This study presents simulations of Greenland surface melt for the
Eemian interglacial period (∼130000 to 115 000 years
ago) derived from regional climate simulations with a coupled surface
energy balance model. Surface melt is of high relevance due to its
potential effect on ice core observations, e.g., lowering the
preserved total air content (TAC) used to infer past surface
elevation. An investigation of surface melt is particularly
interesting for warm periods with high surface melt, such as the
Eemian interglacial period. Furthermore, Eemian ice is the deepest and
most compressed ice preserved on Greenland, resulting in our inability
to identify melt layers visually. Therefore, simulating Eemian melt
rates and associated melt layers is beneficial to improve the
reconstruction of past surface elevation. Estimated TAC, based on
simulated melt during the Eemian, could explain the lower TAC
observations. The simulations show Eemian surface melt at all deep
Greenland ice core locations and an average of up to
∼30 melt days per year at Dye-3, corresponding to
more than 600 mm water equivalent (w.e.) of annual melt. For higher
ice sheet locations, between 60 and 150 mmw.e.yr-1 on average
are simulated. At the summit of Greenland, this yields a refreezing
ratio of more than 25 % of the annual accumulation. As a
consequence, high melt rates during warm periods should be considered
when interpreting Greenland TAC fluctuations as surface elevation
changes. In addition to estimating the influence of melt on past TAC
in ice cores, the simulated surface melt could potentially be used to
identify coring locations where Greenland ice is best preserved.
Introduction
The Eemian interglacial period (∼130000 to
115 000 years ago; hereafter ∼130 to 115 ka) was the
last period with a warmer-than-present summer climate on Greenland
. Favorable orbital
parameters (higher obliquity and eccentricity compared to today)
during the early Eemian period caused a positive northern summer
insolation anomaly (and negative winter anomaly) at high latitudes,
which led to a stronger seasonality . This
stronger seasonality with relatively warm summer seasons is favorable
for high melt rates across the Greenland ice sheet.
Unfortunately, the presence of surface melt can influence our ability
to interpret ice core records. Measurements of CH4, N2O, and
total air content (TAC) can be affected if melt layers are
present. Other ice core measurements such as δ18O,
δD, and deuterium excess appear to be only marginally
affected . However, refrozen melt has the
potential to form impermeable ice layers (melt layers henceforth) that
alter the diffusion of ice core signals.
The observed TAC of ice core records is the only direct proxy for past
surface elevation of the interior of an ice sheet; i.e., the TAC is
governed by the density of air which mainly decreases with
elevation. However, TAC is also affected by low-frequency insolation
variations (changing orbital parameters) at both Antarctic and
Greenlandic sites . Furthermore,
find a TAC response on millennial timescales (during
Dansgaard–Oeschger events), which is hypothesized to be related to rapid
changes in accumulation. While TAC can be estimated for each
individual ice core without the need for other reference ice cores,
another indirect method which has been applied to infer Holocene
thinning of the Greenland ice sheet
requires several ice cores. compare the
changes of δ18O at coastal ice caps (stable surface
elevation due to confined topography) with Greenland deep ice cores
and infer elevation changes. Unfortunately, Eemian ice core records
are sparse, and therefore TAC is the only direct method available to
estimate surface elevation changes this far back in time. Since the
assumed surface elevation also influences the actual Eemian
temperature reconstructions and its uncertainty range, an accurate TAC
record is of high importance. The following example illustrates this
importance: the North Greenland
Eemian Ice Drilling project (NEEM)-derived surface temperature anomaly
at 126 ka is
7.5±1.8∘C (relative to the last 1000 years)
without accounting for elevation changes; including the elevation
change based on TAC measurements, the temperature estimate becomes
8±4∘C. This means that more than half of the
uncertainty of this temperature estimate is related to the uncertainty
of past surface elevation.
Despite the importance that melt can have for the interpretation of
TAC and other variables of ice core records, the number of studies
analyzing the frequency of melt layers in Greenland ice cores is
limited .
This study investigates regional climate simulations and observations
at seven deep Greenland ice core sites – Camp Century, Dye-3, East Greenland Ice-Core Project (EGRIP), Greenland Ice Core
Project (GRIP), Greenland Ice Sheet Project 2 (GISP2), NEEM, and North Greenland Ice Core Project (NGRIP). Additionally, an ice cap in the vicinity
of the ice sheet is examined – the Agassiz ice cap, located in the
northern Canadian Arctic. TAC is derived from regional climate and
melt simulations at these locations of interest
(Sect. ). Furthermore, the simulated local
temperature and melt are evaluated, and the impact on TAC is estimated
and compared with ice core observations
(Sects. and ). The results
indicate that Greenland ice core records from warm periods, such as
the Eemian interglacial period, might be more affected by surface melt
than previously considered (Sect. ).
