This dissertation analyzes the teleconnection between low-latitude climate and the Arctic by examining correlations between Arctic temperatures and tropical ocean sea surface temperatures from 1979-2015. Significant correlations were found between tropical SSTs and Arctic air temperatures across all seasons, with the strongest correlations over Greenland and NE Canada in spring and summer. The El Nino Southern Oscillation was also found to influence Arctic temperature trends, particularly when a strong El Nino event coincides with a negative Northern Annular Mode phase. Arctic temperatures additionally correlate with climate indices like the North Atlantic Oscillation, Atlantic Multidecadal Oscillation, and Quasi-Biennial Oscillation.

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Analysis of the teleconnection between low-latitude climate
and the Arctic, with comparison to the Antarctic-low
latitude teleconnection.
Dissertation submitted for the MSc in Polar and Alpine Change
University of Sheffield
Department of Geography
James Brooks
September 2015

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Abstract
Arctic temperatures and their relationship with tropical ocean sea surface
temperatures (SSTs) are analysed using reanalysis and observational datasets, from
1979-2015, in order to explore the nature, and the temporal and spatial behaviour of
the teleconnection. In this study, the Arctic region, the tropical Pacific and Atlantic
Oceans were used. Correlation analysis of Arctic 2m-air temperature and tropical
SST, along with composite analysis and maximum covariance analysis (MCA) were
carried out. Several climate indices were used in this study also, such as the North
Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), the Quasi-
Biennial Oscillation (QBO) and the Northern Annular Mode (NAM). The Arctic
climate variability can be explained by the complex relationship present between the
tropics and the Arctic, in the temperature and geopotential height fields. These
correlations were across synchronous and lagged timescales, and the correlations were
found to not be stable over time or across all sectors. Significant correlations between
the tropical SST and the Arctic air temperatures were found for all seasons, with
varied significance for different regions of the Arctic, where strongest correlations
were present over Greenland and NE Canada sectors, for spring and summer. The
ENSO (El Nino or La Nina) event strength is found to be a significant influential
factor of Arctic temperature trends; when a strong El Nino event coincides with a
negative NAM, a teleconnection of Rossby wave propagation exists between the
tropical Pacific and the Arctic in the geopotential height field. Tropical SSTs correlate
significantly with the NAO, which in turn is correlated to Arctic temperatures. Arctic
temperatures also correlate significantly with the AMO and QBO climate indices.

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Table of Contents
1. INTRODUCTION
4
2. DATA
9
2.1. REANALYSIS DATA
10
2.2. OBSERVATIONAL STATION DATA
12
3. METHODOLOGY
16
4. RESULTS
21
4.1. ANALYSIS OF A LINK BETWEEN NINO REGIONS AND THE ARCTIC
21
4.2. ENSO COMPOSITE ANALYSIS
25
4.3. CORRELATION OF NAO AND ARCTIC TEMPERATURES
28
4.4. CORRELATION ANALYSIS OF AMO AND QBO WITH ARCTIC TEMPERATURES
33
4.5. ANALYSIS OF NAM-ENSO TELECONNECTION
35
4.6. TELECONNECTION ANALYSIS OF THE TROPICAL PACIFIC OCEAN, TO ARCTIC
CLIMATE
39
4.7. TELECONNECTION ANALYSIS OF THE ATLANTIC OCEAN, TO ARCTIC CLIMATE
42
5. DISCUSSION
44
5.1. ARCTIC-TROPICS LINK
44
5.2. ENSO LINK WITH ARCTIC TEMPERATURES
45
5.3. ENSO-NAM TELECONNECTION
46
5.4. LINK OF NAO AND ARCTIC TEMPERATURES
49
5.5. TELECONNECTION OF THE TROPICAL PACIFIC OCEAN TO ARCTIC CLIMATE
52
5.6. LINK OF AMO TO ARCTIC TEMPERATURES
53
5.7. TELECONNECTION OF TROPICAL AND NORTH ATLANTIC OCEAN TO ARCTIC
CLIMATE
54
6. CONCLUSION
56
APPENDICES
59
ACKNOWLEDGEMENTS
73
REFERENCES
74

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1. Introduction
Over the past 30 years, rapid Arctic warming has been widely attributed to
anthropogenic climate change [Francis and Vavrus, 2012; Overland et al., 2012;
Polyakov et al., 2012; IPCC, 2013]. One of the most striking examples of surface-
temperature warming is the polar region in the Northern Hemisphere (figure 1.1),
with the 10 warmest years in the instrumental record occurring since 2000 [NOAA,
2015]. As figure 1.1 shows that the warming is not spatially uniform, this raises a
question of whether natural climate variability has a role in driving and causing
regional climate change [Bader, 2014].
During recent decades, temperature increase has been larger over the Arctic
than the rest of the world, at the surface and throughout the troposphere, and has
given rise to the term Arctic Amplification (AA) [Screen and Simmonds, 2010;
Screen et al., 2012; Perlwitz et al., 2015]. AA has been most pronounced during the
autumn [Screen et al., 2013], and the period of AA has been found to coincide with
Arctic sea ice loss since around 2000 [Comiso et al., 2008; Parkinson and Comiso,
2013]. The modelling and empirical studies referred to here indicate that Arctic sea
ice loss has been the overriding driver of observed surface warming in the Arctic
[Perlwitz et al., 2015].
Figure 1.1 Trends in annual mean surface temperature across the Arctic region. The map
highlights the observed change per decade of annual mean surface and near-surface
temperature, for the period 1979-2012 (based on the ERA-Interim climate data set). Adapted
from Extended Data Figure 1 of Ding et al. (2014))

5. 140132032
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The reasoning behind observed Arctic tropospheric warming, however, is a
matter of controversy [Cohen et al., 2014; Perlwitz et al., 2015]. The magnitude of
Arctic warming throughout the lower-middle troposphere has been widely reported to
mostly be an atmospheric response to Arctic sea ice loss [Francis and Vavrus, 2012;
Perlwitz et al., 2015]. However, Perlwitz et al. [2015] explain that atmospheric
models, where changes in observed Arctic sea ice are specified, find that effects of
sea ice loss on Arctic temperatures are primarily confined to the lowermost
troposphere levels [Screen et al., 2013]. Therefore, the AA of warming in the lower to
middle troposphere is thought to unlikely be due to sea ice loss, suggested by the
modelling studies [Perlwitz et al., 2015]. An alternative explanation for the observed
warming in the troposphere above the Arctic is increased poleward heat transport,
which is related to SST changes that have occurred outside of the Arctic region
[Screen et al., 2012; Perlwitz et al., 2015].
The possible teleconnection between low-latitude climate and the Arctic is an
emerging area of research, with few studies published at time of writing. There is
extensive literature on how Rossby waves from the Pacific Ocean influence the North
Atlantic Oscillation (NAO) through Rossby wave breaking (RWB) [Strong and
Magnusdottir, 2008; Li and Lau, 2012; Wang and Magnusdottir, 2012]. However, the
only study on the specific topic of Arctic-low latitude teleconnection is by Ding et al.
[2014]. They set out, following the link found between the Antarctic and the tropics,
to establish if there was a significant link for the Arctic [Bader, 2014].
Over the past few decades, most surface warming and increases in
geopotential height in the Arctic have occurred over NE Canada, Greenland and north
Siberia [Ding et al., 2014] (as shown in figure 1.2), and much of the annual variability
in this region is linked to circulation changes in the North Atlantic, namely the NAO
[Bader, 2014].

