Introduction
The response of glaciers to atmospheric forcing is of interest
as glaciers are seen as useful scalable proxy records of past climate (e.g.
Mölg et al., 2009a) and because the rapid changes occurring in many
glaciated regions have implications for both global sea level rise (Kaser et
al., 2006) and water resources (e.g. Jost et al., 2012). Reliable attribution
of past glacier states and prediction of future ones is dependent on a
thorough understanding of the physical processes operating at the glacier
surface that link glacier change with climate, that is, the surface mass
balance (SMB) and surface energy balance (SEB). For debris-free, mid-latitude
glaciers, the SMB is primarily the sum of the relative magnitudes of
accumulated solid precipitation and melt. While, in general, incoming
short-wave radiation (SW↓) is the major source of energy
for glacier melt, variations in SMB are considered to be forced by changes in
air temperature and precipitation (Oerlemans, 2005), through both
accumulation and melt processes. Reduced solid precipitation often results in
an albedo feedback that increases melt; thus increased air temperature can
result in enhanced melt if the amount of precipitation that falls as snow
decreases. Other mechanisms responsible for the efficient relationship
between air temperature and melt vary widely (Sicart et al., 2008), and
include the variability of turbulent sensible (QS) and latent (QL) heat
fluxes, incoming long-wave radiation (LW↓), and a (somewhat
spurious) covariance between air temperature and SW↓ in many
continental areas. The primary influence of air temperature on melt rate is
also modulated by other influences on the SEB such as surface albedo
(Oerlemans et al., 2009), humidity (Gillett and Cullen, 2011), and cloud
transmission (Pellicciotti et al., 2005).
The strong effect of clouds on glacier SEB has received increased attention
in the last decade. Advances in automatic weather station (AWS) deployment on
glacier surfaces (Mölg et al., 2009b), the availability of high-quality
radiation measurements (van den Broeke et al., 2004), and development of
methods to extract information about cloud cover in data sparse areas
(Kuipers Munneke et al., 2011), have allowed the variation of SEB and SMB
with cloud cover to be characterized in many areas. Sicart et al. (2010) show
that clouds dominate day-to-day variations in LW↓ in mountainous
areas while numerous studies detail the fundamental changes in SEB with
cloudiness that are often coincident with changes in glacier surface boundary
layer (SBL) properties (van den Broeke et al., 2006; Giesen et al., 2008;
Gillett and Cullen, 2011). Given their strong control on the SEB, and
coincidence with changes in SBL properties it is vital that the role of
clouds in altering the sensitivity of SMB to changes in atmospheric state
variables (especially air temperature) be assessed.
The glaciers of the Southern Alps of New Zealand occupy a unique position in
the westerly wind belt of the Southern Ocean, a region dominated by
mid-latitude atmospheric circulation (Tait and Fitzharris, 1998; Ummenhofer
and England, 2007). The large barrier the Southern Alps poses to the
prevailing winds creates a high precipitation environment, which, coupled to
the relatively low elevation of glacier termini (Hoelzle et al., 2007),
creates high mass turnover glaciers that have shown high sensitivity to
climatic variations in temperature-index glacier modelling studies (Anderson
et al., 2006; Oerlemans, 2010). For these reasons the glaciers of the
Southern Alps are seen as useful indicators of regional atmospheric
circulation in the southwest Pacific and form a vital component of
palaeoclimate work (e.g. Lorrey et al., 2007). While a change in
precipitation phase and the associated albedo feedback has been shown to be
an important component of the sensitivity of SMB to air temperature in New
Zealand as in other glaciated regions (Oerlemans 1997; Anderson et al.,
2006), there is a suggestion that increased turbulent (mainly sensible) heat
fluxes dominate variations in melt (Anderson et al., 2010). This has led some
authors to interpret past glacier fluctuations as a linear and direct proxy
for regional air temperature (e.g. Putnam et al., 2012), at the exclusion of
most other elements of the glacier–climate system.
It has been well established that synoptic-scale processes exert a strong
control on the SMB in the Southern Alps, with periods of 20th century glacier
advance and retreat associated with anomalies in the regional climate system
(Fitzharris et al., 2007). Given that this synoptic variability is closely
linked to inferred changes in cloudiness as well as air mass properties (Hay
and Fitzharris, 1988), and that these synoptic controls are thought to have
varied over palaeoclimatic timescales (Drost et al., 2007; Ackerley et al.,
2011), it is vital that the influence of clouds on SMB is separated out from
the influence of air mass properties (in particular air temperature). Recent
field studies on Brewster Glacier in the Southern Alps, have shown the high
frequency of cloudy conditions during all seasons (> 50 %
overcast conditions) as well as the significant and variable effect of clouds
on SW↓, LW↓, and net radiation (Rnet) (Conway et al.,
2015). In this context it is timely to examine in detail the influence of
clouds on glacier surface climate, SEB, and melt, as well as the manner in
which clouds alter the sensitivity of SMB to air temperature in the Southern
Alps.
This paper addresses these issues by resolving the SEB and SMB at a site in
the ablation zone of Brewster Glacier over a 22-month period in 2010–2012.
High-quality surface climate data presented in Cullen and Conway (2015) are
used to force an SMB model (Mölg et al., 2008) to estimate both SEB and
SMB terms over this period (measurement period). The cloud metrics presented
in Conway et al. (2015) are used to identify clear-sky and overcast
conditions and thus characterize surface climate, SEB, and melt energy during
each condition. To test the sensitivity of SMB to changes in surface climate
and radiative components, a more heavily parameterized version of the model
is used. This model allows us to separate the effects of changes to surface
climate and radiative properties, as well as assess the influence of clouds
on the sensitivity. The sensitivity analyses are run using a 2-year time
series (sensitivity period) that was constructed from data collected in the
measurement period. The following section provides a brief description of the
site, data sets, and modelling methods before the results and discussion are
presented in subsequent sections.
Map of Brewster Glacier showing AWS locations and
surrounding topography. Contour lines are at 100 m intervals. Long-term
mass-balance network (MB stakes) shown as filled circles. The glacier margin
shown is based on a 1997 GPS survey (Willis et al., 2009). The ridgeline to
the southeast of the glacier is the main divide of the Southern Alps.
The inset map shows the location of Brewster Glacier within New
Zealand.
Methods
Site description and instrumentation
Brewster Glacier is a small mountain glacier situated in the Southern Alps
immediately west of the main divide (Fig. 1). It experiences a temperate
maritime high precipitation environment. Annual precipitation is
approximately 6000 mm water equivalent (w.e.), while the annual air
temperature over the glacier surface at 1760 m a.s.l. is 1.2 ∘C
(Cullen and Conway, 2015). In comparison to other glaciers in the Southern
Alps, it has a somewhat lower average slope (16∘) but similar mean
and terminus elevation (Hoelzle et al., 2007). As it is located on the main
divide with relatively high exposure to synoptic weather systems, at the
midpoint of the north–south distribution of glaciers in the Southern Alps
(Chinn et al., 2012), it is likely to experience the atmospheric controls on
SMB that affect the Southern Alps in general.
