mathrm{0}}}}={\displaystyle \frac{h\left(\mathrm{\Phi}\right)\mathrm{sin}\mathit{\varphi}\mathrm{sin}\mathit{\delta}+\mathrm{cos}\mathit{\varphi}\mathrm{cos}\mathit{\delta}\mathrm{sin}h\left(\mathrm{\Phi}\right)}{h\left(\mathrm{0}\right)\mathrm{sin}\mathit{\varphi}\mathrm{sin}\mathit{\delta}+\mathrm{cos}\mathit{\varphi}\mathrm{cos}\mathit{\delta}\mathrm{sin}h\left(\mathrm{0}\right)}}{\displaystyle \frac{\mathrm{\Delta}t}{\mathrm{\Delta}{t}_{\mathrm{\Phi}}}}.\end{array}$$

3 First evaluation of the scheme

Back to toptop
The dEBM and two empirical schemes are calibrated and evaluated using the state-of-the-art regional climate and snowpack model MAR (Fettweis et al., 2017) as a reference.

The elevation angle used in the dEBM is estimated as $\mathrm{\Phi}=\mathrm{17.5}{}^{\circ}$, applying Eq. (9) with a typical albedo of 0.7 and $\mathit{\tau}{\widehat{S}}_{\mathrm{r}}=\mathrm{800}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}$ being roughly estimated
from the summer insolation in the ablation regions (Eq. 12). This
estimate corresponds to a transmissivity of *τ*≈0.6, which is in
good agreement with Ettema et al. (2010). Further, the dEBM is optimized to
reproduce the total annual Greenland surface melt averaged over the entire
MAR simulation by calibrating the background melting condition as
${\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}>-\mathrm{6.5}$ ^{∘}C and the parameter
$\mathit{\beta}=\mathrm{10}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}\phantom{\rule{0.125em}{0ex}}{\mathrm{K}}^{-\mathrm{1}}$. We then apply the scheme to
${\stackrel{\mathrm{\u203e}}{\mathrm{SW}}}_{\mathrm{0}}$, PDD${}_{\mathit{\sigma}=\mathrm{3.5}}\left({\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}\right)$
and albedo *A* from a MAR simulation of Greenland's climate (years 1948 to
2016) (Fettweis et al., 2017) and compare estimated melt rates with the
respective MAR melt rates.

Two empirical schemes are considered in the same way: a PDD scheme based on
PDD${}_{\mathit{\sigma}=\mathrm{5}}\left({\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}\right)$, defined and calibrated in
Krebs-Kanzow et al. (2018a), and a scheme, in the following referred to as
dEBM_{const}, which is a simplified variant of the dEBM where parameters
are constant in time and space:

$$\begin{array}{}\text{(14)}& M=\left(\right(\mathrm{1}-A){\stackrel{\mathrm{\u203e}}{\mathrm{SW}}}_{\mathrm{0}}+{k}_{\mathrm{1}}{\mathrm{PDD}}_{\mathit{\sigma}=\mathrm{3.5}}({\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}})+{k}_{\mathrm{2}}){\displaystyle \frac{\mathrm{1}}{\mathit{\rho}{L}_{\mathrm{f}}}},\end{array}$$

with ${k}_{\mathrm{1}}=\mathrm{10}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}\phantom{\rule{0.125em}{0ex}}{\mathrm{K}}^{-\mathrm{1}}$ and ${k}_{\mathrm{2}}=-\mathrm{55}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}$.
The dEBM_{const} is very similar to the ITM scheme and also uses
similar parameters to
Robinson et al. (2010) but includes PDD instead of temperature, which
particularly yields different results for low temperatures. As in
Robinson et al. (2010), we treat *k*_{2} as a tuning parameter to optimize the
scheme and also use ${\stackrel{\mathrm{\u203e}}{T}}_{\mathrm{a}}>-\mathrm{6.5}$ ^{∘}C as a
background melting condition.

The computational cost of the dEBM in this application is very similar to the
other two schemes, as parameters are computed only once prior to the
application. All schemes reproduce the total annual Greenland surface melt
averaged over the entire MAR simulation of 489 Gt with a
relative bias not exceeding 1 *%* (the mean bias is
0.4 Gt for the PDD scheme, −0.6 Gt for the
dEBM_{const} and −2.0 Gt for the dEBM). These calibrations
are primarily conducted to facilitate a fair comparison between the different
schemes and are not necessarily optimal for other applications.