6. 140132032
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Ding et al. [2014], amongst other studies [Budikova, 2009; Cohen et al.,
2014], are able to show recent warming is indeed strongly related to the negative
phase of the NAO (potentially linked to sea ice loss and Arctic Amplification (AA)),
and that warming across NE Canada and Greenland is the result of atmospheric-
circulation changes in the high troposphere. Ding et al. [2014] expand and explain
that this is due to anomalous Rossby wave train behaviour originating from tropical
Pacific cooling [Bader, 2014]. In their study, Ding et al. [2014] argue that it is
unlikely that Arctic decadal temperature changes in the upper troposphere are locally
forced, i.e. Arctic Amplification and sea ice loss, which also links to Perlwitz et al.
[2015]. They suggest instead that the warming throughout the atmosphere is the result
of atmospheric circulation changes in the high troposphere, and that such changes are
remotely forced [Bader, 2014].
By linking cooling in the tropical Pacific with trends in atmospheric
circulation and regional Arctic warming, Ding et al. [2014] were able to highlight the
inent annual mean surface
ince 1979 has occurred in
this region, much of the
ssociated with the leading
ty in the North Atlantic,
15
. Here we show that the
associated with a negative
hich is a response to anom-
ing in the tropical Pacific.
prescribedtropicalseasur-
rculation changes and asso-
over northeastern Canada
upledModelIntercompar-
prescribed anthropogenic
nges related to the North
pheric warming. This sug-
warming in the northeast-
rctic arises from unforced
IntergovernmentalPanelon
retreat of sea ice and warm-
missions of greenhouse gases
such as that associated with
ensuggestedtobeanimpor-
c region and responsible for
e recentresults also indicate
utsidetheArctichaveplayed
arming in the Arctic17
.
esand modellingtoexplore
c forcing and natural vari-
ctic. We identify a specific
gnificantly to recent Arctic
northern high latitudes.
ecause the analyses of geo-
actual height of a pressure
lesovertheNorthernHemi-
g the modern satellite era18
.
nterim19
andMERRA20
)and
unced annual mean surface
79 has occurred over north-
Siberia(Fig.1aandExtended
he Siberian coast are highly
th in situ sea-ice variability
standcanberelateddirectly
osphere has also experienced
oposphericwarmingismost
surface and tropospheric warming in the northeastern Canadian-
Greenland sector of the Arctic is nearly twice as large as the Arctic-
mean warming.
esearch Center, University of Washington, Seattle, Washington 98195, USA. 2
Department of Atmospheric Sciences, University of Washington,
Environmental Science, Monash University, Victoria 3800, Australia. 4
Climate Research Department, APEC Climate Center, 12 Centum 7-ro,
Surfacetemp.change(°C)300–850hPatemp.change(°C)Z200change(m)Non-zonal(m)
90° N
60° N
–0.2 –0.1 0.1 0.2 0.3 0.4
30° N
0
30° S
60° E
SST change
120° E 120° W 060° W
0.9
0.5
20
11
8
5
2
–2
–5
–8
–11
15
10
5
–5
0.4
0.3
0.2
–0.2
0.7
0.5
0.3
–0.3
180°
0 60° E 120° E 120° W 060° W180°
0 60° E 120° E 120° W 060° W180°
0 60° E 120° E 120° W 060° W180°
EQ
90° N
60° N
30° N
EQ
90° N
60° N
30° N
EQ
90° N
60° N
30° N
EQ
a
b
c
d
Figure 1 | Observed trend pattern of annual mean field for 1979–2012.
Lineartrend(per decade) ofannualmean surface temperature (a), 300–850 hPa
temperature (b), 200-hPa geopotential height (Z200; c) and the non-zonal
component of 200-hPa geopotential height (d). In a, surface temperature is
shown over land or ice; SST is shown over ocean. In d, purple vectors
(units: 106
Pa m2
s22
, vectors less than 105
Pa m2
s22
are omitted) denote the
wave activity flux associated with the eddy Z200 trend pattern. Grid points with
trends that are statistically significant at the 99% confidence level are denoted
by small crosses. The box in c indicates the domain over which data are
averaged in Extended Data Fig. 6. EQ, Equator.
8 M A Y 2 0 1 4 | V O L 5 0 9 | N A T U R E | 2 0 9
Macmillan Publishers Limited. All rights reserved©2014
Figure 1.2 Observed trend pattern of annual mean fields from 1979-2012. (a) Linear trend (per
decade) of annual mean surface temperature, (b) 300-850hPa temperature, (c) 200hPa
Geopotential height (Z200). Adapted from Fig. 1 from Ding et al. [2014].

7. 140132032
7
complexity of processes involved in regional climate change. Ideas involving air
advection, geopotential height anomalies and Rossby waves were accounted for in
this study. Emphasis was put on examining the teleconnection between the Arctic and
low-latitudes, and to aid with this, comparison was made with the Antarctic-low
latitude climatic link. Two tropical regions are found to influence the warming
observed over West Antarctica in particular.
The first is the Pacific Ocean, with the role of ENSO influencing Antarctic
climate [Ding et al., 2011]. Positive correlations have been found (~0.5 and sig. (p)
95% confidence level) between Nino 3.4 sea-surface temperatures (SSTs) and
pressure at the centre of the Amundsen-Bellingshausen Sea (ABS) [Lachlan-Cope
and Connelley, 2006]. A study by Fogt et al. [2011] found that ENSO correlates with
and influences the SAM; El Nino events relate to a negative SAM, and La Nina
events relate to a positive SAM. These findings highlight the existence of a
teleconnection, with the SAM related to interannual variability, however they do not
fully account for warming in the West Antarctic. Furthermore, there is a lack of
significant trends in ENSO through time in the tropical Pacific, which suggests a
tenuous link to recent warming over West Antarctica [Ding et al., 2011]. However,
changes in the tropical Pacific that are not always related to ENSO do affect
circulation at high latitudes – this is achieved through the generation of atmospheric
Rossby waves from positive SST anomalies (ENSO and non-ENSO related), which
propagate towards the Southern Ocean due to zonal winds [Ding et al., 2011]. This
study examines how the tropical Pacific Ocean links with the Arctic, through the use
of the tropical Pacific-Antarctic link, referred to here, as a guide.
The second link is with the tropical Atlantic; SSTs in the Atlantic are currently
increasing, and due to this over recent years, processes in the Atlantic have come to
the attention of climate researchers including the Atlantic Multidecadal Oscillation
(AMO) [Li et al., 2014a]. Surface warming is brought about by the AMO altering the
surface pressure in the Amundsen Sea region of West Antarctica [Li et al., 2014a],
with a low-pressure anomaly created through Rossby waves trains. This in turn heats,
through warm air advection by cyclonic circulation, the Antarctic Peninsula
[Stammerjohn et al., 2008], contributing to a warming trend of approximately 6 K
over the past 5 decades [Li et al., 2014a]. Due to recent positive trends in tropical

8. 140132032
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Atlantic SSTs, analysis of the Atlantic link to the Arctic in this study is vital for future
temperature change and variability. This study established if a similar link between
the Atlantic Ocean exists with the Arctic, with the idea of air advection into the Arctic
analysed due to changes in the Atlantic Ocean SST field.
Similar to the SAM influence on Antarctic warming patterns, the Northern
Annular Mode (NAM) plays an important role on the climate characteristics of the
Arctic. In the negative phase, for example, Arctic sea ice is thought to thicken with
help from wind fields and survive summer melt, as warm Atlantic air is unable to
penetrate the Arctic Ocean at high latitude [Rigor et al., 2002]. However, in the
present warmer climate state, its associated ice transport into the western Beaufort Sea
actually enhances summer ice loss [Stroeve et al., 2011]. Stroeve et al. [2011] and
Kim et al. [2014] explain that the winter of 2009/10 shed light on the chaotic nature of
this phenomenon, and suggests that possibly the state of the NAM has smaller
influence now on Arctic climate. The NAM/ENSO teleconnection is analysed in this
study, through the use of geopotential height anomalies and composite analysis in
order to determine air advection into the Arctic.
This study analysed the links between the Antarctic and tropics in relation to
the Arctic, and sought to find out if there are similar temporal and spatial patterns in
the Arctic as for the Antarctic. In order to do this, ideas and findings from other
studies were applied as a guide in relation to the Arctic-low latitude teleconnection
[Ding et al., 2011; Fogt et al., 2011; Li et al., 2014a].