Variables measured, sensor specifications, and mean annual values at
AWSglacier during the study period.
Variable
Instrument
Accuracy
Mean annual value
Air temperature (Ta)
Vaisala HMP 45AC
0.3 ∘C
1.2 ∘C
Relative humidity (RH)
Vaisala HMP 45AC
3 %
78 %
Wind speed (U)
RM Young 01503
0.3 m s-1
3.3 m s-1
Atmospheric pressure (p)
Vaisala PTB110
0.5 hPa
819 hPa
Incoming short-wave radiation (SW↓meas)
Kipp and Zonen CNR4
5 %a
140 W m-2
Outgoing short-wave radiation (SW↑)
Kipp and Zonen CNR4
5 %a
93 W m-2
Incoming long-wave radiation (LW↓meas )
Kipp and Zonen CNR4
5 %a
278 W m-2
Surface temperature (Ts)
Kipp and Zonen CNR4
1 ∘Cb
-2.7 ∘C
Precipitation (Pscaled)c
TB4 + Scaledc
25 %c
6125 mmc
Surface and sensor height
SR50a
± 1 cm
NA
a Uncertainty is estimated to be less than the
manufacturer's specifications as noted in van den Broeke et al. (2004) and
Blonquist et al. (2009).
b Based on a 5 W m-2
uncertainty in outgoing long-wave radiation.
c From AWSlake during snow-free period only. Pscaled is
based on scaled relationship between AWSlake and a lowland
station (Cullen and Conway, 2015). Uncertainty is estimated from fit of
scaled relationship.
Data from an automatic weather station (AWS) situated in the ablation area of
Brewster Glacier (AWSglacier) were used in this study (Fig. 1).
Table 1 gives details of instrumentation and annual average surface climate
variables at AWSglacier, while further details of the locality
and AWS instrumentation can be found in Cullen and Conway (2015).
Measurements at AWSglacier ran for 22 months from 25 October 2010
to 1 September 2012 (inclusive). Air temperature (Ta) shows a
moderate seasonal cycle (with a range of 8 ∘C), and air mass changes appear to override the subdued
diurnal range in Ta. Wind speed (U) is moderate with a
persistent down-glacier flow despite the small fetch and exposed location
(Conway, 2013). Humidity is high with average vapour pressure
(ea) exceeding that of a melting surface for 4 months during
summer. Cloud cover is frequent and associated with on-glacier wind direction
(Conway et al., 2015). Annual mass balance in the vicinity of
AWSglacier is generally negative, despite the large accumulation
(> 3 m w.e.) of winter snowfall during May to September. The
significant annual ablation (> 4 m w.e.) generally starts
during October, exposing an ice surface in early January and continuing till
April or later.
Data treatment and cloud metrics
Cullen and Conway (2015) describe the treatment of the AWS data in detail but
a summary of the main steps is given here. Raw Ta data were
corrected for the overestimation of Ta measured in the
unaspirated shields during times of high solar radiation and low wind speed.
This resulted in a mean correction to the original data set of
-0.7 ∘C. To facilitate SMB modelling, a continuous precipitation
data set (Pscaled) was constructed by comparing summer rain gauge
observations from a second AWS situated in the pro-glacial area
(AWSlake) to a nearby lowland rain gauge (R2 = 0.9 at a
daily level).
To construct a high temporal resolution record of observed SMB, surface
height observed using a sonic ranger (Cullen and Conway, 2015) was combined
with periodic snow density measurements. Snow pits near the start of snowmelt
indicated a consistent density approaching 500 kg m-3 during late
October (443 kg m-3 on 23 October 2010; 483 kg m-3 on 27
October 2011), while density during midwinter was more moderate
(320 kg m-3 on 18 July 2011). Thus, while the density of melting snow
during spring is relatively well constrained, the increasing density due to
subsurface processes (e.g. viscous compaction and melt – refreezing) during
the winter months produces some uncertainty in the relationship between
surface height and SMB. Beyond the snow–ice transition in early January, a
standard ice density of 900 kg m-3 was assumed, while short periods of
new snowfall were assigned a fresh snow density of 300 kg m-3 (Gillett
and Cullen, 2011).
The long-wave equivalent cloudiness (Nε) used in this study was
determined from measurements of LW↓ and theoretical upper
(overcast) and lower (clear-sky) values of LW↓ that are based on
surface level meteorological variables, a method that has been used
successfully in other glaciated areas (van den Broeke et al., 2006; Giesen et
al., 2008). The data set and specific methods used are presented in Conway et
al. (2015), but a brief summary is given below. At each half-hourly interval
a theoretical upper limit for LW↓ is set by applying the
Stefan–Boltzmann law to the observed Ta and an emissivity of 1.
A lower limit is set using the clear-sky model of Konzelmann (1994), which
has both Ta and ea as dependent variables. These two
curves are assumed to represent the minimum and maximum LW↓ at a
given Ta and ea, corresponding to cloudiness values
of 0 and 1, respectively. By assuming that cloudiness increases linearly
between these minimum and maximum values, Nε is then calculated
from measured Ta, ea and LW↓ at each
half-hourly interval. Following Giesen et al. (2008), clear-sky conditions
are defined when cloudiness values are smaller than 0.2 and overcast
conditions are defined as cloudiness values larger than 0.8.
The inclusion of ea, as well as Ta, as a dependent
variable in the calculation of theoretical clear-sky LW↓was
necessary, as clear-sky LW↓is strongly dependent on both variables
at this temperate location (Durr and Philipona, 2004; Conway et al., 2015).
The effect of this is to include a larger proportion of days in the clear-sky
category, as some clear-sky days with high ea (and
LW↓) would have been excluded had only Ta been used in
the calculation of clear-sky LW↓. A comparison to cloudiness
derived from incoming short-wave measurements gave a correlation coefficient
of 0.89 and a root-mean-square difference (RMSD) of 0.19 (Conway et al.
2015), suggesting the method is a satisfactory approach to assess cloudiness
at this site.
Though not directly comparable to traditional cloud fraction metrics based
on manual or sky camera observations, Nε effectively
characterizes the impacts of clouds on surface radiation fluxes. It also has
the advantage over metrics based on SW↓, in that it provides 24 h coverage and is not affected by solar zenith angle or multiple
reflections between the surface and atmosphere.
Configuration of SEBmr and SEBpr, showing input data and
references used in the calculation of radiation terms in each configuration.
Variable
Model version
Reference and/or input data
α
SEBmr
Accumulated albedo; van den Broeke et al. (2004)
SEBpr
Oerlemans and Knap (1998) (Pscaled, Ta)
SW↓
SEBmr
SW↓surface
SEBpr
Conway et al. (2015) (Nε, Ta, RH)
LW↓
SEBmr
LW↓meas
SEBpr
Conway et al. (2015) (Nε, Ta, RH)
Input parameter uncertainty introduced in Monte Carlo
simulations of SMB uncertainty.