MethodologyClimate and surface mass balance simulations
This study uses climate and surface mass balance (SMB) based on two
Eemian time slice simulations with a fast version of the Norwegian
Earth System Model NorESM1-F;
representing (constant) 125 and 115 ka conditions and one
pre-industrial (PI; constant 1850 forcing) control simulation. These
global simulations are dynamically downscaled over Greenland with the
regional climate model Modèle Atmosphérique
Régional (MAR v3.7; 25km×25km), which was extensively
validated over Greenland under present-day climate conditions
.
MAR employs a land surface model (SISVAT; Soil Ice Snow Vegetation
Atmosphere Transfer) with a detailed snow energy balance
fully coupled to the model
atmosphere. MAR's atmosphere uses the solar radiation scheme of
and accounts for the atmospheric
hydrological cycle (including cloud microphysics) based on
and . The
snow–ice component of MAR is derived from the Crocus snowpack model
, simulating mass and energy fluxes between
snow layers and reproducing snow grain properties as well as their
effect on surface albedo. The MAR model has 24 atmospheric layers (up
to 16 km above ground) and SISVAT 30 snowpack layers.
The NorESM-F experiments are spun up for 1000 years with constant 1850
forcing (greenhouse gas (GHG) concentrations and orbital parameters)
to a quasi-equilibrium state. The PI simulation is run for another
1000 years with constant forcing. The two Eemian time slice
simulations are branched off from the initial 1000-year spin-up and
run for another 1000 years each with constant 125 and 115 ka
forcing, respectively (changed GHG concentrations and orbital
parameters compared to PI). For the MAR experiments, NorESM is run for
another 30 years for each of the three experiments and the output is
saved on a 6-hourly basis. These 30 years are used as boundary forcing for
MAR. After disregarding the first 4 years as spin-up, the final
26 years are used for the analysis (thin lines in
Figs. –,
–). All
climate simulations use a fixed, modern ice sheet geometry, in the
absence of a reliable Eemian ice sheet estimate and high computational
costs of a coupling with an ice flow model
e.g.,.
The MAR SMB is analyzed in a study investigating the influence of
climate model resolution and SMB model selection on Eemian SMB
simulations , which amongst other things
shows the high importance of solar insolation in Eemian
simulations. Additionally, while providing the most complete
representation of physical surface processes in the pool of
investigated models, MAR shows a less negative SMB than an
intermediate complexity model during the warmest Eemian simulations
(mainly due to a higher ratio of refreezing).
Furthermore, the discussed SMB is used in a study investigating the
Eemian Greenland ice sheet volume with a higher-order ice sheet model
.
showed that different external SMB forcings show a larger influence on
the Eemian ice volume minimum than sensitivity experiments performed
with internal ice dynamics (like changed basal friction). The ice
sheet simulations with the MAR SMB show a moderately smaller Eemian
ice sheet with the difference equivalent to ∼0.5m of
sea level rise (with respect to the modern ice sheet).
In this study, the MAR SMB simulations are analyzed at seven deep
Greenland ice core locations – Camp Century, Dye-3, EGRIP, GRIP,
GISP2, NEEM, NGRIP – and an adjacent ice cap – the Agassiz ice cap
(Fig. ). Due to model topography
misrepresentation at the ice sheet margins, i.e., the model topography
is lower than in reality at the Agassiz ice cap location (model
resolution 25 km), a substitute location (Agassiz_sub) in the
vicinity of the ice cap, with a model elevation similar to the
observed elevations, is chosen (Table ).
Overview map of Greenland ice core locations considered in
this study. The gridded data show the simulated annual melt rate
under 125 ka conditions. Note that Agassiz_sub refers to a
substitute location necessary due to the model topography
misrepresentation (see Sect. ).
Agassiz_sub refers to a substitute location used due to model topography misrepresentation. For details, see Sect. .