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2. Data
This study took into account several geographical regions; the tropical Pacific
Ocean (El Nino 1+2, 3, 3.4 and 4 regions), the tropical and north Atlantic Ocean, and
the Arctic. When examining the Arctic, the region focused on was dependant on
findings in the reanalysis and observational datasets. Ding et al. [2014] focused on
Greenland and NE Canada, as the tropospheric warming was greatest there. With
regards to this study, all available weather stations were used over the Arctic. Spatial
correlations between SST, 500hPa geopotential height (Z500) and time were carried
out in order to understand more regarding the tropical teleconnection over the Arctic.
Figure 2.2 Nino regions in the Tropical Pacific Ocean, used
in this study [NOAA, 2015]
Figure 2.1 Map of the Arctic region [NERC Arctic Office,
2015]
Figure 2.3 Map of the Atlantic Ocean [University
of Texas, 2015]

10. 140132032
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The Arctic region spans from 600
N-900
N and 00
-3600
longitude [Benn and
Evans, 2010], as shown in figure 2.1. For the interest of this study, the tropical Pacific
Ocean region spans from 1600
E-800
W and 200
S-200
N, as shown in figure 2.2, with all
four Nino regions used. This geographical range was chosen as the Nino regions are
within these latitudinal and longitudinal boundaries, and it follows previous studies’
choice that use the tropical Pacific for climatic and oceanic studies [Ding et al. 2011,
2014]. The tropical Atlantic spans from 200
S-200
N, 700
W-200
W, and North Atlantic
spans from 0-700
N, 700
W-100
W (as shown in figure 2.3); these were used in line with
previous work carried out by Li et al. [2014a].
This study used station observational data and reanalysis data products to
explore the tropical forcing of Arctic warming. Only post-1979 observations and data
were used, because the analysis of temperature and geopotential height (amongst
other variables) over the Northern Hemisphere and Arctic regions is more reliable and
accurate during the modern satellite era [Bromwich et al., 2007; Ding et al., 2014].
2.1. Reanalysis Data
Reanalysis data were used in this study, and the products of interest are called
ERA-Interim, which supplied atmospheric and oceanic circulation data, and
temperature variable data. ERA-Interim is one of the latest global atmospheric
reanalysis datasets produced by the European Centre for Medium-Range Weather
Forecasts (ECMWF). The analysis is produced with a 2006 version of the IFS
(Cy31r2) model, and the data is 4-dimentional variational, with spatial resolution at
approximately 80km on 60 vertical levels, from the surface up to 0.1 hPa [Dee et al.,
2011]. The data used in this study was monthly averages of daily means (see
Berrisford et al. [2011] for detailed documentation of the parameters in ERA-
Interim).
The ERA-Interim reanalysis provides the geopotential height data, with
500hPa (Z500) chosen for this study. Geopotential height is roughly the height above
sea level of a pressure level [NOAA, 2015]. At an elevation of h, the geopotential is
defined as:

11. 140132032
11
where g is the acceleration due to gravity, Φ is latitude, and z is the geometric
elevation. Thus, geopotential is the gravitational potential energy per unit mass at that
elevation h. The geopotential height is:
which normalizes the geopotential to the standard gravity at mean sea level [Lynch
and Cassano, 2006; Hofmann-Wellenhof and Moritz, 2006; Eskinazi, 2012].
Error sources are found in ERA-Interim [Dee et al., 2011]. An error source
that has relevance to this study is regarding the quality of trends derived from
reanalysis data; they need to be verified against independent observations. This can be
difficult and problematic in the tropics or the Arctic, as observational data for certain
parameters can be sparse in these regions. However, due to the importance of
studying the Tropical Pacific over recent decades [IPCC, 2013; Cai et al., 2014], vast
observation networks are now in existence, and this issue can be regarded as
negligible in the tropics [NDBC, 2015].
A project called Climate Reanalyzer was used through a web interface
(http://cci-reanalyzer.org/Reanalysis_monthly/index_correl.php), in order to generate
maps of linear correlation between gridded reanalysis data and user-defined
observational data or climate indices. Note should be taken with this project, as it
does not supply detrended data. Panoply data plotter tool was used, obtained from
http://www.giss.nasa.gov/tools/panoply/, to work with the large netcdf files (.ncdf)
obtained for the ERA-Interim data. It allowed slicing of geo-referenced latitude-
longitude arrays from larger multidimensional variables [NASA GISS, 2015]. An
online monthly and seasonal climate composite plotter was used in this study, found
at http://www.esrl.noaa.gov/psd/cgi-bin/data/composites/printpage.pl. This tool
(Eq 1)
(Eq 2)

12. 140132032
12
allowed plotting of seasonal composites (averages) of the mean or anomalies (mean -
total mean) of variables from the NCEP reanalysis [NOAA, 2015].
2.2. Observational station data
Land based station and oceanic data for SST and 2m-air temperature, for
monthly averages, were used in this study. For the Arctic, open access land-based
station data were obtained from a subsidiary of the National Oceanic and
Atmospheric Administration (NOAA) website called the National Climatic Data
Centre (NCDC). 10 weather stations were selected using a map feature; the search
criteria included stations that had data spanning the study period of interest from
1979-2015, and that had high data coverage (at least 90% for the study period).
Weather station data for Greenland was also obtained from Prof. Ed Hanna
(University of Sheffield), who supplied 7 station datasets. Details of all 17 weather
stations can be found in table 2.1, Appendix table 1, and spatially in figure 2.4.

13. 140132032
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Figure 2.4 Map of Arctic weather station locations. Esri, DeLorme, GEBCO, NOAA NGDC, and other
contributors. Sources: Esri, GEBCO, NOAA, National Geographic, DeLorme, HERE, Geonames.org, and other
contributors.

14. 140132032
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Table 2.1 Details of Arctic weather stations. All stations are within ~100m of sea-level. Sources:
Cappelen [2011], Cappelen et al. [2001], Hanna et al. [2014], NOAA [2015], Steffen and Box [2001].
For full station name details, see Appendix table 1.
For the tropical oceans, gridded SSTs were obtained from another subsidiary
of the NOAA, called the National Weather Service Climate Prediction Centre (CPC).
The SST data was part of the NOAA Extended Reconstructed Sea Surface
Temperature (SST) V3b project (ERSSTv3), where a global monthly SST analysis
from 1854 to present was derived from the most recently available International
Comprehensive Ocean-Atmosphere Data Set (ICOADS) [NOAA, 2015]; data with
missing fields were filled in using statistical methods. The spatial coverage of the data
is 2-degree latitude x 2-degree longitude global grid, spanning most of the globe
(880
N–880
S, 00
E–3580
E). The monthly SST indices were used from the various Nino
regions in the tropical Pacific Ocean, with table 2.2 and figure 2.2 displaying the
geographical locations used in this study.
Station name
used in study
World
Meteorological
Organisation
(WMO) Code
Latitude
(0
N)
Longitude
(0
W)
Elevation
(m)
Available data
period
Baren - 780
140
49 Jan 1979-Feb 2015
Barrow - 710
-1570
12 Jan 1979-Feb 2015
Eureka - 800
-860
10 Jan 1979-Feb 2015
Federova - 780
1040
12 Jan 1979-Feb 2015
Ostrov - 800
770
10 Jan 1979-Feb 2015
Svalbard - 780
150
28 Jan 1979-Feb 2015
Chok - 710
1480
44 Jan 1979-Feb 2015
Dikson - 740
800
42 Jan 1979-Feb 2015
Hopen - 770
250
6 Jan 1979-Feb 2015
Tiksi - 720
1290
6 Jan 1979-Feb 2015
Aas 4220 680
42’ -520
45’ Unknown Jan 1958-Aug 2013
Ilul 4221 690
14’ -510
04’ Unknown Jan 1873-Aug 2013
Nuuk 4250 640
10’ -510
45’ Unknown Jan 1890-Aug 2013
Paam 4260 620
0’ -490
40’ Unknown Jan 1958-Aug 2013
Qaq 4272 600
43’ -460
03’ Unknown Jan 1961-Aug 2013
Uper 4211 720
47’ -560
08’ Unknown Jan 1873-Aug 2013
Narsar 4270 610
10’ -450
25’ Unknown Jan 1873-Aug 2013

15. 140132032
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Nino Region Latitude (0
N or S) Longitude (0
W or E)
1+2 0 – 100
S 900
W – 800
W
3 50
N – 50
S 1500
W – 900
W
3.4 50
N – 50
S 1700
W – 1200
W
4 50
N – 50
S 1600
E – 1500
W
Table 2.2 Location details for the different Nino regions in the Tropical Pacific Ocean.
NOAA_ERSST_V3 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from
their Web site at http://www.esrl.noaa.gov/psd/.
For the Arctic temperature and the tropical Pacific SST data, as well as the
various climatic indices (NAO, NAM etc.), detrending was carried out. Detrending is
the statistical operation of removing a trend from a time series. It is used as a pre-
processing step in this study in order to prepare the time series for analysis by
methods that assume stationarity, i.e. correlation coefficient and regression analysis
[Arizona, 2015].
Error sources are present in the weather station data that was downloaded, the
main of which was missing values for some of the stations. This was corrected using
two techniques; interpolation from other stations nearby to fill in the missing data, or
by using linear regression of the station for the study time period (1979-2015), where
the regression curve was used to extrapolate temperature values (used for several of
the Arctic stations provided by Prof. Ed Hanna).
Several climatic indices were used in this study. The AMO and QBO were
used, and both the AMO and QBO values were downloaded from the Earth System
Research Lab (ESRL) subsidiary of NOAA
(http://www.esrl.noaa.gov/psd/data/correlation/). The NAO index was used; the
monthly NAO index values were downloaded from the Climate Prediction Centre
(CPC), which is a subsidiary of the NOAA
(http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml). The NAM
index was also used in this study; monthly NAM index values were also downloaded
from the Climate Prediction Centre (CPC)
(http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao.shtml).