Input parameter
Value(s)
Systematic (random) error
Model
Roughness length for momentum (z0v)a
3.6 × 10-3 m
z0v × 10^ NORMRND (0,0.274)
SEBmr, pr
Rain/snow threshold (Tr/s)b
1.0 ∘C
0.3 (0.5)
SEBmr, pr
Albedo of surface (αsnow, αfirn, αice)b
0.95 (αsnow) 0.65 (αfirn) 0.42 (αice)
0.05
SEBpr
Constant for cloud extinction coefficientc
0.1715
0.0048 (0.0048)
SEBpr
Multiplier for cloud extinction coefficientc
0.07182 hPa-1
0.0324 (0.0324)
SEBpr
Albedo of surrounding terraind
0.45
0.1
SEBpr
Clear-sky emissivity constante
0.456 Pa-1 K
0.0204 (0.0204)
SEBpr
a Standard deviation of z0v (Conway and Cullen,
2013). NORMRND is a MATLAB function that selects a random number from a
normal distribution with mean of 0 and standard deviation of 0.274;
b Machguth et al. (2008); c 95 %
confidence interval of optimized coefficients (Conway et al., 2015). Limited
to 0.95; d Assumed; no random errors as terrain albedo
will not vary at this timescale (30 min); e RMSD of
clear-sky values (Conway et al., 2015).
Model description
An SMB model (Mölg et al., 2008) was used to resolve surface energy and
mass fluxes at AWSglacier for the full 22-month study period. A
full description of the model is given in Mölg et al. (2008, 2009a), but
a short description of the parameterization of each term is given here. The
model computes SMB as the sum of snow accumulation, melt, refreezing of
liquid water in the snowpack, and mass fluxes of water vapour (deposition and
sublimation), while surface temperature (Ts) is less than
273.15 K. Fluxes of vapour while the surface is melting are not
included directly in the SMB, as condensation and evaporation add and remove
mass from the liquid meltwater at the surface, respectively. The model uses
Ts as a free variable to close the SEB (Eq. 1) at each 30 min
time step:
QM=SW↓(1-α)+LW↓-σεTs4+QS+QL+QR+QC,
where QM is the energy for surface melt while
Ts = 273.15 K, SW↓ is the incoming solar
radiation, α is the albedo, LW↓is the incoming long-wave
radiation, σ is the Stefan–Boltzman constant
(5.67 × 10-8 W m-2), ε is the emissivity of
snow/ice (equal to unity), QS and QL are the turbulent sensible and latent
heat fluxes, respectively, QR is the rain heat flux, and QC is the conductive
heat flux through the glacier subsurface. The convention used is that energy
fluxes directed towards the surface are positive.
Two different configurations of the model are presented in this paper,
distinguished only by their treatment of surface radiation fluxes. For the
first, SEBmr, we used measured values of SW↓, LW↓, and
albedo from AWSglacier (Table 2) to provide best estimates of SEB
and SMB terms for analysis over the measurement period. For the second,
SEBpr, we used parameterized radiation fluxes (Table 2) to assess the
sensitivity of the SMB to changes in surface climate (detailed further in
Sect. 2.5). All other energy fluxes are calculated consistently between
configurations. QR is calculated using Pscaled, assuming rain
temperature is equal to Ta. New snow was calculated from
Pscaled using a rain/snow threshold (Tr/s) of
1 ∘C and a fixed density of 300 kg m-3. The iterative SEB
closure scheme of Mölg et al. (2008) was used to calculate
Ts, with QC being calculated as the flux between the surface and
the top layer of the 12-layer subsurface module (subsurface levels: 0.1,
0.2, 0.3, 0.4, 0.5, 0.8, 1.4, 2, 3, 5, and 7 m). Penetrating short-wave
radiation was not included in the model, as the subsurface temperature
profile was not measured throughout the study period; hence the optimization
of a penetrating short-wave radiation scheme would be subject to large
uncertainty. The depth, density, and temperature (isothermal at
0 ∘C) of the snowpack was prescribed at the start of the measurement
period from snow-pit measurements (see Sect. 2.2), while the bottom
temperature in the subsurface module was held fixed at 0 ∘C.
The turbulent heat fluxes, QS and QL, were calculated using a
bulk-aerodynamic approach using the Clog parameterization as described
by Conway and Cullen (2013). The roughness lengths for momentum (z0v),
temperature (z0t), and humidity (z0q) over an ice surface at
AWSglacier are well constrained by in situ measurements
(z0v = 3.6 × 10-3 m, z0t=z0q = 5.5 × 10-5 m; Conway and Cullen, 2013), though
spatial and temporal variability is still probable. A further period of eddy
covariance measurements over a spring snow surface (27 October to 3 November
2011) showed a log-mean value for z0v of 1.8 × 10-3 m
(σ=1.3 × 10-2 m, n=31), using the same filtering
criterion as Conway and Cullen (2013). No reliable estimates of z0t or
z0q were possible because of the large uncertainties involved with the
small temperature and vapour pressure gradients experienced during this
period. Given the similar, but more uncertain, z0v over snow and the
large effect of z0t on the effective roughness length which tends to
counter a change in z0v (Conway and Cullen, 2013), roughness lengths
derived over ice were adopted for the entire period.
Estimation of uncertainty using a Monte Carlo approach
To estimate uncertainty in modelled SMB, a series of Monte Carlo simulations
were made covering the range of input data and parameter uncertainty expected
for each configuration of the model (SEBmr and SEBpr). Table 3 shows the
parameter uncertainty introduced for each configuration, while input data
uncertainty was kept consistent with that used in Conway and Cullen (2013)
and is given in Table 1. For both configurations, 5000 runs of the
measurement period were made, with systematic and random errors being
assigned to each input variable before each simulation and time step,
respectively. Errors were calculated by multiplying the uncertainties
associated with each input variable (Tables 1 and 3) by normally distributed
random numbers (μ = 0; σ=1), with the exception of z0v
which was logarithmically transformed before the uncertainty was applied. The
5000 SMB time series computed for each configuration were subjected to a
first-order check, using measured Ts as a proxy for a realistic
simulation of the SEB. Runs were removed when 30 min modelled Ts
had an RMSD > 1.5 K or R2 < 0.9, which removed
∼ 10 % of runs from each ensemble. The remaining runs were then
used to compute an ensemble mean and standard deviation for the SMB
accumulated over 1-day and 10-day periods in addition to the full
measurement period. Runs that did not correctly predict the accumulated SMB
at the end of the measurement period were not removed, as it was unknown if
any systematic errors would remain constant over the study period. Thus, the
model uncertainty over a shorter time period (e.g. 1 or 10 days) was kept
independent of the final “correct” accumulated SMB.
Daily average albedo observed at AWSglacier (red) during
the measurement period and modelled in SEBpr (blue) using the expressions of
Oerlemans and Knap (1998), with locally optimized coefficients.