Equations (6) and (14) appear formally similar, with the second term being
temperature dependent (the temperature contribution) and the first and
third term being independent of temperature and only dependent on solar
radiation (the radiative contribution). However, the respective
parameters cannot be compared directly, as Δ*t*_{Φ} and *q*_{Φ}
depend on latitude and month. Δ*t*_{Φ} and *q*_{Φ} modulate the
radiative contribution and Δ*t*_{Φ} modulates the temperature
contribution in Eq. (6). Figure 1a illustrates the radiative contributions and Fig. 1b the
temperature contributions diagnosed from the MAR simulation in comparison
to the respective contribution from the dEBM_{const}. On the GrIS the
radiative contribution can exceed 25 mm day^{−1} in the
summer months and the two schemes appear qualitatively similar. The radiative
contribution in the dEBM becomes less efficient for long melt periods, as the
same insolation must balance the outgoing long-wave radiation for a longer
time. On the other hand, the radiative contribution can also decrease towards
short melt periods if the Sun only marginally rises above the minimum
elevation angle at solar noon. At high latitudes, this effect becomes
important for higher estimates of the minimum elevation angles (Sect. 4). The
temperature contribution of the dEBM does not exceed
15 mm day^{−1} (Fig. 1b) and becomes more efficient with
longer melt periods and would agree with the dEBM_{const} for a melt period
of 18 h.

Atmospheric forcing (insolation and temperature) and albedo are obtained here
from the MAR output and are fully consistent with the MAR melt rates.
Consequently, we can evaluate the skill of the considered schemes
independently of the quality of the atmospheric forcing and the
representation of albedo. On the other hand, we cannot evaluate the
performance of the schemes for defective input. With respect to error
propagation, the PDD scheme might be more robust, as it only requires temperature as a forcing and only
distinguishes between snow and ice but does not require albedo. Given the
ideal input, all schemes reproduce the year-to-year evolution of the total
Greenland surface melt of the MAR simulation reasonably well (Fig. S3). The
PDD scheme yields increasing errors with intensifying surface melt rates,
which is not apparent for the dEBM_{const} and dEBM (Fig. 2). On the
other hand, dEBM_{const} particularly overestimates (underestimates)
melt rates for very short (long) melt periods. In comparison to the two
empirical schemes, the dEBM produces smaller local errors with biases being
pronounced only in a narrow band along the ice sheet's margins (Fig. 3).

4 Sensitivity to model parameters and boundary conditions

Back to toptop
In the above application, the parameters *β* for sensible heat and the
background melting condition *T*_{min} have served as tuning
parameters. The parameter $\mathit{\beta}=\mathrm{10}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}\phantom{\rule{0.125em}{0ex}}{\mathrm{K}}^{-\mathrm{1}}$ was
determined by optimizing the scheme to MAR melt rates. This value agrees
reasonably well with the moderate wind speeds found in PROMICE observations
during melt periods (Fig. S2). Changing *β* by ±20 *%* changes the
total annual Greenland surface melt by ±3 *%*. The choice of
${T}_{\mathrm{min}}=-\mathrm{6.5}$ ^{∘}C is in good agreement with observations,
which reveal no substantial melt for temperatures $<-\mathrm{7}$ ^{∘}C
(Orvig, 1954). Increasing the background melting condition
*T*_{min} particularly reduces the melt rates at high elevations,
while reducing *T*_{min} results in a longer melting season and
increases the annual surface melt. Using no background melting condition at
all, results in unrealistic melt rates at high elevations and would almost
double the predicted total Greenland surface melt. Changing *T*_{min}
by ±1 K changes the predicted mean annual surface melt by ±8 *%* for the MAR simulation used in this study. Intense surface melt is
usually accompanied by warm temperatures and is thus insensitive to the
choice of *T*_{min}. As refreezing particularly suppresses the
contribution of weak surface melt at low temperatures, the resulting run-off
can be expected to be less sensitive to the choice of *T*_{min}.

