We use isotopic composition (
The Princess Elizabeth Land sector of East Antarctica. Blue
iso-contours display the spatial pattern of surface snow
While understanding the behaviour of the Antarctic climate system is crucial in the context of present-day global environmental changes, key gaps arise from limited observations. Prior to the International Geophysical Year (1955–1957), the primary sources of climatic data were ice core records. Deep ice cores have provided a wealth of climatic and environmental information covering glacial–interglacial variations of the past 800 000 years (EPICA, 2004). However, the spatio-temporal characteristics of Antarctic climate variability in the most recent centuries remains poorly known and understood (Jones et al., 2016; PAGES 2k Consortium, 2013).
The network of ice core records spanning the last centuries is distributed highly unevenly. Quite extensive coverage of some regions of Antarctica, such as West Antarctica (Kaspari et al., 2004) or Dronning Maud Land (Altnau et al., 2015; Oerter et al., 2000) contrasts with other regions that remain poorly studied. As a result, attempts to reconstruct the climatic variability of the whole Antarctic continent (Jones et al., 2016; PAGES 2k Consortium, 2013; Schneider et al., 2006; Frezzotti et al., 2013) are limited by the lack of available data.
In our previous work we summarized available isotopic data for the vicinity of Vostok Station in order to construct a robust stack climatic record over the past 350 years (Ekaykin et al., 2014). Here we present a new stacked climate record for Princess Elizabeth Land (PEL), the territory located between the Russian stations of Progress, Vostok and Mirny, East Antarctica. This record is based on water stable isotope data from six sites and spans the last 350 years (Fig. 1). We note an imperfect correlation between the stacked isotopic record and regional surface air temperature variations, underlying the fact that the isotopic content of precipitation is not simply a proxy of temperature but rather a parameter that covaries with the local climate in a manner similar to temperature (Steig et al., 2013).
We also highlight significant relationships between regional climate and large-scale modes of variability of the Southern Hemisphere.
Section 2 describes our data and methods. Section 3 is focused on the results and their discussion before we conclude in Sect. 4.
In this study we use data from six individual records obtained in Princess Elizabeth Land (Fig. 1, Table 1).
Information on sites where individual time series were obtained.
n/a
The designation “105 km” (67.433
The designation “400 km” (69.95
The designation “VRS 2013” (78.467
The designation “NVFL-1” (77.11
The designation “NVFL-3” (76.405
The designation “PV-10” (72.805
We estimated the dating uncertainty by comparing age calculated using only
firn density data and average snow accumulation rate for a given site with
the age of the reference age markers and came to the conclusion that the age
errors do not exceed 10 %. For the reference years (1816 and 1993, where
we have absolute dating), the error approaches 0. The largest error is
expected for the 400 km series, where we do not have reference age
markers. However, if we use the prominent 1840 cold event (see Sect. 3.3)
observed in all records as a marker, then we may estimate a relative dating
error for this series as
We also use the accumulation data from the site “200 km” (Fig. 1), spanning the period 1640–1987, as published in Ekaykin et al. (2000). The accumulation values from sites “150 km” and 400 km are corrected both for layer thinning with depth and for the advection of ice from upstream of the glacier to account for the spatial gradient of the snow accumulation rate.
Figure 2 displays the individual
We then apply a rectangular-shaped low-pass filter to cut off the variability
with periodicities shorter than 27 years (i.e. frequencies
The normalized and filtered time series are displayed in Fig. 3. Despite some common features, this comparison shows significant discrepancies between individual records. One reason for the mismatches may lie in age scale uncertainties. However, this hypothesis is ruled out by the comparison of individual series around 1816 and 1993 (the dates of firn layers containing Tambora and Pinatubo volcanic eruption debris, denoted by vertical dashed lines in Fig. 3), when the relative dating error tends to 0: observed discrepancies do not arise from chronological uncertainties alone. Alternatively, this mismatch may arise from a significant level of noise even in the filtered series and factors other than the local temperature that control the isotopic composition of precipitation.
Normalized and low-pass-filtered individual records (with a cut-off for variations on timescales shorter than 27 years), displayed using the same colors as in Fig. 2. The thick grey line is the stacked record (PEL2016). The dashed grey lines show the less robust marginal parts of the stack. Vertical dashed lines mark reference horizons that contain the debris of Tambora (1815) and Pinatubo (1991) volcanic eruptions, respectively deposited until 1816 and 1993 in Antarctica.