Mass-balance sensitivity configuration
To assess the mass-balance sensitivity (ΔSMB) at
AWSglacier, further runs were made with the SEBpr configuration
using a hybrid 2-year data set (sensitivity period). The goal was not only to
show the extent to which elements of the climate system could force SMB
changes but also to understand how uncertainty in model input data or
parameterization impacted estimates of SMB. Because the measurement period
started in spring, the initial depth and density of the snowpack was
prescribed in these runs. However, a realistic evolution of snow depth with
perturbations in surface climate (especially Ta) is required to
assess ΔSMB, i.e. ΔSMB is assessed with accumulation seasons
preceding ablation seasons. To this end, a hybrid 2-year data set was
constructed using data from AWSglacier by rearranging the
measurement period time series. The particular periods used were (in order) 1
May to 1 September 2012, 2 September to 24 October 2011, and 25 October 2010
to 30 April 2012. This gave two full SMB seasons (1 May–30 April) in
sensitivity runs and retained variability in the input data without relying
on data from off-glacier sources. Fortunately, the snow depth predicted by
SEBpr at the end of the first hybrid accumulation season matches that at the
start of the measurement period (25 October 2010) so the evolution of
snow depth (and albedo) during the remainder of the sensitivity run is
comparable with that in the measurement period.
To enable the amount of solid precipitation to alter albedo within SEBpr,
albedo was simulated using the parameterization of Oerlemans and Knap (1998).
This scheme computes albedo from three values representative of fresh snow
(αfrsnow), firn (αfirn), and ice
(αice), accounting for the evolution of fresh snow to firn
through an e-folding constant (t*) which describes the characteristic
albedo timescale. Two modifications were made to the scheme (Mölg et al.,
2012). Firstly, when new snowfall is removed by melt, the albedo reverts back
to the albedo of the underlying surface. Secondly, a daily total snowfall in
excess of 5 cm (depth) was introduced as a threshold above which the new
snowfall impacts albedo, as small snowfall is most likely redistributed into
crevasses and hollows on the glacier surface, where it will have a minimal impact on the
albedo.
An analysis of measured albedo (αacc) at AWSglacier
allowed local values of αfrsnow (0.95),
αfirn (0.65), and αice (0.42) to be defined
(Fig. 2). The higher local values are likely indicative of lower levels of
contaminants that are responsible for reduced albedo at other sites
(Oerlemans et al., 2009) and a lack of debris surrounding Brewster Glacier. A
better fit to the evolution of measured albedo was also found by decreasing
t* to 10 days, which seems reasonable given the higher rate of melt (and
therefore snow metamorphism) in this maritime environment. Figure 2 also
shows a marked difference in ice surface albedo between the two seasons. It
is unclear if this difference reflects changes over a large spatial scale or
if a localized increase in sediment observed in the vicinity of
AWSglacier during the summer of 2012 contributed to the decrease
in albedo during the second season. Without a clear basis for this variation,
a mean value of αice = 0.42 was adopted for both
seasons.
The value of ΔSMB was computed by conducting runs with SEBpr over the sensitivity
period, introducing a range of systematic perturbations to input data and
parameters (introduced in Sect. 3.4) and comparing SMB between each run. To
calculate variations in ΔSMB with cloudiness, ΔSMB was computed
at each model time step (i.e. mm w.e. 30 min-1) for each perturbation
run. Model time steps were then selected based on cloudiness
(Nε) and a monthly average produced for clear-sky and overcast
conditions. For ease of interpretation, ΔSMB was converted to a daily
rate (mm w.e. day-1) by multiplying half-hourly ΔSMB by the
number of model time steps within a day (48). By definition, the sum of
ΔSMB for each time step within a year is equal to the accumulated
ΔSMB of the entire year, which is the more commonly reported value
(e.g. 1.5 m w.e. yr-1).
Results
Model evaluation
Both configurations of the SMB model (SEBmr and SEBpr) were validated against
observed Ts and SMB during the measurement period. Modelled
Ts from reference runs of both configurations agreed well with
Ts calculated from measurements of outgoing long-wave radiation
(Fig. 3). Errors at the 30 min time step were comparable to other studies
(van den Broeke et al., 2011), and monthly averages indicated no seasonally
dependent errors in the SEB. Both configurations successfully simulated the
large accumulation and ablation observed at AWSglacier during the
measurement period (Fig. 4). SMB during the first accumulation season was
within ± 10 % of that observed (Table 4), which was encouraging
given the uncertainties in the scaled precipitation data set and rain/snow
threshold. SEBmr showed small discrepancies in modelled ablation (around
10 %) for the ice surface in the first season and the snow surface in the
second season (Table 4). SEBpr showed a similar performance, with an
underestimate of ablation for the ice surface in the second season likely
related to the lower albedo observed during this season (Fig. 4). Despite
these small deviations, both configurations produced SMBs over the two
seasons that were well within the accumulated uncertainty due to measurement
and parameter errors (grey shading in Fig. 4). The small discrepancies
between modelled and observed ablation could have been removed, perhaps
through specifying different zov for snow and ice surfaces.
However, given that the deviations were not consistent between each season
and model, both models exhibited large accumulated uncertainty, and our
interest was primarily at shorter timescales, we found no strong reasoning
for tuning model parameters to fit model values precisely.
Observed versus modelled surface temperature for (a) SEBmr
and (b) SEBpr runs. Red dots are 30 min averages, while black dots
are monthly averages.
Accumulated SMB during the measurement period as modelled by the
reference runs of SEBmr and SEBpr. The points give observed mass balance from
periodic stake and snow-pit measurements. The SMB for selected ablation and
accumulation periods (shown as Abl1 snow etc.) are given in Table 4. The
shaded envelope shows ± 1 standard deviation from the mean of SEBmr,
calculated using Monte Carlo simulations (see Sect. 2.4 for details).
Observed versus modelled mass balance for (a, b) SEBmr and
(c, d) SEBpr over 1-day and 10-day periods. Error bars show
± 2σ from the ensemble mean values. The solid diagonal line is a
1:1 line.
Observed and modelled SMB (m w.e) for selected periods between
stake measurements in ablation (Abl) and accumulation (Acc) seasons. Figure 4
shows the length of each period.