Melt schemes which do not include the diurnal cycle of radiation will predict the same melt rate for a given combination of insolation and temperature forcing, irrespective of latitude or season. By contrast, Fig. 4 indicates a strong sensitivity in the dEBM surface melt predictions to latitude in summer. According to the dEBM, a short melt period with intensive solar radiation is causing melt more effectively than a longer melt period with accordingly weaker solar radiation. This sensitivity is particularly prominent in high latitudes and may explain the latitudinal bias found in many studies which do not resolve radiation on sub-daily timescales (Krapp et al., 2017; Krebs-Kanzow et al., 2018a; Plach et al., 2018).

The TOA solar flux density $\widehat{{S}_{\mathrm{r}}}$ only depends on the
distance between the Earth and Sun and due to the eccentricity of the Earth's
orbit gradually varies by ±3.5 *%* from the solar constant from
December to July respectively. On orbital timescales this seasonal deviation
from the solar constant may amount to 10 *%*. Transmissivity *τ* and
emissivity *ϵ*_{a}, on the other hand, strongly depend on cloud
cover and atmospheric composition and additionally depend on the solar
elevation angle. As a consequence,
the minimum elevation angle Φ may be less then 13^{∘}
($\mathit{\tau}\widehat{{S}_{\mathrm{r}}}=\mathrm{1150}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}$ for clear-sky, intense
summer insolation). For overcast sky and weak summer insolation, we can
ultimately expect $\mathit{\tau}\widehat{{S}_{\mathrm{r}}}<\mathrm{400}\phantom{\rule{0.125em}{0ex}}\mathrm{W}\phantom{\rule{0.125em}{0ex}}{\mathrm{m}}^{-\mathrm{2}}$. In
that case, however, it is not justified to use the clear-sky emissivity in
Eqs. (5) and (7). Consequently, the
proposed scheme is no longer suitable, as net outgoing long-wave radiation will vanish and the energy
balance will become very sensitive to turbulent heat fluxes.
Applications aiming at continental
ice sheets driven by climatological force will be restricted to a much
narrower range of scenarios. As one can expect that transmissivity decreases
towards the morning and afternoon hours, it may be justified to reduce the
estimate of $\mathit{\tau}\widehat{{S}_{\mathrm{r}}}$ by a few percent. Figure 4
reveals that the scheme becomes very sensitive if the minimum elevation angle
Φ takes values close to or larger than the obliquity of the Earth. Under
such conditions, the duration of the melt period will vanish near the pole.
However, the scheme is remarkably insensitive to intensified insolation (and
accordingly reduced elevation angle Φ) or variations in the obliquity.
Accordingly, estimating the elevation angle locally and for each month using
Eq. (12), which is possible but computationally more expensive, does
not noticeably improve the skill of the dEBM (not shown).

5 Discussion and conclusion

Back to toptop
The presented new scheme for surface melt (dEBM) requires, like the insolation temperature melt scheme (ITM), monthly mean air temperatures and insolation as input but implicitly also includes the diurnal cycle. Together with suitable schemes for albedo and refreezing (Robinson et al., 2010), it may replace empirical surface melt schemes which are commonly used in ice sheet modelling on long timescales.

An application to the Greenland Ice Sheet indicates that the scheme may improve the spatial representation of surface melt in comparison to common empirical schemes. However, an evaluation against an independent data set is desirable. The most important advantage of the dEBM over empirical schemes may be that it can be globally applied to other ice sheets and glaciers and under different climate conditions, as parameters in the scheme are physically constrained and implicitly account for the orbital configuration.

In the presented formulation a threshold temperature serves as a prerequisite for surface melt on monthly timescales. This threshold temperature should be considered as a tuning parameter, as the representation of the ice–atmosphere boundary layer in Earth system models may differ considerably from the MAR simulation, which here has served as a reference. Furthermore, long-wave radiation and non-radiative heat fluxes are only crudely represented. Depending on the application, it may be advisable to adapt the parameterization of turbulent heat fluxes and long-wave radiation to different climate regimes in order to account for changed wind speed, humidity, cloud cover or greenhouse gas concentration.