In order to isolate the climatic signal from the noise, we constructed a stacked climatic record for the PEL region, hereafter named PEL2016 (grey line in Fig. 3). For a given year, the value of this record consists of the average of the values of individual records available for this year.
A number of research stations have been established in the PEL area, as
indicated in Fig. 1. Unfortunately, most of them have very short (if any)
meteorological records. Relatively long records are available only for five
stations: the Australian station Davis (1957–1964 and 1969–2015), the
Chinese station Zhongshan (1989–2015), and the Russian stations Progress
(1989, 1991 and 2003–2015), Mirny (1956–2015) and Vostok (1958–2015 with
gaps in 1962, 1994, 1996 and 2003). The monthly data were downloaded from
The correlation between Progress, Zhongshan and Davis annual mean
temperature datasets, located very close to each other, is 0.96–0.98 (note
that only statistically significant correlation coefficients with a
confidence level
We also use data from the automatic weather station (AWS) LGB59 located at the slope of the Antarctic ice sheet inland from Progress Station (Fig. 1), available for the period 1994–1999, as well as surface air temperature data from Casey and Mawson.
In order to investigate possible relationships between PEL climate multi-decadal variations and large-scale modes of variability, we use data on the indices of the Antarctic Oscillation (AAO), the Interdecadal Pacific Oscillation (IPO) and the Indian Ocean Dipole (IOD).
The AAO index, also known as the SAM (Southern Annular Mode), is defined as
a mean latitudinal difference of sea level pressure at 40 and 65
IPO is defined as a sea surface temperature (SST) anomaly over the
Pacific Ocean. The positive phase of IPO is characterized by a relatively
warm central and eastern tropical Pacific and a relatively cold
north-western and south-western Pacific (Henley et al., 2015; Dong and Dai,
2015). The IPO index is closely related to PDO (Pacific Decadal Oscillation), but
PDO better characterizes the Northern Pacific, while IPO is better applicable
to the whole Pacific region. We used IPO data because in the previous study
we found a teleconnection between the climate variability in the central
Antarctic and the tropical Pacific (Ekaykin et al., 2014). The data on the IPO index
since 1870 are available here:
The IOD is characterized by the Dipole Mode index (DMI), which is
defined as the SST gradient between the western equatorial Indian Ocean
(50–70
Here, we first consider the variability of surface air temperature recorded
at the meteorological stations in Princess Elizabeth Land to assess whether
the studied sector is characterized by uniform climate variability and to
provide a reference regional temperature record for comparison with the
Correlation coefficients between annual mean surface air temperature data at Vostok, Mirny and Davis vary between 0.6 and 0.9 (Table 2). Correlation coefficients between the automatic weather station LGB59 (located between Davis and Vostok, Fig. 1) and these three stations vary between 0.86 and 0.96. Despite the short record at LGB59, they are also significant at the 95 % confidence level. These results demonstrate that the region encompassed between these three stations has experienced similar climatic variability. This is further confirmed by a cluster analysis of surface air temperature data from 12 Antarctic stations (see Fig. S1 in the Supplement), showing that Vostok, Mirny, Casey, Mawson and Davis data form a single cluster in terms of climatic variability.
Correlation matrix between individual surface air temperature records from meteorological stations in the Princess Elizabeth Land.
All the correlation coefficients are statistically significant with 95 % confidence level.
Interestingly, the correlation coefficient between Mirny and Vostok data is
significantly weaker in 1958–1976 (
During the whole period of instrumental observations, the strongest relationships observed for temperature at Vostok were with temperature data at Mirny and Mawson coastal stations from the Indian Ocean sector and more precisely the sector between the Davis Sea and the Cooperation Sea.
As a result, Fig. 4a shows the average temperature anomaly from Vostok, Mirny and Davis stations. Hereafter, we use this stacked temperature record as an estimate of the temperature anomaly for the whole PEL sector.
Climatic variability in the Southern Hemisphere in 1958–2015.
We then compare the low-frequency variations in these various temperature records, using the 27-year low-pass filter (Fig. S2). Both Vostok and Mirny demonstrate a quasiperiodical variability with a period of about 30 years and maxima in the late 1970s and the late 2000s and demonstrate a very high similarity at low frequency. While Davis data have the same periodicity, their maxima are shifted to the early 1970s and early 2000s. If we consider other Antarctic stations, we see complex behaviour of air temperature in different sectors of Antarctica: most stations also show a 30-year cycle, but with a significant phase shift relative to the PEL region.