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3. Methodology
With the collected data, statistical tests were carried out, with regression and
correlation coefficient analysis. For the correlation coefficient analysis, the
significance of the correlation values was found by using the degrees of freedom
(DOF), to establish the 5% significance level figure. When seasonal analysis was
carried out, months were allocated to seasons according to Parkinson et al. [1999];
boreal winter was assigned as December-January-February (DJF), boreal spring as
March-April-May (MAM), boreal summer as June-July-August (JJA), and boreal
autumn as September-October-November (SON).
Pearson correlation coefficient analysis was carried out between Arctic
stations and the Nino regions, split by season (DJF, MAM, JJA and SON) for Nino
SSTs and Arctic 2-m air temperature measurements. This technique was used so that
it could be established whether Arctic station temperatures have a significant
relationship with the Nino regions at different times of the year. It also allowed the
analysis of whether there is a spatial distribution of significant correlations across
different regions of the Arctic; through the use of MATLAB software, Arctic maps
overlaid with dots signifying significant correlation locations were created, to show if
certain Nino regions are more correlated than others with the Arctic.
Lagged Pearson correlation coefficient analysis was carried out between
Arctic air temperatures and Nino region SSTs. 1-6 month lags were used in order to
capture any Arctic-low latitude teleconnection, for a comprehensive study, and also as
it is known that any changes or events in the tropical Pacific Nino regions take time to
propagate to the Arctic and affect the atmospheric variables [Ding et al., 2014]. By
using 6 different time lags, one attempted to look for when any correlation value
changes occur, and when the correlations are both strongest and weakest.
Lagged Pearson correlations were also used for analysis of the relationship
between detrended Arctic weather station data and both detrended Atlantic
Multidecadal Oscillation (AMO) and Quasi Biennial Oscillation (QBO) index values.
The AMO is a leading mode of global variability [Li et al., 2014a], and is a mode of
natural variability occurring in the North Atlantic Ocean with its principle expression

17. 140132032
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in the SST field [UCAR, 2012]. It has been associated with changes in the global
oceanic thermohaline circulation, but may also be influenced by atmospheric blocking
and from the indirect aerosol effect [Li et al., 2014a]. The QBO is regarded as the
mean zonal wind of the equatorial stratosphere and zonally symmetric easterlies and
westerlies alternate regularly [Hung, No Date]. Despite it being known that laterally
propagating extratropical Rossby waves do not play a major role in forcing the QBO
[Wallace and Holton, 1968], it is thought that the extratropical Rossby waves
contribute to the QBO’s momentum budget [Hung, No Date]. Lagged correlations
show how the correlations change with different time lags, and the results were
plotted spatially in MATLAB to analyse the geographical distribution of the results,
using significance plot correlation maps.
Composite analysis was carried out to establish if Arctic temperatures were
significantly different during an El Nino event compared to a La Nina event, and also
if temperatures in the Arctic not during an ENSO event (regarded as neutral) are
significantly different than those during an El Nino or La Nina event. This analysis
shows whether the two different ENSO events cause significant differences on the
Arctic temperatures. From the monthly mean temperature station data from 1979-
2015, t-tests for each individual weather station were carried out to determine if
certain areas or stations have a greater significance difference between ENSO events
than others, which was using detrended data; this process did not assume a linear
relationship. Labelling of the strongest El Nino and La Nina years was carried out to
establish the strong ENSO events, and to aid with this the Oceanic Nino Index (ONI)
was used, which identifies El Nino (warm) and La Nina (cool) events in the tropical
Pacific Nino 3.4 region. ENSO events are defined as 5 consecutive overlapping 3-
month periods at or above the +0.50
C anomaly for warm (El Nino) events, and -0.50
C
for cool (La Nina) events. The threshold was then further broken down into strong
(10
C or above SST anomaly) events [NOAA, 2015]. The Arctic temperatures were
then noted for the El Nino and La Nina events and the two lags, with 2-tailed, paired-
sample student t-tests carried out on the datasets, to test for significance. Where the
labelling of the El Nino and La Nina events is concerned, the peak months of the
event was chosen, which is December-January-February (DJF). This was aided with
help from using the 3-month mean Oceanic Nino Index (ONI) values, where the peak
was seen to be more-or-less equal between ENSO events. The 3-month running mean

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of ERSST.v4 SST anomalies in the Nino 3.4 region was based on centered 30-year
base periods updated every 5-years [Huang et al., 2015; NOAA, 2015].
Pearson Correlation coefficient analysis, annual and seasonal, was carried out
between the Arctic weather station 2-m air temperature data and the monthly mean
North Atlantic Oscillation (NAO) index from 1979 to 2011, as Hurrell and Deser
[2009] and Ding et al. [2014] state that recent warming over the Greenland and NE
Canada is linked to a negative phase of the NAO. As well as using the station data
and the NAO index from the CPC, an online tool called Climate Reanalyzer was used
in order to calculate the spatial correlations between geopotential heights at 500hPa at
each grid point with the NAO (compared to 200hPa used in Ding et al. [2014]).
500hPa geopotential height was chosen as a better alternative due to the surface
altitude level of Greenland being quite high due to the ice sheets, but also because it
means the 500hPa level is situated close to the jet stream in the upper-troposphere.
This allowed the study of external forcing in the Arctic to be more in depth, due to the
jet stream height in the upper atmosphere being where the increased heat transport
into the Arctic is taking place [Perlwitz et al., 2015].
As outlined in the introduction, the Antarctic-tropics link was used to aid in
analysis of the Arctic-tropics teleconnection. One of the methods to do this was to
study the link between the Northern Annular Mode (NAM), i.e. the Arctic Oscillation
(AO), and the El Nino/La Nina events in the tropical Pacific Ocean (ENSO). This was
carried out with regards to the Antarctic-tropics teleconnection, in a study by Fogt et
al. [2011], who researched the El Nino/La Nina link with the Southern Annular Mode
(SAM) that exists around the Antarctic continent.
To investigate changes in the NAM index due to an ENSO teleconnection, this
study followed some similar techniques that have been used in other climatic studies
[Fogt and Bromwich, 2006; Fogt et al., 2011]. Pearson correlation and composite
analysis were both used in this study. Nonetheless, correlation analyses do not always
provide the best insight on the ENSO teleconnection variations, because correlation
does not provide any insight into the magnitude of the ENSO events and can be
strongly influenced by the presence of outliers in the dataset [Fogt et al., 2011];
Lachlan-Cope and Connolley [2006] explain strong ENSO forcing is required to