Period
Observed
SEBmr
SEBpr
Abl1 snow
-1.74
-1.78
-1.67
Abl1 ice
-3.35
-2.92
-3.28
Acc1
1.52
1.40
1.46
Abl2 snow
-1.51
-1.78
-1.48
Abl2 ice
-1.94
-1.87
-1.66
We also compared SMB over 1-day and 10-day periods to ensure we could
correctly simulate the large temporal variability in accumulation and
ablation with each configuration of the model (Fig. 5). SEBmr effectively
captured the large variability in SMB during both accumulation and ablation
seasons with maximum 10-day ablation and accumulation rates on the order of
50 mm w.e. day-1 (Fig. 5b). A consistent bias in ablation was not
observed, confirming our decision not to tune modelled melt exactly over the
season. The significant number of large daily ablation events
(> 50 mm w.e. day-1) observed in the ablation record
were, in general, captured by SEBmr (Fig. 5a). If anything, a bias toward
under-prediction of these events was seen. This bias is likely related to an
under-prediction of QR, as the time-averaging Pscaled
underestimated the very intense rainfall rates
(> 100 mm day-1) associated with the largest ablation
events (Gillett and Cullen, 2011). The 10-day accumulation rates were captured
well, while daily totals exhibited larger scatter, reflecting the difficulty
of determining observed winter SMB from surface height records as well as the
large combined uncertainty due to Pscaled, Ta, and
Tr/s. The good agreement of modelled and observed SMB at
these short temporal resolutions suggests SEBmr is able to capture the
variations in melt and accumulation forced by the key synoptic atmospheric
controls.
SEBpr showed similar agreement to observed SMB at both daily and 10-day level
(Fig. 5c, d). The larger uncertainty in modelled ablation was expected given
the uncertainties involved in parameterizing incoming radiation fluxes and
albedo. A positive bias in modelled ablation rates was exhibited, though the
1:1 line is still well within the model uncertainty (2σ). This bias
was likely an artefact of the limited value of the cloud extinction
coefficient (k), which produced a positive bias in ensemble mean
SW↓ as compared to the reference run (not shown). However, this
bias was of less concern as the remaining analysis used the reference run and
not the ensemble mean from the Monte Carlo runs to explore cloud effects on
SMB and ΔSMB. That the temporal variability of SMB was effectively
captured by SEBpr gives us confidence that this configuration captures the
same atmospheric controls on SMB as SEBmr and as such provides a reliable and
useful tool for sensitivity analysis.
Variation of SBL climate with cloudiness
The seasonal variation of surface climate in both clear-sky and overcast
conditions during the measurement period is shown in Fig. 6a, b. Air
temperature exhibited a clear but relatively small (∼ 8 ∘C)
seasonal cycle and was only slightly lower in overcast conditions compared to
clear-sky conditions (Table 5). Vapour pressure was significantly higher in
overcast conditions, due to the combination of high relative humidity (RH) with
only slightly lower mean air temperature. Consequently in overcast conditions, mean ea
was above the saturated vapour pressure of a melting snow/ice surface
(6.11 hPa) during December to April, while in clear-sky conditions mean
ea only reached this condition during February. Average
Ts exhibited pronounced differences, being significantly higher
in overcast conditions during every month. Average wind speed (U) was
somewhat higher (0.1 to 0.7 m s-1) in overcast conditions during most
of the ablation season, while only small or non-significant differences with
cloudiness were noted in other seasons (Table 5). Thus, the main changes in
surface climate observed during cloudy periods were an increase in
ea, which, despite slightly lower Ta, were
accompanied by a large increase in Ts.
Variation of SEB and melt with cloudiness
Monthly average SEB terms diagnosed using SEBmr showed marked variation with
cloudiness and season during the measurement period (Fig. 6c, d). Clear-sky
conditions were characterized by large and opposing fluxes. SWnet dominated
the seasonal cycle, provided the largest source of energy during the summer
months, and peaked after the summer solstice in response to decreased albedo
associated with the transition from a snow to ice surface in early January.
LWnet remained a large sink throughout the year, creating strongly negative
Rnet during the winter months (JJA) that was responsible for cooling the
glacier surface. Low Ts in clear-sky conditions allowed QS to
remain directed towards the surface throughout the year. QS was of a similar
magnitude to LWnet and peaked during the winter months in response to an
increase in both U and the surface-air temperature gradient (Fig. 6a, b).
QL was much smaller in magnitude than QS and of a generally negative sign,
indicating that during clear skies, sublimation or evaporation dominated over
deposition or condensation. QR was absent and positive QC indicated that
nocturnal cooling of the surface and subsurface was occurring. QM in excess
of 20 W m-2 (equivalent to 5 mm w.e. day-1) was present for a
7-month period between October and April (inclusive). In general the seasonal
cycle of QM followed that of SWnet, but was modulated by variations in QL
and QS.
Monthly mean surface climate (a, b) and surface energy
fluxes (c, d) at AWSglacier in (a, c) clear-sky
and (b, d) overcast conditions. Partial cloud conditions are a
graduation between the two extremes and are not shown for brevity. Surface
climate variables include air and surface temperature
(Ta and Ts; ∘C), wind speed
(U; m s-1), vapour pressure (ea; hPa),
and relative humidity (RH) on a scale from 0 to 10 (i.e. % / 10).
Mean differences in surface climate between clear-sky and overcast
conditions. Positive values indicate an increase in overcast conditions.
Variable
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual
Ta (∘C)
-1.3
-0.2
-0.8
-3
-0.9
-0.5
-1.3
-1.2
-0.7
-0.1
-1.4
-1.2
-1.1
RH (%)
35
25
35
53
44
39
52
37
45
33
34
37
39
ea (hPa)
2.5
2.2
2.5
3.1
2.6
2.2
2.6
1.7
2.3
2.1
2
2.7
2.4
U (m s-1)
0.6
0.1
0.6
-0.2
-0.1
0
0
-0.2
-0.1
-0.1
0.2
0.7
0.1
TS (∘C)
0.5
0.3
0.9
1.5
4.7
5.4
6.3
5.2
5.8
2.6
1.5
0.6
2.9
p (hPa)
-7
-4
-8
-6
-10
-7
-12
-4
-9
-3
-7
-8
-7
Boldface indicates monthly differences are significant at the
95 % level using a two-sided t test assuming unequal variances.
Temperature and wind speed are normalized to 2 m values.
Average surface energy fluxes (W m-2) for melting periods in
clear-sky and overcast conditions, all melting periods, and all periods
during the measurement period. Numbers in parentheses show the proportion of
QM for each condition.
SWnet
LWnet
Rnet
QS
QL
QR
QC
QM
Melting + clear-sky periods
240 (121)
-67 (-34)
173 (87)
39 (20)
-7 (-3)
0 (0)
-6 (-3)
199
Melting + overcast periods
36 (33)
15 (14)
51 (46)
30 (27)
24 (22)
7(7)
-2 (-2)
110
Melting periods
96 (70)
-8 (-6)
88 (65)
32 (24)
15 (11)
5 (3)
-3 (-2)
136
All periods
49 (83)
-27 (-46)
22 (37)
31 (53)
2 (3)
2 (4)
2 (3)
58
Melting conditions are selected as periods where
QM > 0 in SEBmr.