In the Indian Ocean sector, temperature peaks appear increasingly delayed when moving from west to east. For example, the first maximum occurred late in the 1960s at Mawson, early in the 1970s at Davis, in the second half of the 1970s at Mirny and late in the 1970s at Casey. This feature may reflect a low-frequency component of the Antarctic Circumpolar Wave (Carril and Navarra, 2001).
With respect to multi-decadal trends, contrasted patterns emerge: some stations (Esperanza, Novolazarevskaya, Davis, Vostok, Mirny, McMurdo) display a warming trend, while a cooling trend emerges at Halley or Dumont d'Urville (Fig. S2).
This comparison of instrumental temperature records highlights different
patterns of multi-decadal variability across different sectors of Antarctica,
which is important for interpreting paleoclimate records and for combining
various proxy records for temperature reconstructions (Jones et al., 2016).
Our analysis nevertheless demonstrates coherency within Princess Elizabeth
Land, where we will use the stacked temperature record from Vostok, Mirny and
Davis as a reference regional signal (hereafter named PEL temperature
anomaly) for the calibration of
Here we compare the PEL temperature anomaly with indices that characterize
climatic variability in the Southern Hemisphere. First, as expected, a very
strong negative relationship with the AAO index (
The correlation coefficient of PEL temperature anomaly with the IPO index is
weak (Fig. 4c), but the residuals of the PEL temperature regression with AAO
are negatively correlated with the IPO index (
A multiple linear regression approach leads to the conclusion that combined variations in AAO and IPO explain 59 % of the temperature variance on an inter-annual scale. While such teleconnection between Pacific and central Antarctic climate have previously been reported from Vostok data (Ekaykin et al., 2014), the underlying mechanism is not known. Finally, no significant correlation was identified between PEL temperature and the IOD index (Fig. 4d).
However, different results emerge when considering the low-pass-filtered time
series. On multi-decadal timescales, a strong positive correlation (
The stacked
This invokes a discussion of the factors that may disturb the correlation between the local air temperature and the stable water isotopic composition of precipitation in Antarctica (Jouzel et al., 2003).
Firstly, isotopic composition of precipitation is not a function of local air
temperature but of the temperature difference between the evaporation area
and the condensation site, which defines the degree of heavy water molecule distillation from an air mass. The study of the moisture origin for this
sector of Antarctica (Sodemann and Stohl, 2009) demonstrates that different
parts of the PEL differ in their moisture origin. Coastal areas receive
moisture from higher latitudes (46–52
Secondly, we should define which temperature is actually recorded in the isotopic composition of precipitation. For central Antarctica, where much (or most) of the precipitation is “diamond dust” from a clear sky (Ekaykin, 2003), the effective condensation temperature is conventionally considered equal to the temperature on the top of the inversion layer. But this is definitely not true for the coastal areas, where most precipitation falls from the clouds. Thus, the difference between near-surface and condensation temperature may vary in space and time.
Thirdly, the precipitation seasonality is another factor that may change the relationship between the air temperature and stable isotope content in precipitation. At Vostok the amount of precipitation is evenly distributed throughout the year (Ekaykin, 2003), so the snow isotopic content corresponds well to the mean annual air temperature, but we do not have robust information either about the other parts of the PEL or about the seasonality changes in the past.
Yet we believe that the main factor affecting the isotope–temperature relationship is “stratigraphic noise”. Indeed, even when we study the ice cores obtained at a short distance from one another (Ekaykin et al., 2014), the correlation between the individual isotopic records is still small, though the climatic conditions are the same.
This is why we argue that constructing a stacked isotopic record is an optimal way to reduce the amount of noise in the series and to highlight the variability that is common for the whole studied region, provided that the region is climatically uniform.
Despite the statistically insignificant correlation coefficient, we assume
that the stacked
The temperature reconstruction is displayed in Fig. 5b as a temperature anomaly relative to the 1980–2009 period. We also show the instrumentally obtained air temperature anomaly in Fig. 5b on the same temperature scale.
Antarctic climatic variability over the past 350 years.