Observed surface melt
The PI climate and SMB simulations are compared to present-day
satellite and temperature observations at the locations of
interest. The two observational melt day datasets are both derived
from satellite-borne passive microwave radiometers – Scanning
Multichannel Microwave Radiometer (SMMR), the Special Sensor
Microwave/Imager (SSM/I), and the Special Sensor Microwave
Imager/Sounder (SSMIS). The first dataset, MEaSUREs
(Greenland Surface Melt Daily 25 km EASE-Grid 2.0, version 1),
covers the years 1979 to 2012 and is available for the entire Northern Hemisphere. The melt onset is identified by comparing 37 GHz,
horizontally polarized (37 GHz H-Pol) brightness temperatures with
dynamic thresholds associated with a melting snowpack
. Unfortunately, the Agassiz ice cap is not
covered by this dataset. The second dataset, T19Hmelt,
covers the whole MAR grid at 25 km from May to September for most
years between 1979 and 2010. It uses data collected at K-band
horizontal polarization (T19H) with a constant brightness temperature
threshold of 227.5 K. Both satellite
datasets are discussed to show their different sensitivities and to
illustrate the uncertainty of these satellite-based melt observations.
The seasonal temperature observations at weather stations and 10 m
borehole temperatures (representing annual mean temperatures from 1890
to 2014) are taken from a collection of shallow ice core records and
weather station data . Finally, the bore
hole temperatures from the Agassiz ice cap are taken from
.
Observed total air content
Firstly, the Dye-3 TAC for the ice core depth range of
∼240 to 1920 m was extracted from Fig. 4
therein. Since
indicate that ice from warmer periods (higher δO18
values), likely Eemian, is located below 2000 m at Dye-3, the
presented Dye-3 TAC record does not represent Eemian
conditions. Secondly, the GRIP TAC dataset
covers depths from ∼120 to 2300 and ∼2780 to 2909 m, while an age mode is only provided for the upper part (oldest ice at 41 ka). For the deeper sections of the core, a published unfolding of the GRIP core age bands in Fig. 3 therein is used to assign an age to
the observations. Thirdly, the GISP2 TAC data were extracted from a
Supplement table of and cover the
period from 127.6 to 115.4 ka. Fourthly, the NEEM TAC observations
cover the deepest section of the NEEM ice
core from ∼2200 to 2500 m depth (corresponding to
an age of ∼75 to 128 ka; not continuous) and an
example for Holocene conditions from depths between
∼100 and 1400 m (no age provided). Finally, the
NGRIP TAC record includes the entire core
from ∼130 to 3080 m; however, the sampling
resolution varies. An age model is provided for the entire dataset
with a maximum age of ∼120ka. Note that only the
Eemian sections for GRIP, GISP2, NEEM, and NGRIP are shown in
Fig. .
Calculation of the model-derived total air content
The model-derived TAC is calculated with the annual mean surface
pressure and the annual mean near-surface temperature from the MAR
regional climate simulations at every location of interest
:
TAC=VcPcTcT0P0,
where Vc is the pore volume at close-off in
cm3g-1 of ice, Pc
the mean atmospheric pressure at the elevation of the close-off depth
interval in mbar, Tc the firn temperature prevailing at the same
depth interval in K, P0 the standard pressure (1013 mbar), and
T0 the standard temperature (273 K). Vc is calculated as a
function of Tc following an empirical relation
:
Vc=(6.95×10-4Tc)-0.043.
This theoretical TAC is then reduced (TACred) depending on the
percentage of refreezing of the annual accumulation (RZper):
TACred=TAC×1-RZper100+TACrefrozen×RZper100,
where TACrefrozen is calculated using Henry's solubility law
for N2 and O2 (neglecting other
atmospheric gases) to account for air that is dissolved in the
meltwater before refreezing:
TACrefrozen=Ca,N2+Ca,O2,
with Ca,N2 and Ca,O2 being the aqueous-phase concentration
of N2 and O2, respectively:
Ca,N2=Pc⋅Catm,N2⋅Hcp,N2
and
Ca,O2=Pc⋅Catm,O2⋅Hcp,O2,
where Catm,N2 and Catm,O2 are the atmospheric concentration
ratios (0.79 and 0.21), and Hcp,N2 and Hcp,O2 are
Henry's solubility constants (10.49×10-6 and
2.1982×10-5) for N2 and O2,
respectively. Henry's law assumes that the meltwater is in
equilibrium with the ambient air at a temperature of 273 K and at the
local atmospheric pressure (Eqs. and ). No
air is occluded in the form of bubbles in the freezing process.