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produce the Rossby wave train pattern to influence high latitude climate. Therefore,
this study uses the composite analysis to filter out weak ENSO events (only strong El
Nino and La Nina events used), and the composites were based on monthly-detrended
datasets, allowing the correlations to be between the residuals of each dataset (see
earlier description on how strong ENSO events were determined using ONI values).
Correlation maps, produced using MATLAB, aid in geographically displaying the
results of the correlation coefficient analysis. To establish if the NAM has a link with
the ENSO events, composite student t-tests were carried out between the NAM values
for El Nino and La Nina events.
With the NAM-ENSO teleconnection in mind, a further technique to study the
effect ENSO events have on the Arctic temperatures, through the NAM, was carried
out. The ENSO data from NOAA [2015] was manipulated, and a list was made for all
strong ENSO event seasons, as well as all neutral ENSO seasons, from 1979-2015
(see earlier description on how strong ENSO events were determined using ONI
values). A second list was then made that assigned the seasons, for all years, a NAM
index value, allowing categorisation of seasons for the pairings of El Nino/NAM- and
La Nina/NAM+, as well as neutral ENSO/NAM- and neutral ENSO/NAM+. These
years were entered into the NCEP/NCAR Reanalysis Seasonal Climate Composite
plotter, provided by NOAA [2015]. 500hPa geopotential height (Z500) composite
anomaly plots were created for the different NAM/ENSO pairings, which were
relative to the 1981-2010 long term Z500 mean.
In order to fully study the tropics-Arctic teleconnection, maximum covariance
analysis (MCA) decomposition was used to capture the dominant coupled modes
between the Northern Hemisphere Z500 (0-850
N) and tropical Pacific SST (200
S-
200
N, 1600
E-800
W), and also between Northern Hemisphere Z500 and north Atlantic
(0-700
N, 700
W-100
W) and tropical Atlantic (200
S-200
N, 700
W-200
W) SST. The
method refers to isolating pairs of spatial patterns and their associated time series, by
performing singular value decomposition (SVD) on the temporal covariance between
the two data fields [Wallace et al., 1992; An, 2003]. For example, the SST (T) is
represented by:

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where e (x, y) is the spatial pattern associated with the SST anomalies. αn (t) denotes
the expansion coefficient associated with SST stress anomalies, and is calculated by
projecting SST anomaly fields at a given time on each eigenvector [An, 2003].
MCA is similar to Empirical Orthogonal Function (EOF) analysis in that they
both deal with a covariance matrix, and its decomposition. In EOF, the spatial-
temporal field in the covariance matrix is singular, whereas in MCA it is based on the
decomposition of a ‘cross-covariance’ matrix from two fields [An, 2003]. The
statistical software used for the MCA was R [R Core Team, 2014] (see Appendix
table 2 for full MCA script used). In the computation of the cross-covariance matrix,
the number of columns, i.e. the amount of spatial points, need not be the same,
however the row dimensions, i.e. time, must be equal [Bjornsson and Venegas, 1997].
The resolution of the MCA carried out was using 2-degree latitude x 2-degree
longitude ERA-Interim data. It should be noted that, in this study, only the first mode
of the MCA decomposition was carried out, due to limitations in building the
programming code in R.
To further study the tropics-Arctic teleconnection, canonical correlation
analysis (CCA) was carried out, which is used for diagnosing coupled patterns in
climate fields and measures the linear relationship between two multidimensional
variables [Borga, 2001]. CCA was used to capture the maximum correlation between
the Northern Hemisphere Z500 (0-850
N) and tropical Pacific SST (200
S-200
N, 1600
E-
800
W). Barnett and Preisendorfer [1987] illustrated the aforementioned method;
CCA analysis is based upon a shortened subset of Empirical Orthogonal Function
(EOF) coefficients to explain the variability (i.e. principle components) instead of
using the original field, as in MCA. This method produces similar results to that of
MCA, but the patterns produced reflect maximum correlation rather than maximum
covariance [Storch and Zwiers, 1999]. The statistical software used for the CCA was
R [R Core Team, 2014] (see Appendix table 3 for full CCA script used).
(MCA; von Storch
ingular value de-
erton et al. 1992;
a useful tool for
wo different geo-
n climate research
et al. 1992; Wang
refers to a method
nd their associated
analysis (so-called
ar algebra) on the
two data fields. To
ns of two variables
h other.
chnology Contribution
ch Center Contribution
An, International Pa-
Hawaii at Manoa, Hon-
For example, the sea surface temperature (SST; T)
and zonal wind stress (tx) anomalies are represented by
linear combinations by applying MCA,
T(x, y, t) 5 a (t)e (x, y)O n n
n
t (x, y, t) 5 b (t) f (x, y),Ox m m
m
where en(x, y) and f m(x, y) are the spatial patterns (ei-
genvectors) associated with SST and zonal wind stress
anomalies, respectively, and an(t) and bm(t) denote the
expansion coefficient associated with SST and wind
stress anomalies, respectively, and they are calculated
by projecting SST and wind stress anomaly fields at a
given time on each eigenvector. Using the MCA, we
can detect the most coherent patterns between two var-
iables. However, sometimes a resulting pattern is not
due to a physical interaction between two variables but
due to an external forcing effect. In this case, it is nec-
essary to remove the external effect from the coherent
pattern. In this study, I propose a method called ‘‘con-
ditional maximum covariance analysis’’ (CMCA) for
removing the unwanted signal and for detecting the in-
ternal coupled mode.
(Eq 3)

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4. Results
4.1. Analysis of a link between Nino regions and the Arctic
The first statistical test was correlation analysis between the Nino region SSTs
and the Arctic weather station 2m air temperature, split by season (DJF, MAM, JJA,
SON), the results of which can be found in Appendix table 4, with a summary in table
4.1 below. When correlation coefficient results are positive, this indicates Arctic
temperatures increasing with Nino temperature increases, whereas negative values
show decreasing Arctic temperatures with decreasing Nino temperatures.
Tropical Pacific
region
What seasons were significantly
correlated? (p 95% confidence level)
Correlation +ve
or -ve
Nino 1+2 All seasons Negative
Nino 3 Summer (sometimes sum+wint) Negative
Nino 3.4 Summer and Spring Sum = negative
Spr = positive
Nino 4 Spring Positive
Table 4.1 Pearson Correlation coefficient results between average Arctic 2m Air Temp and Nino
SSTs split by season from 1979-2015
It appears, from table 4.1 and figure 4.1, that the significant correlation values
(p 95% confidence level) mostly occur in the spring and summer seasons, with Nino
1+2 regions showing significant correlations all year round. Of the 17 Arctic weather
stations used in this study, 4 showed results of no significant correlations for any
season or Nino region pairing.
Figure 4.1 Mean monthly temperature values for Svalbard summer temperatures, correlated
against Nino 3.4 summer SSTs

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Figure 4.2 shows four correlation maps; the Nino 3.4 correlation analysis with
Arctic temperatures during Spring (A), Summer (B), Autumn (C) and Winter (D)
from 1979-2015. In spring, the correlation results return as mostly significantly
positive values, whereas in summer, the correlation results are significantly negative.
Across the two seasons, however, the spatial distribution of the significant values
seem to be quite similar, except with a few less significant values for summer. In
spring, the correlations are strongest over the Siberian-Arctic and NE Canada
coastline, whereas in summer, the correlations are strongest over Greenland.
Lagged correlations between detrended Arctic station temperatures and the
Nino region SSTs were the second statistical technique carried out; results for every
station is shown graphically in figure 4.3 (see Appendix table 5 for full results). At 0
time lag, significant negative correlations exist between the Eastern Pacific Nino
regions (1+2, and 3) and Arctic station temperatures, and significant positive
correlations with the Western Pacific Nino regions (3.4 and 4). From the 1 to 6 month
time lags on Arctic station data, significant positive correlation values dominate.
Significant negative correlations then occur once again, but this time at 4, 5 and 6-
month lag, and on the opposite side of the Pacific, i.e. Western Pacific Nino regions
(3.4 and 4). Figure 4.3 clearly shows a peak in correlation coefficient values at around
the 4-month lag.

