This study examines the stable water isotope signal
(

Polar ice sheets store deposited precipitation as stratified ice layers
thousands of years back in time. This precipitation consists of stable water
isotopes (

The aim of this study is to examine how much of the

The Renland ice cap has an area of

This study utilizes three ice cores drilled on Renland in the analyses
(Table

The records from 1988 were measured with an isotope ratio mass spectroscopy
(IRMS) with a discrete resolution of

Locations of ice core drill sites (blue squares) and the instrumental temperature records (red squares).

The subset of the three ice cores used in this study: processing information, analysis and coordinates.

Firn diffusion dampens the annual oscillations in the

As this study compares the seasonally averaged

The procedure below outlines in three steps how this was done separately for the 2015, 1988 M and 1988 S cores.

It is important to ensure that the chronologies of the three ice cores are
synchronous before comparing the

In order to analyze the seasonal signals of the

The three ice cores'

Correlation coefficients (

Annually averaged

The

From this analysis, the study can comment on two things. First, the two 1988
cores have the most robust common signal of all the tested combinations. As
this was for two adjacently drilled ice cores, utilizing all three records
still results in a larger spatial atmospheric representativeness of the
region. Secondly, the high SNR and correlation coefficients imply that the
chronologies from the annual layer detection algorithm and the manual
counting are consistent. This has implications for future ice core science as
manual layer counting can be a slow and inefficient procedure. Thus, manual
counting can effectively be replaced with the StratiCounter software by

Mean signal-to-noise variance ratios calculated for the summer, winter and annually averaged data using two and three cores in the period 1801–1987.

The high combined SNR values and correlation coefficients indicate that it is
beneficial to combine the time series into a stacked

Figure

Summer (red), winter (blue) and annually averaged (green)

The relationship between Renland

Annually averaged

Correlation coefficients between the

The high correlation between

Running correlation of 50 years between the Stykkishólmur
temperature and the

The spatial extent of the correlation between the

All in all, these results support the correlations from
Sect.

Figures showing the correlation between winter

Variances of the summer

Monthly averaged

A strengthening and weakening of, respectively, the low-pressure system over
Iceland and high-pressure system over the Azores control both the direction
and strength of westerly winds and storm tracks over the North Atlantic.
Fluctuations in the difference in atmospheric pressure at sea level between
Iceland and the Azores is described by the North Atlantic Oscillation (NAO).
Changes in the NAO has previously been found to have an imprint on
precipitation in western Greenland

While the NAO is best described through a principal component analysis of
multiple sea level pressure records or gridded datasets of sea level pressure
in the North Atlantic region, this study uses an approximation where the NAO
index is based on pressure observations only near the two centers of action
of the surface pressure field (the Azores and Iberian Peninsula and Iceland).
Such an approximation was carried out by

The connection between the NAO index and seasonally (and annually) averaged

Correlation coefficients between the

In this section, it is investigated whether there is a connection between the Renland

This analysis uses a Fram Strait SIE record covering the period 1820–2000
reconstructed by

In order to examine the lacking

Annually averaged

The position of the sea ice edge or line (SIL) is another way of characterizing
the local sea ice extent. Here, we use a record of seasonal SIL anomalies in
the Greenland Sea from

This study compares the winter-averaged

Running correlations of 50 years between Stykkishólmur temperature
and the

Annual temperature anomaly plotted with respect to annual

The Arctic sea ice concentration (SIC) data (fractional ice cover in
percentage) from the ERA-Interim reanalysis

The connection between the Renland stable water isotopes and the local
climate conditions is further investigated by correlating the RECAP

Maps showing the correlation coefficients between the ERA-Interim
sea ice concentration and the RECAP

Maps showing the

The analysis showed no constant linear coupling between the Renland

Alternatively, if the seasonal distribution of precipitation on Renland
changed significantly prior to the 1910s, it could lead to a change in the
relationship between the

Moreover, while this study found that the Renland

This study found that by quantifying the mean signal-to-noise variance
ratios, a robust seasonal

These results have implications for ice core temperature reconstructions
based on the linear relationship between

The annualized

This study uses the firn diffusivity parameterization of

saturation vapor pressure over ice (Pa)

based on Eq. (

In this study, time series have often been smoothed with a 5-year moving mean
before estimating their correlation. Potentially, this results in
artificially improved correlation coefficients as a moving mean is a low-pass
filter. It is therefore necessary to quantify the significance of the linear
relationship (

Synthetic data are created by generating time series with the same power
spectrum as the

This procedure is simulated 1000 times. For each iteration, the correlation
coefficient between the synthetic

Winter-averaged

Summer-averaged

Correlation coefficients for different combinations of sea ice line anomalies (SIL) versus
Stykkishólmur temperature and

The authors declare that they have no conflict of interest.

The RECAP ice coring effort was financed by the Danish Research Council through a Sapere Aude grant, the NSF through the Division of Polar Programs, the Alfred Wegener Institute, and the European Research Council under the European Community's Seventh Framework Programme (FP7/2007–2013)/through the Ice2Ice project and the Early Human Impact project (267696). The authors acknowledge the support of the Danish National Research Foundation through the Centre for Ice and Climate at the Niels Bohr Institute (Copenhagen, Denmark). We kindly thank Dmitry Divine and one anonymous reviewer whose thoughtful comments helped improve and clarify this paper.

This research has been supported by the FP7 Ideas: European Research Council (grant no. ICE2ICE (610055)).

This paper was edited by Elizabeth Thomas and reviewed by Dmitry Divine and one anonymous referee.