ResultsTemperatures
The simulated PI annual mean (near-surface) temperatures (1850 climate
forcing) at the eight locations of interest
(Fig. ; black columns; short bold
lines – ensemble means; short thin lines – individual model years)
generally fit well with observations from weather stations
(Fig. ; long bold lines in black; standard
deviation in gray shading). However, the annual means inferred from
10 m borehole temperatures (Fig. ; long
bold lines in gray; average of the years 1980 to 2014) are
consistently colder than the simulated PI means. The lower borehole
temperatures represent snow temperatures which are typically cooler
than the ambient air temperatures. Only at the Agassiz site, the
borehole temperatures are higher. This exception is likely related to
the usage of a substitute location (see Sect. ).
Annual mean (near-surface) temperature at Greenland ice
core locations simulated by the MAR climate model for three time
slices. Individual model years (short thin lines) and their mean
(short bold lines, numerical values on top of columns) are
compared to mean observations from weather stations (long bold
lines in black), their corresponding standard deviation (gray
shading), and 10 m borehole temperatures (annual mean; long bold
lines in gray).
The annual mean temperatures at most locations only vary by
0.5 ∘C between the time slice simulations, i.e., no large
difference between PI (Fig. ; black) and
warmest Eemian simulations (Fig. ;
orange). This is to be excepted since the annually integrated solar
irradiance is similar in all time slices.
However, the varying Eemian seasonality
results in consistently ∼3–4 ∘C (with
respect to PI; black) warmer summer (JJA;
June–July–August) temperatures at all locations for mid-Eemian
conditions (125 ka: orange) and cooler temperatures for late Eemian
conditions (115 ka: blue). The simulated PI summer
temperatures (Fig. ; black columns;
short bold lines – ensemble means; short thin lines – individual
model years) show good agreement with observations from weather
stations (Fig. , long bold lines in
black).
Mean (near-surface) JJA (June–July–August) temperature at
Greenland ice core locations simulated by the MAR climate model
for three time slices. Individual model years (short thin lines)
and the mean (short bold lines, numerical value on top of columns)
are compared to mean observations from weather stations (long bold
lines in black).
The precipitation-weighted temperatures
(Fig. ) show a similar pattern to the JJA
temperatures (Fig. ). This is
understandable since most precipitation in Greenland falls around the
summer months and these temperatures are calculated by multiplying
daily temperatures with daily precipitation, summing up the results
over the year and then dividing by the sum of the annual
precipitation; i.e., precipitation is used as a weight, instead of
time, in annual mean temperatures. Precipitation-weighted temperatures
are arguably closer to what is recorded in an ice core (temperature at
the time of deposition) and these temperatures show a less pronounced
warming for mid-Eemian conditions (125 ka: orange), i.e., maximum
3 ∘C warmer compared to PI (black).
Number of melt days
Passive microwave satellite data show a strong difference in observed
melt days per year (presence of surface water)
(Fig. ; first three columns from the
left; brown and green) between central ice core locations (GRIP,
GISP2, NGRIP, NEEM, EGRIP), where surface melt is sparse, and
locations closer to the margins (Camp Century, Dye-3) and ice caps
(Agassiz), where melt is much more frequent. Central locations show
between 0 and ∼1 melt days per year in the last
∼30 years for which satellite data are available. The
exact values vary depending on the location, satellite dataset, and
whether the extreme melt event of 2012 is included.
Annual melt days at Greenland ice core locations derived from satellite data and simulated by the MAR climate model. Observations in the first three columns from the left are compared with simulations in the fourth and fifth column. Columns from the left: (1) passive microwave data from MEaSUREs (1979 to 2012); (2) the same data as in (1) but with a different processing T19Hmelt; (1979 to 2010); (3) the MEaSUREs dataset excluding the extreme melt year 2012 (1979 to 2010); (4) simulated melt for pre-industrial (PI); and (5) 125 ka conditions. Individual model years (thin lines) and the ensemble means (bold lines, numerical values on top of columns) are shown. For Agassiz, simulation results for the substitute location are shown, as discussed in Sect. .
The simulated PI melt day frequency
(Fig. , black columns) shows good
agreement with the observations (Fig. ;
brown and green columns), i.e., low melt frequencies at the central
locations and higher melt frequencies at locations at the
margins. However, the simulated PI melt frequencies are generally
lower than present-day observations (especially at the Agassiz
location), with the exception of Dye-3, which shows a higher simulated
melt frequency.