Following Ekaykin et al. (2014), who reported a closer relationship between Vostok isotopic data and summer temperature than with the annual mean temperature, we performed additional analyses of relationships between our stacked isotope record and other temperature time series (e.g. monthly or seasonal temperature anomalies), but this does not improve the isotope–temperature correlation.
Despite discrepancies in the individual isotopic records (Fig. 3), a common
signal identified in the stacked record leads to several conclusions about
PEL climate variability over the past 350 years. During this time interval,
regional surface air temperature shows a long-term increasing trend and an
overall warming by about 1
A remarkably cold phase is observed during the 1840s, during which PEL
temperature could fall 1.2
Further studies are needed to understand whether such remarkably cold conditions arise from internal variability or are driven by the response of regional climate to an external perturbation. A possible candidate could be a response to volcanic forcing (Sigl et al., 2015). A moderate event is associated with the eruption of Cosigüina in 1835. According to the inventory of volcanic events recorded in the Vostok firn cores (Osipov et al., 2014), an unknown volcano erupted in 1840; however, the amount of deposited sulfate was about 15 % of that of Tambora, so it is not expected to have a major effect on the climate system. So far, the influence of volcanic forcing on Antarctic climate and the response time remain poorly known. By contrast, recent studies have stressed the delayed response of the North Atlantic Oscillation (Ortega et al., 2015) to major volcanic eruptions as well as their role as pacemakers of bidecadal variability in the North Atlantic (Swingedouw et al., 2015).
The period before 1700 is probably the coldest part of the record, but this
is not a robust result as the two records spanning this time interval show
somewhat different behaviours (Fig. 3). However, another stack of five East
Antarctic cores from PAGES (Past Global Changes) 2k (Fig. 5e) also highlights that the 1690s could
have been the coldest decade of the last 350 years. We also compare the
PEL2016 record with other Antarctic temperature reconstructions. Schneider
et al. (2006) used high-resolution isotopic records from five Antarctic sites (a
stack of Law Dome records, Siple Station, a stack of Dronning Maud Land
records and two ITASE (International Trans Antarctic Scientific Expedition) sites from West Antarctica). Although this record is
not significantly correlated with PEL2016 (
We also investigate the similarities between PEL2016 and the filtered stack
normalized isotopic East Antarctic record based on five East Antarctic ice cores
(Fig. 5e; data are available in the supplement of PAGES 2k Consortium, 2013).
The correlation with PEL2016 is weak (
The main difference between our PEL2016 record and the other isotopic stacked records for the whole of Antarctica (Fig. 5d) and for East Antarctica (Fig. 5e) appears for long-term trends, with a long-term increase in PEL2016 but no similar feature in the other reconstructions. We suggest that contrasted regional long-term trends may disappear in continental-scale reconstructions (see Fig. S2).
Finally, we compare our PEL2016 record with an IOD time series since 1870,
also processed with a low-pass filter. The strong correlation coefficient (
We now investigate the low-pass-filtered values of the snow accumulation rate, available at the 105 km, 200 km and Vostok sites (the latter is a stack curve from three deep snow pits), normalized over the period 1952–1981 (Fig. 6). All of them exhibit a negative trend, more prominent for the 200 km series. This result contradicts the stacked Antarctic snow accumulation rate record (Frezzotti et al., 2013) showing an overall increase in the accumulation rate during the last 200 years. Our finding is also not supported by the accurate assessment of average accumulation rate change between successive reference horizons at Vostok, showing a slight but significant increase in snow accumulation rate since 1816 (Ekaykin et al., 2004). Our results, moreover, stress the fact that, during the past few centuries, opposite long-term trends may have occurred in temperature and accumulation. This is counter-intuitive with respect to atmospheric thermodynamics and to the expected covariation of heat and moisture advection towards inland Antarctica. A similar divergence of the centennial trends of snow isotopic composition and accumulation rate was observed by Divine et al. (2009) at the coastal sites of Dronning Maud Land but not at the inland sites (Altnau et al., 2015).
Normalized (relative to period 1952–1981) and low-pass-filtered records of snow accumulation rate at sites 200 km (purple), 105 km (magenta) and Vostok (orange).
Processes other than snowfall deposition may, however, affect the ice core records. In the vicinity of 105 km, large “transversal” snow dunes have recently been evidenced (Vladimirova and Ekaykin, 2014). Such features may lead to a strong non-climatic variability in the snow accumulation rate at a given point, due to dune propagation effects. Blowing snow events may also have a significant influence on mass balance in the coastal zone of Antarctica (Scarchilli et al., 2010), potentially introducing additional post-deposition noise.