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A
B
Figure 4.2 Correlation maps to display the relationship between detrended Nino 3.4 SSTs, and detrended (A)
Spring Arctic temperatures, (B) Summer Arctic temperatures, (C) Autumn Arctic temperatures, and (D)
Winter Arctic temperatures. The colour of the dot plot signifies the correlation coefficient (positive or
negative) and the black circles around some of the stations highlight significant correlation coefficient
analysis values (p 95% confidence level)
C
D

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Figure4.3Graphtoshowthelaggedcorrelationcoefficientanalysisresults,between
detrendedArctic2mairtemperaturesanddetrendedNinoregionSSTs.Theredshaded
areaindicatestherangeofinsignificantcorrelationvalues(p95%confidencelevel)

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4.2. ENSO Composite Analysis
Composite analysis was also carried out, which involved 2-tailed paired
sample student t-tests for Arctic weather station 2m temperature data during ENSO
events (El Nino and La Nina); there is no assumption of a linear relationship in this
analysis. The station composite analysis sought to establish if a significant difference
in Arctic temperature exists between El Nino and La Nina events, to see if ENSO
events have a significant affect on Arctic temperatures. Table 4.2 shows a summary of
results for these composite analyses.
Arctic
Weather
Station
Arctic-El Nino and Arctic-La
Nina t-test results for all ENSO
events (p value)
Arctic-El Nino and Arctic-La
Nina t-test results for strong
ENSO events (p value)
0 lag 3-month
lag
6-month
lag
0 lag 3-month
lag
6-month
lag
Baren 0.48 0.83 0.12 0.45 0.84 0.17
Barrow 0.39 0.55 0.61 0.40 0.65 0.75
Eureka 0.98 0.98 0.89 0.86 0.98 0.72
Federova 0.04 0.76 0.97 0.02 0.46 0.71
Ostrov 0.12 0.98 0.27 0.07 0.78 0.48
Sval 0.54 0.89 0.10 0.59 0.86 0.13
Chok 0.99 0.98 0.98 0.07 0.90 0.99
Dikson 0.13 0.71 0.50 0.44 0.53 0.65
Hopen 0.34 0.73 0.05 0.61 0.57 0.11
Tiksi 0.36 0.95 0.97 0.03 0.93 0.91
Aas 0.50 0.80 0.30 0.30 0.99 0.83
Ilul 0.20 0.90 0.43 0.18 0.96 0.72
Narsar 0.97 0.96 0.42 0.34 0.98 0.92
Nuuk 0.45 0.56 0.30 0.92 0.86 0.62
Paam 0.49 0.95 0.43 0.78 0.86 0.76
Qaq 0.94 0.45 0.49 0.42 0.85 0.99
Uper 0.53 0.89 0.53 0.35 0.70 0.94
Table 4.2 Composite Analysis t-test results (p values) for each detrended Arctic station
temperature, split by 0, 3 and 6 month lags, between El Nino and La Nina events. Green
highlighted cells indicate significant t-test values (p 95% confidence level). Light-blue highlighted
cells indicate significant t-test values (p 90% confidence level)
As one can see, at 0 time lag, one station has a significant difference in Arctic
temperature (p 95% confidence level) between El Nino and La Nina events, in the all
Nino events category, and 2 significant differences in Arctic temperature between El
Nino and La Nina events, for just strong ENSO events. When the data moves to 3-
month and 6-month time lags, no significant t-test results (p 95% confidence level)

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are present for any Arctic weather station between El Nino and La Nina events,
between all ENSO events at 6-month lag.
As there is very little significant difference in Arctic temperatures between El
Nino and La Nina events (both strong and moderate) for most stations, the next step
was to carry out student t-tests for the ENSO event Arctic temperature, against Arctic
temperatures for all months not during an ENSO event (regarded as neutral dates).
The results for these t-tests can be found in table 4.3. Table 4.3 shows that for the 0
and 3-month lags, for every Arctic station (except Hopen at 3-month lag), there is a
significant difference between Arctic temperatures for neutral dates, and ENSO event
Arctic temperatures. At 6-month time lag, however, there are no stations that have
significant differences between ENSO event Arctic temperatures and neutral date
temperatures.

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Arctic
Weather
station
Neutral dates t-test against El
Nino events (p value)
Neutral dates t-test against La
Nina events (p value)
0-lag
3-
month
lag
6-
month
lag 0-lag
3-
month
lag
6-
month
lag
Baren 2.34x10-05
0.011 0.779 0.0007 0.0065 0.2707
Barrow 8.11x10-05
0.005 0.761 0.0020 0.0018 0.9570
Eureka 2.10x10-03
0.001 0.806 0.0019 0.0007 0.9265
Federova 3.05x10-04
0.006 0.660 0.0166 0.0015 0.9534
Ostrov 3.94x10-05
0.008 0.874 0.0046 0.0051 0.3514
Sval 1.11x10-05
0.009 0.646 0.0004 0.0058 0.2722
Chok 3.19x10-04
0.002 0.862 0.0008 0.0016 0.8544
Dikson 1.96x10-03
0.018 0.858 0.0024 0.0063 0.4867
Hopen 2.71x10-06
0.079 0.893 0.0002 0.0343 0.0561
Tiksi 2.54x10-04
0.005 0.719 0.0028 0.0040 0.8054
Aas 1.34x10-03
0.004 0.578 0.0075 0.0044 0.6838
Ilul 8.02x10-04
0.008 0.192 0.0118 0.0088 0.2766
Narsar 3.84x10-04
0.019 0.718 0.0035 0.0180 0.7757
Nuuk 2.76x10-04
0.006 0.954 0.0001 0.0041 0.6102
Paam 5.63x10-04
0.026 0.741 0.0003 0.0198 0.9513
Qaq 1.08x10-04
0.021 0.776 0.0014 0.0296 0.7667
Uper 2.36x10-03
0.025 0.337 0.0095 0.0128 0.3143
Table 4.3 Composite Analysis t-test results for each detrended Arctic temperature station
dataset, split by 0, 3 and 6-month lags, against El Nino and La Nina events. Highlighted cells
indicate significant t-test results (p 95% confidence level)

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4.3. Correlation of NAO and Arctic temperatures
Correlation analysis was carried out between detrended Arctic weather station
2-m air temperature and detrended North Atlantic Oscillation (NAO) index values,
expanding on the findings of Ding et al. [2014] that state recent warming in
Greenland and NE Canada is linked to a negative phase of the NAO. Table 4.4 shows
the results of the correlation analysis, with figure 4.4 graphically representing the
correlation results from one Arctic station.
Arctic Weather
station
Correlation (r)
values for Arctic
temperatures
against NAO
Arctic Weather
station
Correlation (r)
values for Arctic
temperatures
against NAO
Baren -0.109 Tiksi -0.184
Barrow -0.223 Aas -0.353
Chok -0.203 Ilul -0.352
Dikson -0.135 Narsar -0.395
Eureka -0.248 Nuuk -0.392
Federova -0.156 Paam -0.396
Hopen -0.068 Qaq -0.409
Ostrov -0.135 Uper -0.329
Sval -0.105
Table 4.4 Correlation coefficient analysis results for each detrended Arctic Weather station 2-m
air temperature annual values, against detrended annual NAO index values, from 1979-2015.
Highlighted boxes indicate significant correlation coefficient values (p 95% confidence level)
As can be seen in table 4.4 and figure 4.5, all except for one Arctic weather
station (Hopen) show significant negative correlations with the NAO index values,
over the study period of 1979-2015. This means that warmer (cooler) temperatures are
present across the Arctic region, with a negative (positive) NAO index. The
correlation values are greater for the stations situated in Greenland, with smaller yet
significant (p 95% confidence level) values for the stations located elsewhere around
the Arctic region. Figure 4.5 shows how the correlation coefficient strength is
spatially consistent to the findings in Ding et al. [2014], with the strongest

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correlations located over Greenland and NE Canada, but also with significant
correlations located across the Arctic region.
The next step that was carried out with regards to correlation analysis between
Arctic weather station 2-m air temperature and the NAO index was to correlate
seasonally; the full results for these correlation coefficient analyses can be found in
Appendix table 6. As can be seen in Appendix table 6 and figure 4.6 (for A-spring, B-
summer, C-autumn, and D-winter), the temperatures at the weather stations located
across Greenland show significant negative correlations with the NAO index for
Figure 4.5 Correlation map for annual NAO index values, against Arctic air temperatures, from
1979-2015. The colour of the dots signify the correlation value, with black circles highlighting
significant correlation coefficient values (95% confidence level)
Figure 4.4 Scatter graph highlighting a significant negative correlation between detrended 0-
lag Qaq 2-m air temperatures and NAO index values, for 1979-2015

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every season, whereas the correlation results are more varied as you look across the
Arctic region as a whole for each season.
A
B
C
D
Figure 4.6 Correlation maps for (A) Spring NAO, (B) Summer NAO, (C) Autumn NAO, and (D) Winter
NAO, against Arctic temperature data, from 1979-2015. The colour of the dots signify the correlation value,
with black circles highlighting significant correlation coefficient values (p 95% confidence level)