Melt and refreezing
The 125 ka simulations (Fig. ; orange
columns) show a significantly higher melt frequency at all locations
(more than 30 melt days per year at Dye-3) compared to the
PI simulations (Fig. ;
black columns) and observations (Fig. ;
brown/green columns). The SMB simulations show surface melt at all ice
core locations during the warm mid-Eemian with an annual meltwater
production (Fig. ) for warmer locations of ∼300mmw.e.yr-1 (Camp Century) and
∼600mmw.e.yr-1 (Dye-3). However, even
modern dry, high-altitude locations show an annual surface melt of
∼60 (GRIP, GISP2), 80 (NGRIP), and up to
120 mmw.e.yr-1 (EGRIP). NEEM shows
∼150mmw.e.yr-1 for the warmest Eemian
simulations.
Annual refreezing percentage (of accumulation) at Greenland ice core locations simulated by the MAR climate model for three time slices. Individual model year percentages (thin lines) and the simulation ensemble mean percentages (bold lines, numerical values on top of columns) are shown.
Calculated TAC at Greenland ice core
locations derived from simulations with the MAR climate model for
three time slices (see method in
Sect. ). Individual model years (thin lines) and
the simulation ensemble means (bold lines, numerical values on top
of columns) are compared to observed late Holocene and Eemian
ranges (horizontal gray and orange shading, respectively; 2 standard deviations). Dashed lines illustrate the model-derived
TAC before reducing it by the refreezing percentage (not
distinguishable for the respective time slices; see
Sect. ). Note that the Holocene range at NGRIP is very
narrow and almost completely overlaps with the Eemian range, and
there is no Holocene range for GISP2 and no Eemian range for
Dye-3.
The mean simulated amount of refreezing exceeds 40 % of the annual
accumulation at most ice core locations under warm mid-Eemian
conditions (Fig. ; thick orange
lines). Even at the highest locations, GRIP and GISP2 at
∼3200m elevation, refreezing surpasses 25 % of the
annual accumulation under 125 ka conditions. The largest amount of
refreezing is simulated at Agassiz_sub, EGRIP, and Dye-3, where
refreezing percentages reach 80 % to 90 %.
Observed TAC from five Greenland ice cores – NEEM, GRIP,
GISP2, NGRIP, and Dye-3. Observations (circles) are compared with mean
simulated TAC for 115 ka (blue lines) and 125 ka simulations
(orange lines). Furthermore, data points used to calculate the
Eemian range in Fig. (orange circles) and
the model-derived TAC before reducing it by the refreezing
percentage (dashed lines; see Sect. ) are
shown. Note that NEEM, GRIP, and GISP2 are shown against age (robust
age models), while NGRIP and Dye-3 are shown against ice core
depth. The NEEM melt zone is
highlighted with a gray shading. The y axes are reversed.
Total air content
Theoretical TAC derived from simulated surface pressure and annual
mean temperature and reduced according to the
amount of simulated refreezing (Fig. and
Sect. ) shows significantly lower values for the
125 ka simulations. Most of the higher ice core locations (GRIP,
GISP2, NGRIP, NEEM, EGRIP, and Camp Century) show simulated TAC values
between 45 and 70 mLkg-1 on average, whereas the respective PI
values are between 90 and 100 mLkg-1. At Dye-3, the simulated
TAC is about 25 mLkg-1 on average for the warm 125 ka Eemian
simulations compared to 75 mLkg-1 during PI. Observed Holocene
TAC from ice core records (Fig. ; horizontal
gray shading) fits well with the PI simulations, while observed Eemian
TAC (Fig. ; horizontal orange shading) is not as
low as the simulated values.
The Eemian ranges in Fig. are calculated as the
average (± 2 standard deviations) of the lowest 10 % of
observed Eemian TAC (Fig. ; used observations
are indicated in orange) for NEEM and NGRIP. Due to the low number of
Eemian observations at GRIP and GISP2, a different threshold of 20 %
is used for this core. For the calculation of the late Holocene ranges
in Fig. , observations younger than 1000, 2000,
and 4000 years are used for GRIP, Dye-3, and NGRIP, respectively. The
late Holocene range for NEEM is calculated from the entire Holocene
example provided in the data (nine data
points; no age provided).
Finally, TAC observations from the deeper ice core sections (i.e.,
possibly Eemian; Fig. ; NEEM, GRIP, GISP2,
NGRIP; circles; inverted y axes) are compared with mean simulated TAC
for 115 ka (Fig. ; blue line) and 125 ka
conditions (Fig. ; orange line). For Dye-3,
the entire TAC record is shown due to the lack of Eemian
observations. However, the ice at the bottom of Dye-3 has been shown
to contain pre-Eemian ice . Note that
NEEM and GRIP are shown against age based on a more robust chronology
involving “unfolding the ice” , while NGRIP and Dye-3 are shown against
core depth.