As a result, we are not confident that the datasets reported in Fig. 6 can be interpreted in terms of climate (snowfall) variations. Further work is needed to distinguish the large-scale climate effect (snowfall deposition) from the non-climatic effects potentially associated with post-deposition (wind erosion, dune propagation, etc.).
In this paper, we presented an analysis of the recent variability in snow
isotopic composition (
In order to interpret these data, we investigated the present-day mean annual surface air temperature variability using instrumental temperature measurements at the Mirny, Davis and Vostok stations, located at the margins of the sector being studied. It was shown that inter-annual climatic variability strongly covaries at these three stations. Cluster analysis demonstrated coherent variations for these stations, together with the nearby stations of Casey and Mawson. However, we stressed phase shifts between multi-decadal temperature variations along the coastal stations: temperature maxima and minima at Vostok and Mirny are delayed by a few years compared to those at Davis. On a broader geographical scale, temperature records from different sectors of Antarctica exhibit different climatic variability on a decadal scale in terms of periodicities, phasing and trends.
We then compared recent temperature variability in the PEL region with indices of Southern Hemisphere modes of variability and highlighted the importance of the Annular Antarctic Oscillation and the Interdecadal Pacific Oscillation, which in total explain 59 % of the temperature variance in this Antarctic region. On the multi-decadal timescale, however, temperature variations appeared most closely related to the Indian Ocean Dipole mode, which may modulate the cyclonic activity that brings heat and moisture to Princess Elizabeth Land.
Given the limitations of ice core data for inter-annual variations, we
processed our isotopic time series with a low-pass filter to cut off
variability expressed on timescales
In order to improve the signal-to-noise ratio, we constructed a stacked
isotopic record for the Princess Elizabeth Land based on data from all six
sites. We then used the linear regression between this record and the
instrumentally obtained air temperature record in order to convert the
isotopic composition scale into an air temperature scale. The apparent
isotope–temperature slope is 9
The newly obtained temperature reconstruction covers the period from 1654 to
2009. During this time, the temperature appears to have gradually increased
by about 1
Finally, our PEL record appeared closely related to the low-frequency component of the Indian Ocean Dipole mode.
The three accumulation time series depicted decreasing long-term trends and large inter-site differences. Further investigations of non-climatic drivers (including wind erosion and dune effects) are needed prior to confident climatic interpretation.
Our time series is provided as a Supplement to this manuscript. Understanding the cause of the reconstructed changes will require us to compare the PEL record with other regional Antarctic records, expanding the work of Jones et al. (2016) and combining simulations and reconstructions in order to better understand the mechanisms of regional climate multi-decadal to centennial variations and to explore the potential response of Antarctic climate to external forcing factors (e.g. volcanic eruptions).
Finally, this study stresses the importance of obtaining a dense network of
highly resolved ice core records in order to document the complexity of
spatio-temporal variations in the Antarctic climate, a key focus of the
Antarctic 2k project
(
Data to this article can be found in the Supplement.
The authors declare that they have no conflict of interest.
We kindly thank Barbara Stenni (the editor of the paper), Elisabeth Thomas, Dmitriy Divine (the reviewers) and Thomas Laepple, whose valuable comments and corrections allowed us to significantly improve the manuscript. We are very grateful to Alice Lagnado for improving the English.
This work is a contribution to the PAGES and IPICS “Antarctica 2k” projects. We are grateful to all the field technicians of the Russian Antarctic Expedition (RAE) and the drillers from St Petersburg Mining University for providing us with high-quality ice cores. We thank RAE for logistical support of our work in Antarctica. The Russian–French collaboration in the field of ice cores and paleoclimate studies is carried out in the framework of the International Associated Laboratory “Vostok”. We thank the CERL's staff for the isotopic analyses. The chemical analyses of the samples were performed at Irkutsk's Limnological Institute of RAS in the framework of the Russian Foundation for Basic Research grant 15-55-16001. One of the authors (Valérie Masson-Delmotte) was supported by Agence Nationale de la Recherche in France, grant ANR-14-CE01-0001.
This study was completed with financial support from the Russian Science Foundation, grant 14-27-00030. Edited by: B. Stenni Reviewed by: D. Divine and E. Thomas