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To further test these correlation coefficients, Climate Reanalyzer was used.
The correlation coefficient analysis used here involved the Arctic weather station
temperature data and the NAO index values. In order to compare and build upon the
Ding et al. [2014] findings of recent warming being linked to a negative NAO,
Climate Reanalyzer was used to calculate the correlations between ERA-Interim
geopotential height at 500hPa (Z500) and the NAO index, split by season.
Geopotential height was used in this instance as temperature change, whether it is
SST or 2m-air temperature, is known to affect geopotential height values; the
correlation (r) between 34-year annual mean geopotential height at 200hPa and
surface temperature is 0.9 in the Arctic region [Ding et al., 2014].
Analysis was carried out to create correlation maps split into season, as shown
in figure 4.6 previously. Correlations were then calculated between the ERA-Interim
geopotential height at 500hPa and the NAO (principal component) index, as outlined
before, but split into each season (as shown in figure 4.7 (A-D)). It should, however,
be noted that it is not possible to show whether the correlations are significant using
this online tool. It is clear that the spatial distribution of Arctic stations with
significant correlations between the NAO and air temperature, match with the
strength of the correlation on the Climate Reanalyzer maps. By this, it is meant that
where the correlation values are significant between Arctic station temperature and
NAO index, it matches up with the locations where the correlation is strong between
geopotential height at 500hPa and the NAO (principal component) index. For
example, in figure 4.7 (A-D) the large dark blue region over Greenland shows an area
of strong correlation between Z500 and NAO values, which is seen in the Arctic
temperature data, with the stations located in and around Greenland displaying
significant correlation results for all seasons. For particular seasons, regions where the
negative (blue) correlation values extend to in figure 4.7, match with locations where
significant values exist in the Arctic temperature and NAO correlations. An example
of this can be found in figure 4.7 (A), where Tiksi weather station, located over the
Siberian Arctic coast line, east of the large dark blue region, has a significant
correlation value in winter with the NAO index, thus showing that the spatial
correlations between NAO and geopotential height match with the Arctic station data
correlations, and therefore link to circulation and advection of air into the Arctic.

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A
B
C
D
Figure 4.7 Climate Reanalyzer output plots, with the correlation values between ERA-Interim geopotential height
at 500hPa and NAO (principal component) values, split by season (A: winter, B: spring, C: summer, D: autumn),
from 1979 to 2011. Images obtained using Climate Reanalyzer (http://cci-reanalyzer.org), Climate Change Institute,
University of Maine, USA.

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4.4. Correlation analysis of AMO and QBO with Arctic temperatures
Correlation analysis was also carried out between detrended Arctic weather
station 2m-air temperature data, and the climatic phenomena of the Atlantic
Multidecadal Oscillation (AMO) and the Quasi Biennial Oscillation (QBO). As can
be seen in table 4.5 and figure 4.8, correlations between all Arctic weather stations
and the AMO exhibit significant positive results. There are slightly greater correlation
values for the weather stations located around Greenland, but the values elsewhere in
the Arctic are still significant at (p) 95% level. As shown in table 4.5 and figure 4.9,
correlations between all Arctic weather stations (except Dikson) and the QBO exhibit
significant negative results, with all values slightly lower than those of the AMO
correlations.
Arctic Weather station Correlation (r) value
with AMO
Correlation (r) value
with QBO
Baren 0.236 -0.190
Barrow 0.301 -0.130
Eureka 0.322 -0.143
Federova 0.263 -0.117
Ostrov 0.260 -0.163
Sval 0.241 -0.185
Chok 0.306 -0.133
Dikson 0.294 -0.0772
Hopen 0.203 -0.181
Tiksi 0.298 -0.129
Aas 0.283 -0.120
Ilul 0.314 -0.121
Narsar 0.323 -0.106
Nuuk 0.334 -0.129
Paam 0.351 -0.129
Qaq 0.330 -0.118
Uper 0.309 -0.143
Table 4.5 Results from correlation coefficient analysis between detrended Arctic weather
stations, and the AMO and QBO index values. Highlighted boxes indicate significant correlation
coefficient values (p 95% confidence level)

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Figure 4.8 Correlation map for annual AMO index values, against Arctic air temperatures, from
1979-2015. The colour of the dots signify the correlation value, with black circles highlighting
significant correlation coefficient values (95% confidence level)
Figure 4.9 Correlation map for annual QBO index values, against Arctic air temperatures, from
1979-2015. The colour of the dots signify the correlation value, with black circles highlighting
significant correlation coefficient values (95% confidence level)

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4.5. Analysis of NAM-ENSO teleconnection
As was stated in the introduction, this study looked to compare the Arctic-
tropics teleconnection to that of the Antarctic-tropics teleconnection, to see if
similarities exist. One method of doing so is studying the link between the Northern
Annular Mode (NAM), i.e. the Arctic Oscillation (AO), and the El Nino/La Nina
event SSTs in the tropical Pacific Ocean. As outlined earlier, the Fogt et al. [2011]
study concluded that El Nino is correlated to a negative SAM, and La Nina is
correlated to a positive SAM around the Antarctic continent.
Correlation coefficient analysis was carried out between NAM index values
and the El Nino/La Nina SSTs in the tropical Pacific, for 0, 3 and 6-month lags, with
the results found in table 4.6. As can be seen in table 4.6, there are very few
significant correlation results between NAM index values and El Nino/La Nina SSTs,
across the tropical Pacific, for all monthly time lags.
Monthly
lag
Nino
region
Correlation (r)
value between El
Nino SST and
NAM index value
Monthly
lag
Nino
region
Correlation (r)
value between La
Nina SST and
NAM index value
0 1+2 -0.261 0 1+2 0.445
3 -0.003 3 0.295
3.4 0.289 3.4 -0.787
4 0.126 4 -0.154
3 1+2 0.370 3 1+2 0.016
3 0.413 3 -0.307
3.4 0.326 3.4 0.270
4 0.318 4 -0.080
6 1+2 -0.050 6 1+2 0.052
3 -0.104 3 0.144
3.4 -0.025 3.4 -0.838
4 -0.171 4 -0.011
Table 4.6 Correlation coefficient results between NAM index values and El Nino/La Nina
SSTs, split by 0, 3 and 6-month lags. Green highlighted cells indicate significant correlation
values (p 95% confidence level)

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As the correlation between Arctic station temperatures and ENSO SSTs is
somewhat inconclusive, the next step was to extract the NAM values for the El Nino
and La Nina events, take an average of each, and then carry out 2-tailed paired sample
t-tests between the El Nino NAM and the La Nina NAM values, to see if there are
significant differences between them. Table 4.7 shows the results of the NAM-ENSO
t-tests, with clear significant differences existing between the El Nino and La Nina
NAM values, at both 0 and 3-month lags. These results suggest that El Nino has a
statistical link to negative NAM, and La Nina events have a statistical link to positive
NAM.
0-month lag 3-month lag 6-month lag
NAM
index
values for
El Nino
dates
NAM
index
values for
La Nina
dates
NAM
index
values for
El Nino
dates
NAM
index
values for
La Nina
dates
NAM
index
values for
El Nino
dates
NAM
index
values for
La Nina
dates
0.97 1.68 -0.57 1.53 0.31 0.85
1.36 3.11 -0.74 -0.25 0.13 0.87
-1.81 3.28 -0.44 0.89 1.10 0.55
-0.53 1.04 -0.20 -0.45 0.06 0.59
0.27 1.27 -0.56 -0.28 -0.14 -0.20
-1.07 1.08 -0.85 0.97 0.25 0.14
-0.07 -1.50 -0.25 1.42 -0.71 -0.40
-2.08 -1.68 -0.04 2.27 -0.21 -0.47
-0.18 1.58 -0.10 -0.04 0.65 -1.06
Average -0.35 1.09 -0.42 0.68 0.16 0.01
t-test p
value
0.04 0.01 0.80
Table 4.7 2-tailed paired sample t-test results for NAM index values, split by El Nino and La
Nina events, for 0, 3 and 6-month lags. Highlighted cells indicate significant t-test results (p
95% confidence level)