The 115 ka simulations generally fit well with the late Eemian (NEEM,
GRIP, GISP2, NGRIP) and Holocene (Dye-3) observations, while the
125 ka simulations are lower than the observations. For NEEM, the
lowest TAC observations are within the ice core section influenced by
melt gray shading in
Fig. ;.
Discussion
The enhanced Eemian seasonality and warmer
Eemian summers
are indicators of
elevated melt during this period. The recent extreme melt event in
Greenland in 2012 and a similar event in 1889
demonstrate that surface melt on the
entire Greenland ice sheet, even at the summit of Greenland, is
possible under recent climate conditions. Even though these extreme
Greenland-wide melt events were caused by a rare large-scale
atmospheric pattern and were further
enhanced by an externally caused albedo lowering ash deposition
from forest fires;, it is likely that such
events are more frequent in a warmer climate such as the Eemian
interglacial period.
The simulations discussed in this study (regional climate plus a full
surface energy balance) indicate surface melt and refreezing
(Figs. and )
at all deep Greenland ice core locations. Even central Greenland
locations close to summit (GRIP, GISP2) show a melt of
∼60mmyr-1
(Fig. ). Due to this high surface melt,
TAC measurements derived from these simulations are between ∼25%
(GRIP, GISP2) and ∼80% (Dye-3, EGRIP) lower than
modern (PI) values (Fig. ). Even though the
presented climate simulations show such extensive melt, there are
several reasons why these simulations can be interpreted as
conservative estimates: (1) the simulated PI melt frequency is mostly
lower than satellite observations
(Fig. ; black vs. brown/green
columns). However, the observation of higher melt frequencies can
likely also be related to the effects of recent global warming which
are not represented in the PI climate simulations. (2) Processes like
ash deposition which were partly responsible for the extreme Greenland
melt events of 2012 and 1889 are not
simulated. (3) The climate simulations use a fixed, modern ice sheet
geometry and including the neglected lowering and retreat of the
Eemian ice sheet would likely increase the simulate warming in many
regions.
Many studies suggest a substantial Eemian ice volume reduction
e.g., particularly in the
marginal regions – an overview of previous Eemian studies can be
found in . The use of a fixed ice sheet
undoubtedly adds additional uncertainties to the presented melt
simulations – e.g., neglecting modifications of local wind patterns
and surface albedo as regions become deglaciated impacting local
near-surface temperature , local
orographic precipitation following the slopes of the ice sheet
, or increased katabatic winds caused by
steeper ice sheet slopes . However, these uncertainties are much
stronger in marginal than in high-altitude regions where the ice
elevation changes were more limited. After all, a future, more
exhaustive evaluation of Eemian melt at the ice cores sites should
investigate different possible ice sheet geometries.
Furthermore, the absence of a simulated annual warming, and proxy data
showing Eemian peak temperatures as high as
+7.5±1.8∘Cwithout altitude
corrections and
+8.5±2.5∘C for NEEM
(the North Greenland Eemian Ice Drilling project in northwest
Greenland) and +5.2±2.3∘Clower
bound as the record only starts after the peak Eemian
warming for NGRIP (North Greenland Ice Core
Project) indicate that the climate simulations might include a cold
bias. The simulated JJA temperatures
(Fig. ) and the simulated
precipitation-weighted temperatures
(Fig. ) show a peak warming of only
∼3–4 and ∼3∘C, respectively. However, the fact
that infer an elevation (at the
deposition site) of several hundred meters higher than at NEEM today
complicates the interpretation of how well the simulated temperatures
fit the proxy-derived observations.
Focusing again on the comparison of melt observations and simulations
(Fig. ), a strong underestimation of
melt at the Agassiz site in the PI simulations becomes apparent. This
strong underestimation is likely related to the use of a substitute
location (geographically shifted, with similar model and observed
elevation) necessary due to low model topography at the original core
site causing unrealistically high melt simulations. Furthermore, the
Agassiz site is only covered by the satellite dataset which appears
to be less sensitive to melt (T19Hmelt with less melt than
MEaSUREs at all sites) and although Eemian ice is absent at the
Agassiz site, the simulated Eemian refreezing percentage
(Fig. ) of approximately 80 % is
consistent with the Agassiz melt record, which indicates a complete
melt of the annual accumulation during the Holocene optimum
∼10–11 ka.