In contrast, average energy terms in overcast conditions were smaller in
magnitude and directed towards the surface (Fig. 6d). SWnet was still the
largest source of energy to the surface. LWnet was positive through most of
the year, due to the enhancement of LW↓ by low cloud cover and
Ts being limited to 0 ∘C. Consequently, Rnet was
positive throughout the year and larger than in clear-sky conditions from
March to November. QS and QL were nearly equal in magnitude and both directed
towards the surface, together producing a source of energy comparable to the
contribution from Rnet. A distinct seasonal cycle in QS and QL was driven
by the strong seasonal variation in surface-air temperature and moisture
gradients in overcast conditions (Fig. 6b). QR made a small contribution to
QM during the summer and QC was negligible. The net result was that despite
the moderate magnitude of individual energy fluxes in overcast conditions,
mean QM was similar to values observed in clear-sky conditions during most
months. The exception was between February and May, where QM in overcast
conditions exceeded values in clear-sky conditions.
Fraction of time surface melting occurred in clear-sky (open
circles) and overcast (closed circles) conditions during each month. Melting
conditions are selected as periods where QM > 0 in SEBmr.
While mean QM was similar in clear-sky and overcast conditions, melting
occurred much more frequently in overcast conditions (Fig. 7). Given that day
length varies between 11.5 and 15.5 h during October to April
(inclusive) and that melt occurred during 70 to 95 % of overcast
conditions, nocturnal melt was a significant feature in overcast conditions
during these months. While clear-sky and overcast conditions accounted for 36
and 45 % of the measurement period, respectively (Conway et al., 2015),
they were responsible for 30 and 50 % of total melt, respectively, simply
because melt occurred more frequently in overcast conditions.
When all melting periods were considered together (42 % of measurement
period), SWnet made the largest contribution to QM, with QS and QL
together contributing a little over one third and QR providing a
non-negligible fraction (Table 6). On average, LWnet and QC were energy
sinks during melting periods. Considering the average SEB terms during all
periods, a shift towards QS at the expense of Rnet was observed, due
to the inclusion of non-melting clear-sky periods where negative LWnet was
largely balanced by QS.
The ΔSMB (mm w.e. yr-1) to perturbations in surface
climate and short-wave radiation terms. While the values shown are the
average change in SMB per year for both positive and negative perturbations
in each climate variable, for clarity, ΔSMB is expressed as the SMB
response to an increase in a given input variable or parameter.
Variable and perturbation
ΔSMB
Ta + 1 K
-2065
Pscaled + 20 %
+770
RH + 10 %
-380
U + 1 m s-1
-790
α + 0.1
+1220
Solar constant -6 %
-260
k + 0.17
-740
Cumulative sum of SMB terms for selected runs of SEBpr over the
2-year sensitivity period. All units are in mm w.e., except for Δ
which is in mm w.e. K-1 yr-1.
Scenario
SMB
Snowfall
Melt
Sublimation
Deposition
Refreezing
+1 K
-9181
3900
13 064
32
134
85
-1 K
-920
5670
6692
38
135
198
Δ (mm w.e. K-1 yr-1)
-2065
443
-1593
2
0
28
Mean SEB terms (W m-2) during melting periods in the +1 K
(a) and -1 K (b) perturbation runs of SEBpr. Also shown
are mean SEB terms (W m-2) in the -1 K perturbation run, for the
same periods as (a), i.e. melting periods in the +1 K
perturbation run (c), and the increases (W m-2) between each
scenario (d, e). The percentage contribution of each flux to QM, or
the increase in QM, is given in parentheses. The percentage contribution of
each flux to ΔSMB is given in the last row (f).
Scenario
SWnet
LWnet
Rnet
QS
QL
QR
QC
QM
(a) +1 K melting periods
89 (62)
-4 (-3)
85 (59)
37 (26)
19 (14)
5 (3)
-2 (-1)
144
(b) -1 K melting periods
70 (68)
-9 (-8)
61 (60)
28 (27)
11 (11)
4 (4)
-2 (-2)
103
(c) -1 K for same periods as (a)
56 (76)
-13 (-17)
43 (59)
23 (32)
7 (9)
3 (4)
-2 (-2)
74
(d) Increase from (b) to (a)
19 (46)
5 (11)
23 (57)
9 (22)
8 (20)
1 (2)
0 (0)
41
(e) Increase from (c) to (a)
33 (47)
8 (12)
41 (59)
14 (20)
13 (18)
2 (3)
0 (0)
70
(f) Contribution to ΔSMB
36 %
9 %
45 %
15 %
14 %
2 %
0 %
77 %
Sensitivity of SMB to surface climate
Model runs with SEBpr over the sensitivity period (see Sect. 2.4) highlight
the large sensitivity of SMB to Ta (Table 7). The mass-balance
sensitivity (ΔSMB) is defined as the average change in SMB per annum
for both positive and negative perturbations in each climate variable. For
clarity, ΔSMB is expressed as the SMB response to an increase in a
given input variable or parameter. The modest change in SMB to
Pscaled ± 20 % indicates that an extremely large
increase in precipitation would be needed to offset the mass loss associated
with moderate atmospheric warming. Increased relative humidity induces a small mass
loss, due to increased LW↓ and QL. Similarly, a mass loss of
790 mm w.e. yr-1 occurs for a 1 m s-1 increase in U, due to
an increased contribution of turbulent heat fluxes to melt. The ΔSMB
to terms controlling SWnet is high, with α ± 0.1 inducing over
half the SMB response of Ta ± 1 K (Table 7). Variations in
the cloud extinction coefficient (k), within the uncertainty range of the
radiation scheme optimization (Conway et al., 2015), induce large changes in
SMB, emphasizing the important contribution of SWnet to melt during
overcast conditions (Table 6). A 6 % decrease in SWTOA (the
approximate change in the solar constant during the last 10 000 years)
results in only a modest mass loss.
To examine how the large ΔSMB to Ta is expressed, a
breakdown of SMB terms was constructed for the +1 K and -1 K
perturbation runs (Table 8). A change in snowfall accounts for 21 % of
ΔSMB, while a small change in refreezing (2 %) and a dominant
change in melt (77 %) account for the remainder. Changes in deposition
and sublimation are negligible. It is worth clarifying here that changes in
snowfall resulting from the perturbations in Ta in this analysis
are due solely to changes in the fraction of precipitation falling as snow
versus rain. This is distinct from the atmospheric feedback between air
temperature and precipitation that can result in increased accumulation due
to enhanced precipitation rates in a warmer climate (e.g. Box et al., 2012).
The temperate nature of the glacier SBL in the vicinity of AWSglacier
increases the ΔSMB to Ta, as most
precipitation falls within a few degrees of the rain/snow threshold and
snowfall can occur at any time of the year (Cullen and Conway, 2015). Indeed,
despite the large ablation at AWSglacier over the 22-month
measurement period (> 9 m w.e.), a decrease in Ta
of 1.3 K would be sufficient to produce a net zero SMB.