The daily melt period is defined by a minimum solar elevation angle. Together
with the melt period, parameters in the dEBM depend on latitude and month of
the year but do not change from year to year if the minimum solar elevation
angle is kept constant and the orbital configuration remains the same. For
the Greenland Ice Sheet, a minimum solar elevation angle of
17.5^{∘} was roughly estimated from the mean summer insolation
normal to a surface at the bottom of the atmosphere. The dEBM is very
sensitive if the intensity of solar radiation is substantially weaker than in
the presented application (e.g. due to cloud cover or atmospheric water
content). In this case it is necessary to carefully re-estimate the minimum
elevation angle and to adjust the model parameters accordingly. Otherwise,
the scheme appears to be relatively insensitive to changes in the orbital
configuration and the parameters chosen in this study may be valid in a
wider range of settings.

The presented formulation has been designed for long Earth System Model applications, but it may be adapted for use in the context of climate reconstructions or applied on regional or local scales. Furthermore, having defined the daily melt period by the minimum elevation angle, it should also be possible to estimate the amount of refreezing by considering the energy balance of the remainder of the day, following a similar approach to Krapp et al. (2017).

Code availability

Back to toptop
Code availability.

A matlab version of the dEBM is available under https://github.com/ukrebska/dEBM/ (Krebs-Kanzow et al., 2018b).

Supplement

Back to toptop
Supplement.

The supplement related to this article is available online at: https://doi.org/10.5194/tc-12-3923-2018-supplement.

Competing interests

Back to toptop
Competing interests.

The authors declare that they have no competing interests.

Acknowledgements

Back to toptop
Acknowledgements.

We would like to thank Xavier Fettweis for providing MAR model output.
Further we are grateful for the valuable comments and constructive
suggestions from Alexander Robinson, Mario Krapp and an anonymous referee.
Uta Krebs-Kanzow is funded by the Helmholtz Climate Initiative REKLIM
(Regional Climate Change), a joint research project of the Helmholtz
Association of German research centres. Paul Gierz is funded by the German
Ministry of Education and Research (BMBF) German Climate Modeling Initiative
PalMod. This work is part of the project “Global sea level change since the
Mid Holocene: Background trends and climate-ice sheet feedbacks” funded by
the Deutsche Forschungsgemeinschaft (DFG) as part of the Special Priority
Program (SPP)-1889 “Regional Sea Level Change and Society”
(SeaLevel).

The article processing charges for
this open-access

publication were covered by a Research

Centre of the Helmholtz Association.

Edited by: Xavier Fettweis

Reviewed by: Mario
Krapp, Alexander Robinson,

and one anonymous referee

References

Back to toptop
Ahlstrom, A. P., Gravesen, P., Andersen, S. B., van As, D., Citterio, M., Fausto, R. S., Nielsen, S., Jepsen, H. F., Kristensen, S. S., Christensen, E. L., Stenseng, L., Forsberg, R., Hanson, S., Petersen, D., and Team, P. P.: A new programme for monitoring the mass loss of the Greenland ice sheet, Geol. Surv. Den. Greenl., 61–64, 2008. a

Bauer, E. and Ganopolski, A.: Comparison of surface mass balance of ice sheets simulated by positive-degree-day method and energy balance approach, Clim. Past, 13, 819–832, https://doi.org/10.5194/cp-13-819-2017, 2017. a

Box, J.: Greenland Ice Sheet Mass Balance Reconstruction. Part II: Surface Mass Balance (1840–2010), J. Climate, 26, 6974–6989, https://doi.org/10.1175/JCLI-D-12-00518.1, 2013. a

Braithwaite, R.: Calculation of degree-days for glacier-climate research, Zeitschrift für Gletscherkunde und Glazialgeologie, 20/1984, 1–8, 1985. a

Braithwaite, R. J.: Calculation of sensible-heat flux over a melting ice surface using simple climate data and daily measurements of ablation, Ann. Glaciol., 50, 9–15, https://doi.org/10.3189/172756409787769726, 2009. a

Carenzo, M., Pellicciotti, F., Mabillard, J., Reid, T., and Brock, B. W.: An enhanced temperature index model for debris-covered glaciers accounting for thickness effect, Adv. Water Resour., 94, 457–469, https://doi.org/10.1016/j.advwatres.2016.05.001, 2016.