Another important aspect for the melt interpretation is the formation
of melt layers and the amount of meltwater needed to form a (visible)
melt layer. While the presented TAC calculations assume Henry's
solubility law for the air content of
the melt layer, the formation of a melt layer in an ice core is a
complicated process, e.g., depending on prevailing snow properties. A
higher number of melt layers is not just the result of uniformly
higher summer temperatures but a combination of an increased contrast
between the pre-melt snow pack temperatures (strongly influenced by
winter temperature) and the summer melt rate (a function of summer
temperature) . Therefore, the enhanced
Eemian seasonality might have been favorable for the formation of
melt layers.
The simulated 125 ka TAC values are consistently lower than the
observations (Figs. and
). However, at NEEM – the ice core with the
most complete Eemian record (likely including peak warming) – the
simulated 125 ka TAC seems to be most similar to the lowest
observations, indicating that the high amount of simulated melt could
explain these observations. The variability of the observed NEEM TAC
in the suggested melt zone between 127 and 118.3 kagray
shading; is large, likely due to the
varying influence of melt layers.
The Eemian TAC measurements at GRIP, GISP2, and NGRIP also show
reduced values (not as low as at NEEM), which can be interpreted in a
similar way as at NEEM – GRIP, GISP2, and NGRIP might have been
influenced by Eemian melt as well. The simulated 125 ka TAC measurements for all
three locations are strongly reduced (relative to PI levels) but do
not reach levels as low as at NEEM. However, these reduced TAC levels
could indicate significant surface melt.
Overall, the lack of a better agreement between observed and simulated
Eemian TAC (i.e., few TAC observations as low as the simulations)
could be related to the sparse number of Eemian peak warming
observations (most ice core records only start after the peak warming;
particularly at GRIP, GISP2, NGRIP, and Dye-3). However, another
possible explanation could be a shift of the precipitation rates in
central Greenland towards much higher values during the Eemian
interglacial period. Unfortunately, accumulation rates are
unconstrained for the Eemian sections of Greenland ice cores.
Furthermore, another uncertainty for the interpretation of the
simulations is the effect of the higher Eemian summer insolation on
the TAC. An anti-correlation between local summer insolation and TAC
is known in ice core records from East Antarctica during the last
400 000 years , and the insolation signal
is also found in Greenlandic TAC
NGRIP;.
estimates (based on data from the Holocene optimum) that the summer
insolation could account for 50 % of the observed Eemian TAC changes
at NEEM.
Nevertheless, the possibility of a melt-induced reduction of TAC
should be considered for the interpretation of Eemian air content to
estimate ice surface elevation changes. An early interpretation of the
first Greenland ice cores (Camp Century, Dye-3) suggested an extreme
scenario for Eemian Greenland with extensive melt and a much smaller
ice sheet, leading to a sea level rise of 6 m. However, this scenario was rejected by later
ice core studies showing evidence of Eemian ice especially
NGRIP and NEEM;. Furthermore, GRIP TAC measurements
have been interpreted as evidence for the
elevation of the summit sites having remained above 3000 m of
altitude during the Eemian and GRIP deuterium excess measurements
remain in the normal range during the Eemian
. However, this last interpretation can
be challenged by measurements of a NEEM Holocene melt layer,
suggesting that the melt layer mainly influences TAC and CH4
observations, while other variables like deuterium excess may be less
influenced by melt .
The climate simulations show surface melt at all deep ice core
locations and at the Agassiz ice cap under 125 ka climate conditions
(Figs. and ;
orange column). Even locations near the summit of Greenland (GRIP,
GISP2, and NGRIP) show a few melt days per year on average (defined
as >8mmd-1) during these warm Eemian simulations. NEEM, the
ice core location with the longest Eemian record, shows
∼8 melt days per year. While the presence of
Eemian surface melt at NEEM was acknowledged previously
, the lower TAC observations at GRIP,
GISP2, and NGRIP could as well be related to Eemian surface melt,
rather than stable or higher elevations.
Finally, it should be emphasized that a robust estimate of Eemian
Greenland surface melt is challenging to obtain with a single climate
model. Ideally, there should be an ensemble of climate models to
explore model biases and uncertainties. However, as pointed out
earlier in this discussion, there are several reasons why the
presented climate simulations could be on the lower end of available
climate model in terms of the amount of simulated Eemian melt. It is
likely that there are other climate models which show more extensive
Eemian surface melt.