The change in melt between Ta perturbation runs can be attributed
to SEB components whose magnitude is either directly dependent on
Ta (i.e. LW↓, QS, QL, and QR), or indirectly altered
by changes to melt and/or snowfall that alter albedo (i.e. SWnet). Table 9
shows mean SEB components for each Ta perturbation run. The most
striking feature is that while a 100 % increase in melt occurs between
-1 K and +1 K runs (Table 8), there is only a 40 % increase in QM
during melt (Table 9, a and b final column). The majority of increased melt
is due to a large increase in the fraction of time melt occurs, from 34 to
48 % of all periods. Thus, a better indication of the contribution of
each SEB term to ΔSMB can be found by examining the change in SEB
terms between runs for the melting periods in the +1 K run (Table 9e). By
multiplying the contribution of each SEB term to the increase in melt by the
fraction melt contributes to the total ΔSMB (77 %; Table 8), we
find the contribution of each SEB term to the ΔSMB (Table 9f). SWnet
makes the largest contribution to the increase in melt and accounts for over
one-third of the ΔSMB. The turbulent heat fluxes, QS and QL, together
account for less than one-third of the ΔSMB, while LWnet and QR make
smaller contributions. Thus, changes in QM that are directly dependent on
Ta contribute less than half of the ΔSMB, while changes in
snow accumulation and the albedo feedback account for the majority. Given the
covariance of cloudiness and SEB terms shown in Sect. 3.3 and the obvious
link between cloudiness and precipitation, further examination of the
interplay between cloudiness and ΔSMB is made in the following
section.
Impact of clouds on SMB sensitivity
To begin to describe the influence of cloud cover on the relationship between
SMB and Ta, the amount of melt that occurred under clear-sky,
partial cloud, and overcast conditions was calculated for each Ta
perturbation run (Fig. 8). Overcast periods exhibit the largest change in
melt between Ta perturbation runs, accounting for 50 % of the
ΔSMB to Ta. Clear-sky and partial cloud conditions show
more modest changes in melt and account for 29 and 21 % of the
ΔSMB, respectively. By calculating the mean ΔSMB in clear-sky
and overcast conditions for each month, a distinct seasonal cycle as well as
a clear dependence on cloudiness emerged (Fig. 9). In general, the
ΔSMB is greatly reduced during winter months, as Ta is
well below Tr/s and ablation is minimal at
AWSglacier. Overcast conditions almost always produce higher
ΔSMB than clear skies, especially during spring and autumn. A peak in
ΔSMB during October is associated with a higher fraction of marginal
melt conditions and average Ta around Tr/s. From
May to October (inclusive) ΔSMB in clear-sky conditions is
minimal. January and February, however, show large ΔSMB in clear-sky
conditions, as the magnitude of SWnet during these months is greatly
influenced by changes in albedo driven by the timing of the transition to an
ice surface and occurrence of summer snowfall. This albedo feedback occurs as
increased Ta decreases the fraction of precipitation falling as
snow, thus decreasing the duration of snow cover and reducing summer
snowfall.
Total surface melt in each cloud cover category for baseline and
climate perturbation scenarios.
The mean daily mass-balance sensitivity (ΔSMB) to a 1 K
change in Ta, separated into clear-sky (green) and
overcast (blue) conditions, in each month of the year. The dashed lines show
ΔSMB resulting from only a direct change in QM, which was derived from
a further model run using measured albedo and perturbing
Tr/s with Ta. The positive
values indicate mass loss for increased Ta.
In order to remove the albedo feedback, further runs of SEBpr were made for
-1 and +1 K scenarios. By using measured albedo and perturbing
Tr/s by the same magnitude as Ta, both
accumulation and SWnet remained consistent between these runs and the
resulting ΔSMB (direct) is due to only changes in QM directly caused
by increased Ta (Fig. 9, dashed lines). The divergence of full
and direct ΔSMB in clear-sky conditions confirmed that changes in
melt due to an albedo feedback dominate clear-sky ΔSMB, especially in
the summer. In overcast conditions, the direct ΔSMB is somewhat less
than the full ΔSMB in each month, as periods with altered snowfall are
removed. Still, the direct ΔSMB remained approximately twice as large
as that in clear-sky conditions through each month. Thus, it is evident that
cloudy conditions have a much stronger influence on ΔSMB to
Ta than clear-sky conditions, with an increased ΔSMB in
cloudy conditions being due to changes in both snowfall and melt, and being
strongest in the spring and autumn seasons.
Discussion
Cloud impacts on SBL and SEB
The large difference in SEB terms between clear and overcast conditions seen
in these results is driven in large part by changes in ea, rather
than changes in Ta. The increase in ea in overcast
conditions is enabled by the poor association of Ta and cloud
cover, in addition to the obvious covariance between RH and cloudiness. That
Ta is not markedly decreased in overcast conditions differs from
similar studies in the European Alps (e.g. Pellicciotti et al., 2005) and
Norway (Giesen et al., 2008), and is indicative of the maritime setting where
air mass properties, rather than a positive association between summertime
insolation and air temperature (Sicart et al., 2008), are the primary control
on SBL variations (Cullen and Conway, 2015). The availability of moist and
relatively warm air masses to the glacier surface also creates positive
LWnet in overcast conditions, which along with increases in QL, allows for
steady melt through much greater periods of time. Consequently, average daily
melt rates are similar in clear-sky and overcast conditions, again in
contrast with studies in the European Alps that show increased melt in
clear-sky conditions (Pellicciotti et al., 2005). Glaciers in Norway (Giesen
et al., 2008) show higher total melt during overcast conditions due to higher
U that increase turbulent heat fluxes during frequent cloud cover. While
increased U and turbulent heat fluxes are observed for the largest melt
events on Brewster Glacier (Gillett and Cullen, 2011), mean U was not well
differentiated by cloudiness over the measurement period, leaving
Ta and ea as the primary controls of mean QS and QL,
respectively.
While LWnet was substantially increased during overcast periods, a “radiation
paradox” (Ambach, 1974) does not occur during most of the melt season in the
ablation zone of Brewster Glacier, due to high SWTOA, large cloud
extinction coefficients, and a smaller difference in sky emissivity in
clear-sky and overcast conditions at this mid-latitude location. In contrast,
maritime sites on the melting margin of the Greenland ice sheet show that clouds
act to increase Rnet throughout the melt season at a range of elevations
(van den Broeke et al., 2008a). At the lowest site where the surface is
melting over 80 % of the summer period, the presence of a strong
`radiation paradox' implies that melt rates are higher during overcast
conditions, which is supported by the absence of increased summer melt during
more frequent clear-sky conditions (van den Broeke et al., 2011). The lack of
a “radiation paradox” during the summer months on the lower part of
Brewster Glacier emphasizes the role of air mass properties that are advected
from the surrounding ocean areas in maintaining Ta and enabling
enhanced LWnet and QL during overcast periods. In the same way, during
the transition periods, especially in the autumn, increased melt rates were
enabled by a “radiation paradox”.