Charbit, S., Dumas, C., Kageyama, M., Roche, D. M., and Ritz, C.: Influence of ablation-related processes in the build-up of simulated Northern Hemisphere ice sheets during the last glacial cycle, The Cryosphere, 7, 681–698, https://doi.org/10.5194/tc-7-681-2013, 2013. a

de Boer, B., van de Wal, R. S. W., Lourens, L. J., Bintanja, R., and Reerink, T. J.: A continuous simulation of global ice volume over the past 1 million years with 3-D ice-sheet models, Clim. Dynam., 41, 1365–1384, https://doi.org/10.1007/s00382-012-1562-2, 2013.

Ettema, J., van den Broeke, M. R., van Meijgaard, E., and van de Berg, W. J.: Climate of the Greenland ice sheet using a high-resolution climate model – Part 2: Near-surface climate and energy balance, The Cryosphere, 4, 529–544, https://doi.org/10.5194/tc-4-529-2010, 2010. a

Falk, U., López, D. A., and Silva-Busso, A.: Multi-year analysis of distributed glacier mass balance modelling and equilibrium line altitude on King George Island, Antarctic Peninsula, The Cryosphere, 12, 1211–1232, https://doi.org/10.5194/tc-12-1211-2018, 2018. a

Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017. a, b, c

Gierz, P., Lohmann, G., and Wei, W.: Response of Atlantic overturning to future warming in a coupled atmosphere-ocean-ice sheet model, Geophys. Res. Lett., 42, 6811–6818, https://doi.org/10.1002/2015GL065276, 2015. a

Heinemann, M., Timmermann, A., Elison Timm, O., Saito, F., and Abe-Ouchi, A.:
Deglacial ice sheet meltdown: orbital pacemaking and CO_{2} effects,
Clim. Past, 10, 1567–1579, https://doi.org/10.5194/cp-10-1567-2014, 2014. a

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a

Krapp, M., Robinson, A., and Ganopolski, A.: SEMIC: an efficient surface energy and mass balance model applied to the Greenland ice sheet, The Cryosphere, 11, 1519–1535, https://doi.org/10.5194/tc-11-1519-2017, 2017. a, b, c, d

Krebs-Kanzow, U., Gierz, P., and Lohmann, G.: Estimating Greenland surface melt is hampered by melt induced dampening of temperature variability, J. Glaciol., 64, 227–235, https://doi.org/10.1017/jog.2018.10, 2018. a, b

Krebs-Kanzow, U., Gierz, P., and Lohmann, G.: dEBM Melting Sceme Code, Tag version 1.0, available at: https://github.com/ukrebska/dEBM/, last access: 7 December 2018b.

Liou, K.-N.: An introduction to atmospheric radiation, Academic Press, second edition, New York, 2002. a

Orvig, S.: Glacial-Meteorological Observations on Icecaps in Baffin Island, Geogr. Ann., 36, 197–318, https://doi.org/10.1080/20014422.1954.11880867, 1954. a

Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., and Corripio, J.: An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier d'Arolla, Switzerland, J. Glaciol., 51, 573–587, https://doi.org/10.3189/172756505781829124, 2005.

Plach, A., Nisancioglu, K. H., Le clec'h, S., Born, A., Langebroek, P. M., Guo, C., Imhof, M., and Stocker, T. F.: Eemian Greenland SMB strongly sensitive to model choice, Clim. Past, 14, 1463–1485, https://doi.org/10.5194/cp-14-1463-2018, 2018. a

Pollard, D.: A simple parameterization for ice sheet ablation rate, Tellus, 32, 384–388, https://doi.org/10.1111/j.2153-3490.1980.tb00965.x, 1980. a

Pollard, D., Ingersoll, A., and Lockwood, J.: Response of a zonal climate ice-sheet model to the orbital perurbations during the Quaternary ice ages, Tellus, 32, 301–319, 1980. a

Reeh, N.: Parameterization of melt rate and surface temperature on the Greenland ice sheet, Polarforschung, 59, 113–128, 1989. a