In the future, an analysis of individual or ensemble Eemian climate
simulations would benefit from a comparison of the observed extreme
melt event in 2012 (and similar events in the recent past) with
simulated extreme Eemian melt events. Relationships in the Eemian simulations between air temperature and local wind patterns and the
resulting simulated melt could be analyzed and used to identify specific weather patterns leading to high surface melt in the simulations (e.g., similar analysis performed by ).
Conclusions
Using regional climate simulations (including a full surface energy
balance), this study shows surface melt at all Greenland ice core
locations during the Eemian interglacial period (e.g., GRIP, GISP2:
∼60mmw.e.yr-1; NGRIP:
∼150mmw.e.yr-1). The amount of refreezing
exceeds 25 % of the annual accumulation at the summit of Greenland
(GRIP, GISP2) and reaches values as high as 90 % at less central
locations like Dye-3 and EGRIP. The simulated air pressure,
temperature, and refreezing are used to estimate Eemian TAC and high melt rates could explain the low corresponding
ice core TAC observations. This is true even though the discussed
simulations could show conservative melt estimates (several
potentially melt-increasing processes are neglected). Therefore, the
possibility of widespread surface melt should be considered for the
interpretation of Greenlandic total air content records (as an
elevation proxy) from warm periods such as the Eemian interglacial
period. Finally, a robust map of Eemian melt estimates in Greenland in
combination with accumulation patterns could be used to identify
potential future ice cores sites on Greenland. Such a procedure would
increase the chances of finding Eemian ice influenced by a minimum
number of melt layers. These sites will have relatively high
accumulation combined with low surface melt.
Additional figures
Annual mean precipitation-weighted temperature at Greenland ice core locations simulated by the MAR climate model for three time slices. Individual model years (thin lines) and the mean (bold lines, numerical values on top of columns) are shown.
Annual melt at Greenland ice core locations simulated by the MAR climate model for three time slices. Individual model years (thin lines) and the mean (bold lines, numerical values on top of columns) are shown.
Annual SMB at Greenland ice core locations simulated by the MAR climate model for three time slices. Individual model years (thin lines) and the mean (bold lines, numerical values on top of columns) are shown.
Code availability
The source code of MAR, version 3.7, is available on the MAR website: https://mar.cnrs.fr (last access: 20 January 2021, ). An description on how to retrieve the source code is given in the download section of the MAR website: https://mar.cnrs.fr/index.php?option_smdi=presentation&idm=10, last access: 20 January 2021.
Data availability
The Eemian MAR simulations are available from the corresponding author upon request. MEaSUREs Greenland Surface Melt Daily 25 km EASE-Grid 2.0, version 1 is freely
available at 10.5067/MEASURES/CRYOSPHERE/nsidc-0533.001. For more information and to request the
T19Hmelt data (, 10.5194/tc-5-359-2011), please contact Xavier Fettweis (xavier.fettweis@uliege.be). For more information and to request the collection of Greenland shallow ice core and weather station data , please contact Anne-Katrine Faber (anne-katrine.faber@uib.no). The TAC observations at NEEM are freely available at 10.1038/nature11789 . The GRIP TAC is freely available at 10.1594/PANGAEA.55086. The GISP2 TAC is freely available as a Supplement to (10.1073/pnas.1524766113). The NGRIP TAC is freely available at https://www.ncdc.noaa.gov/paleo/study/20569 (last access:
27 November 2020, , 10.5194/cp-12-1979-2016). The Dye-3 TAC data was extracted from Fig. 4 in Herron and Langway (1987). For more information and to request the extracted data please contact Sindhu Vudayagiri (sindhu.v@nbi.ku.dk) or Thomas Blunier (blunier@nbi.ku.dk).
Author contributions
AP and BMV designed the study with contributions from KHN. Dye-3 total air content data were extracted from Fig. 4 in by SV. AP made the figures and wrote the text with input from BMV, KHN, SV, and TB.
Competing interests
The authors declare that they have no conflict
of interest.
Acknowledgements
We thank Chuncheng Guo for performing the Eemian NorESM simulations and Sébastien Le clec'h for downscaling the NorESM simulations with the regional model MAR. Furthermore, we would like to thank Anne-Katrine Faber for valuable discussions and providing the shallow ice core data she
compiled during her PhD. We also thank Xavier Fettweis for providing the T19Hmelt data. Furthermore, we very much thank the editor Eric Wolff and two anonymous referees for their comments and suggestions, which significantly improved the manuscript.
Financial support
This research has been supported by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013) (ICE2ICE (grant no. 610055)).
Review statement
This paper was edited by Eric Wolff and reviewed by two anonymous referees.
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