Cloud impacts on SMB sensitivity
The increased sensitivity of SMB to Ta in overcast conditions may
help explain some of the high sensitivity of SMB to Ta in the
Southern Alps. Importantly, average melt is not reduced in overcast
conditions and cloud cover is frequent in the Southern Alps. Therefore, a
large fraction of melt occurs in overcast conditions which the results from
this research suggest are more sensitive to changes in Ta. In
conjunction with increased ea, clouds extend melt into periods of
marginal melt that are more sensitive to changes in Ta, as well
as being strongly associated with frequent precipitation around
Tr/s. Indeed, roughly half of the sensitivity to
Ta is due to an albedo feedback, in line with previous work in
the Southern Alps (Oerlemans, 1997), emphasizing that turbulent heat fluxes
play a secondary role, despite the assertions of recent palaeoclimatic
research (Putnam et al., 2012). In addition, the largest melt events – which
constitute a large fraction of melt over a season (Gillett and Cullen, 2011)
– are associated with overcast conditions and contribute to proportionally
larger changes in melt. Thus, air mass variability, in particular air
temperature associated with high water vapour content, appears to be the
primary control on melt during the summer ablation season.
Aside from their role in the ΔSMB to Ta, the contribution
of turbulent heat fluxes to melt may have been overstated in a number of
studies, at the expense of Rnet. In fact, the contribution of Rnet to
ablation in the present study is similar to that found over mixed snow/ice
ablation surfaces in Norway (68 %; Giesen et al., 2008) and coastal
Greenland (∼ 70 % (S6); van den Broeke et al., 2008b), and similar
to that found for a névé area in New Zealand (Kelliher et al., 1996). There
are a number of possible reasons for the deviation of the current study from
previously reported values for glacier surfaces in the Southern Alps (e.g.
Marcus et al., 1985; Hay and Fitzharris, 1988; Ishikawa et al., 1992;
Anderson et al., 2010). Firstly, in earlier studies simplifications were
usually made in the calculation of the turbulent heat fluxes, including the
assumption that the surface is always melting. Secondly, average SEB terms
were traditionally reported for the entire study period, rather than only
those during periods of melt. Table 6 clearly shows full-period average SEB
terms are biased towards QS, as non-melting nocturnal and winter periods are
included. These periods have higher values of QS, which serve to balance
negative LWnet. Lastly, a number of the studies have been conducted in low
elevation areas, where turbulent heat fluxes are increased, despite these
areas being atypical in the Southern Alps (mean elevation of glacier termini
> 1500 m a.s.l.; Hoelzle et al., 2007).
Implications for modelling glacier–climate interactions
While the present study does not make an assessment of glacier-wide
ΔSMB and therefore is somewhat limited in discussing atmospheric
controls on glacier fluctuations, it shows that the response of glacier melt
to changes in Ta can be altered by clouds. This has two important
implications for our understanding of glacier climate interactions.
Firstly, efforts to characterize glacier–climate connections need to
consider the effects of changing atmospheric moisture on melt rate as well
as accumulation. New avenues to model glacier melt with enhanced temperature
index models (TIMs) or other empirical descriptions of the temperature-dependent
fluxes (e.g. Giesen and Oerlemans, 2012) need to consider the
variance of atmospheric moisture with respect to melt. This is both due to
the strong increase in LW↓ by clouds, but also the association
with increased positive QL in moist environments. This may be important for
other maritime areas, as well as the Southern Alps where TIMs have already
been shown to break down in large melt events (Cutler and Fitzharris, 2005;
Gillett and Cullen, 2011). The use of coupled glacier-mass-balance–atmospheric models also present an avenue to represent past and future
interactions in a physically realistic way (e.g. Collier et al., 2013).
Secondly, it follows that a change in the frequency of cloud cover or
synoptic regime may enhance/dampen the SMB response to Ta. For
example, a decrease in ΔSMB from west to east across the Southern Alps
is likely, in association with the strong gradient of precipitation and
cloudiness (Uddstrom et al., 2001). It is enticing to reduce the relationship
between glacier mass balance and climate to the main causal mechanisms (i.e.
temperature/precipitation paradigm). However, there is also the possibility
that changes in atmospheric circulation coincident with changes in state
variables in the past (i.e. during the last glacial maximum; Drost et al.,
2007; Ackerley et al., 2011) may alter empirical relationships (i.e. TIMs)
informed during the present climate, altering the climate signals derived
from glacier fluctuations. For the Southern Alps, the most compelling
analysis of the controls on SMB points to changes in the regional circulation
patterns (Fitzharris et al., 2007), which are in turn associated with strong
changes in both air mass properties and cloudiness (Hay and Fitzharris, 1988).
Thus, it is likely that average relationships between melt and air
temperature may indeed be changed if a shift to drier or wetter conditions is
experienced.
The high fraction of melt due to SWnet and large contribution of an albedo
feedback to ΔSMB also implies that local or regional influences on
albedo may have an important role in modifying melt rate as seen in other
areas (Oerlemans et al., 2009). Indeed, the LGM period shows higher rates of
glacial loess deposition in New Zealand (Eden and Hammond, 2003); thus the
role of terrigenous dust in modifying glacier ablation rates during the onset
of glacier retreat (e.g. Peltier and Marshall, 1995) is a topic that should
be explored further in the context of the Southern Alps.
Conclusions
We have presented a validated time series of SEB/SMB in the ablation
zone of a glacier in the Southern Alps of New Zealand during two annual cycles.
High-quality radiation data allowed a careful evaluation of the magnitude of
SEB terms, as well as the selection of clear-sky and overcast conditions. An
analysis of SBL climate and SEB showed a fundamental change in SEB with
cloudiness that was driven by an increase in effective sky emissivity and
vapour pressure at the glacier surface. The only slightly diminished
Ta during overcast periods created positive LWnet and also
allowed both QS and QL to remain large and directed toward the surface. This
created a strong increase in the fraction of time the surface was melting in
overcast conditions, which led to a similar average melt rate in clear-sky
and overcast conditions. Given the frequent cloud cover at the site, cloudy
periods accounted for a majority of the melt observed, especially during
autumn when SWnet inputs were lower.
A parameterization of radiation components allowed the sensitivity of SMB to
independent changes in SBL climate and short-wave radiation components to be
assessed. The large sensitivity of SMB to Ta was expressed
primarily through changes in the partitioning of precipitation into snowfall
and rainfall, as well as the associated albedo feedback. The remainder of
this sensitivity was due to changes in the fraction of time the surface was
melting and changes in the magnitude of QS, QL, LWnet, and QR (in that order
of importance). We also presented a novel analysis to show that the sensitivity of
SMB to Ta diverged strongly when partitioned into clear-sky and
overcast periods. Enhanced sensitivity was found in overcast periods due to
the occurrence of precipitation and an ability for melt to be produced over
larger fractions of time. Increased sensitivity during overcast periods may
explain some of the high sensitivity of SMB in the Southern Alps, and raises
the possibility that the response of SMB to Ta in the past or
future may be altered by changing synoptic patterns that are strongly
associated with cloud cover. Thus, it highlights the need to include the
effect of atmospheric moisture (vapour, cloud, and precipitation) on both melt
and accumulation processes when modelling glacier–climate interactions.