Robinson, A., Calov, R., and Ganopolski, A.: An efficient regional energy-moisture balance model for simulation of the Greenland Ice Sheet response to climate change, The Cryosphere, 4, 129–144, https://doi.org/10.5194/tc-4-129-2010, 2010. a, b, c

Roche, D. M., Dumas, C., Bügelmayer, M., Charbit, S., and Ritz, C.: Adding a dynamical cryosphere to iLOVECLIM (version 1.0): coupling with the GRISLI ice-sheet model, Geosci. Model Dev., 7, 1377–1394, https://doi.org/10.5194/gmd-7-1377-2014, 2014. a

Sasgen, I., van den Broeke, M., Bamber, J. L., Rignot, E., Sorensen, L. S., Wouters, B., Martinec, Z., Velicogna, I., and Simonsen, S. B.: Timing and origin of recent regional ice-mass loss in Greenland, Earth Planet. Sc. Lett., 333, 293–303, https://doi.org/10.1016/j.epsl.2012.03.033, 2012. a

Tapley, B., Bettadpur, S., Ries, J., Thompson, P., and Watkins, M.: GRACE measurements of mass variability in the Earth system, Science, 305, 503–505, https://doi.org/10.1126/science.1099192, 2004. a

Tedesco, M. and Fettweis, X.: 21st century projections of surface mass balance changes for major drainage systems of the Greenland ice sheet, Environ. Res. Lett., 7, https://doi.org/10.1088/1748-9326/7/4/045405, 2012. a

van den Berg, J., van de Wal, R., and Oerlemans, H.: A mass balance model for the Eurasian ice sheet for the last 120,000 years, Global Planet. Change, 61, 194–208, https://doi.org/10.1016/j.gloplacha.2007.08.015, 2008.

Wilton, D. J., Jowett, A., Hanna, E., Bigg, G. R., van den Broeke, M. R., Fettweis, X., and Huybrechts, P.: High resolution (1 km) positive degree-day modelling of Greenland ice sheet surface mass balance, 1870-2012 using reanalysis data, J. Glaciol., 63, 176–193, https://doi.org/10.1017/jog.2016.133, 2017. a

Wouters, B., Bonin, J. A., Chambers, D. P., Riva, R. E. M., Sasgen, I., and Wahr, J.: GRACE, time-varying gravity, Earth system dynamics and climate change, Rep. Prog. Phys., 77, https://doi.org/10.1088/0034-4885/77/11/116801, 2014. a

Zemp, M., Frey, H., Gärtner-Roer, I., Nussbaumer, S. U., Hoelzle, M., Paul, F., Haeberli, W., Denzinger, F., Ahlstrøm, A. P., Anderson, B., Bajracharya, S., Baroni, C., Braun, L. N., Càceres, B. E., Casassa, G., Cobos, G., Dàvila, L. R., Delgado Granados, H., Demuth, M. N., Espizua, L., Fischer, A., Fujita, K., Gadek, B., Ghazanfar, A., Hagen, J. O., Holmlund, P., Karimi, N., Li, Z., Pelto, M., Pitte, P., Popovnin, V. V., Portocarrero, C. A., Prinz, R., Sangewar, C. V., Severskiy, I., Sigurdsson, O., Soruco, A., Usubaliev, R., and Vincent, C.: Historically unprecedented global glacier decline in the early 21st century, J. Glaciol., 61, 745–762, https://doi.org/10.3189/2015JoG15J017, 2015. a

Ziemen, F. A., Rodehacke, C. B., and Mikolajewicz, U.: Coupled ice sheet–climate modeling under glacial and pre-industrial boundary conditions, Clim. Past, 10, 1817–1836, https://doi.org/10.5194/cp-10-1817-2014, 2014. a

Short summary

We present a new surface melt scheme for land ice. Derived from the energy balance of melting surfaces, the scheme may be particularly suitable for long ice-sheet simulations of past and future climates. It is computationally inexpensive and can be adapted to changes in the Earth's orbit and atmospheric composition. The scheme yields a better spatial representation of surface melt than common empirical schemes when applied to the Greenland Ice Sheet under present-day climate conditions.

We present a new surface melt scheme for land ice. Derived from the energy balance of melting...

The Cryosphere

An interactive open-access journal of the European Geosciences Union