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To further analyse the NAM-ENSO teleconnection, the effect on northern
hemisphere 500hPa geopotential height (Z500) of SSTs during ENSO events was
studied. In order to carry this out, spatial plots were created for spring and summer of
Z500 composite anomalies, against a 1981-2010 long-term mean, using NCEP/NCAR
reanalysis data [NOAA, 2015]. Spring and summer was used following the significant
correlations between Nino 3.4 SSTs and Arctic station temperatures, highlighted in
section 4.1. The output from using the composite anomaly plotter is shown in figure
4.10 (A-F). It should be noted that there were no cases of NAM+/La Nina
combinations over the study period of 1979-2015, and determining the significance
for whether the anomalies are significantly different from the norm on the plots is not
possible with this online tool.
Figure 4.10 A (spring) and D (summer), for the NAM-/El Nino combinations,
highlight a clear teleconnection between the tropical Pacific SST and the Arctic
atmospheric circulation, through a Rossby wave train signature in the Z500 anomaly
field. For spring, it originates in the tropical Pacific, around 1500
W, with a positive
Z500 anomaly, then moves to a negative anomaly over the American sector and a
positive Z500 anomaly over E Canada, with finally a negative Z500 anomaly area
over the Greenland and NE Canada region. For summer, the picture is quite similar;
except the positive Z500 anomaly region over E Canada is situated over Greenland,
with a negative anomaly region over NE Canada. The plots, for the NAM-/El Nino
combination in spring and summer, only account for two years of strong El Nino, but
nevertheless show that during these strong El Nino events, the tropical Pacific
connects to the Arctic through the Rossby wave propagation. Figure 4.10 (B, C, E and
F) for the combinations of NAM-/neutral ENSO and NAM+/neutral ENSO events
show that there is no clear Rossby wave train of Z500 anomalies present in the
tropical Pacific Ocean that propagate to the Arctic.

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Figure 4.10 NCEP/NCAR reanalysis plots of 500hPa geopotential height (m) anomalies, using the 1981-2010 long-
term mean. (A) Spring El Nino/NAM- combination, (B) Spring neutral ENSO/NAM-, (C) Spring neutral
ENSO/NAM+, (D) Summer El Nino/NAM-, (E) Summer neutral ENSO/NAM-, (F) Summer neutral ENSO/NAM+.
Images provided by the NOAA/ESRL Physical Sciences Division, Boulder Colorado from their Web site at
http://www.esrl.noaa.gov/psd/.
A
B
C
D
E
F

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4.6. Teleconnection analysis of the tropical Pacific Ocean, to Arctic climate
In order to fully study the tropics-Arctic teleconnection, the statistical
technique of Maximum Covariance Analysis (MCA) decomposition was carried out.
The results outlined in this study so far show that the spatially varying character of
Arctic warming suggests an impact from atmospheric circulation changes in the
tropics. Ding et al. [2014] found that annual mean 200hPa geopotential heights
(Z200) have increased over most of the Arctic region since 1979, with greatest
increases over Greenland and NE Canada. With previous studies finding that the
correlation (r) between annual Z200 and surface temperature is 0.9, and 0.8 for the
detrended data [Ding et al., 2014], it shows that changes in geopotential height are
strongly correlated with surface temperature (also shown in section 4.3).
Findings in Ding et al. [2014] suggest that the Z200 changes over Greenland
and NE Canada are associated with a Rossby wave train, originating in the tropical
Pacific. In this study, it has been shown that the NAO is strongly correlated to the
Arctic air temperatures (see table 4.4). With keeping this in mind, it was hypothesized
in Ding et al. [2014] that the SST pattern in the tropics has played a key role in
forcing the wave train warming Greenland and NE Canada, and induced the negative
trend in the NAO index over recent decades.
This study builds upon the findings of Ding et al. [2014] with figure 4.11
showing the leading mode of co-variability between Northern Hemisphere (00
N-
88.50
N) Z500 anomalies, and tropical Pacific (200
S-200
N, 1600
E-800
W) SST
anomalies for the study period of 1979-2015. The leading MCA mode explains 91%
of the squared covariance between the hemispheric Z500 and tropical Pacific SST
fields.

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Following from the MCA decomposition results, canonical correlation
analysis (CCA) was carried out, to study the correlation value between the two fields.
The CCA analysis carried out in this study used the same data fields (Northern
Hemisphere geopotential height at 500hPa (Z500) and tropical Pacific SST) as for the
MCA deocomposition. The resulting CCA results are shown in figure 4.12. It can be
seen that it somewhat resembles the MCA mode figure, shown previously in this
section (figure 4.11).
Figure 4.11 Coupled patterns between northern hemisphere circulation and tropical Pacific SST for 1979-
2015. Results for maximum covariance analysis for anomalies of 1979-2015 northern hemisphere (0-88.50
N)
500-hPa geopotential height (Z500) and anomalies of tropical Pacific (200
S-200
N, 1600
E-800
W) SST. (A) shows
the geopotential height monthly anomalies for the MCA mode 1, with (B) showing the SST monthly anomalies
for the MCA mode 1. (C) is the time series of the Z500 (green) and SST (blue) patterns shown in the top two
boxes.
A
B
C

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Figure 4.12 Coupled patterns between northern hemisphere circulation and tropical Pacific SST for 1979-2015.
Results for canonical correlation analysis for anomalies of 1979-2015 northern hemisphere (0-88.50
N) geopotential
height at 500hPa (Z500) and anomalies of tropical Atlantic (200
S-200
N, 1600
E-800
W) SST. (A) shows the Z500
monthly anomalies for the CCA, with (B) showing the SST monthly anomalies for the CCA. (C) is the time series
of the Z500 (green) and SST (blue) patterns shown in the top two boxes.
A
B
C

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4.7. Teleconnection analysis of the Atlantic Ocean, to Arctic climate
Following from the ideas in section 4.6, with regards to the Antarctic-tropics
context, this study independently identifies a teleconnection between north (0-700
N,
700
W-100
W) and tropical (200
S-200
N, 700
W-200
W) Atlantic SST and Northern
Hemisphere (30-88.50
N) geopotential height at 500hPa (Z500). Figure 4.13 shows the
results for the Northern Hemisphere Z500 and tropical Atlantic SST, with the leading
MCA mode explaining 92% of the squared covariance between the two variables.
Figure 4.14 shows the results for Northern Hemisphere Z500 and North Atlantic SST,
with the leading MCA mode explaining 85% of the squared covariance between the
two variables.
Figure 4.13 Coupled patterns between northern hemisphere circulation and tropical Atlantic SST for 1979-2015.
Results for maximum covariance analysis for anomalies of 1979-2015 northern hemisphere (20-88.50
N)
geopotential height at 500hPa (Z500) and anomalies of tropical Atlantic (200
S-200
N, 700
W-200
W) SST. (A) shows
the Z500 monthly anomalies for the MCA mode 1, with (B) showing the SST monthly anomalies for the MCA
mode 1. (C) is the time series of the Z500 (green) and SST (blue) patterns shown in the top two boxes.
A
B
C

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Figure 4.14 Coupled patterns between northern hemisphere circulation and North Atlantic SST for 1979-2015.
Results for maximum covariance analysis for anomalies of 1979-2015 northern hemisphere (20-88.50
N)
geopotential height at 500hPa (Z500) and anomalies of North Atlantic (00
S-700
N, 700
W-100
W) SST. (A) shows the
Z500 monthly anomalies for the MCA mode 1, with (B) showing the SST monthly anomalies for the MCA mode
1. (C) is the time series of the Z500 (green) and SST (blue) patterns shown in the top two boxes.
A
B
C

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5. Discussion
The Arctic-low latitude tropical teleconnection is present, highlighted through
the statistical techniques used in this study, and consists of various underlying
seasonal and lagged trends. Numerous spatial dimensions exist to the trends, with
similarities that can be drawn from the Antarctic-low latitude teleconnection.
5.1. Arctic-tropics link
An atmospheric response is detected in the Arctic, due to SST changes in the
tropical Pacific. The results indicate that atmospheric and oceanic variability are
significant factors in the changing Arctic climate.
Correlation of Nino SSTs against Arctic 2m air temperature, across 17
weather stations, split by season indicated significant results at certain times of the
year; spring Arctic temperatures showed significant positive correlations, whereas
summer indicated significant negative correlation values (see figure 4.2). Summer
significant negative correlations suggest tropical Pacific cooling driving Arctic
warming, which agrees with Ding et al. [2014].
The lagged correlations indicated the western Tropical Pacific Nino region
(Nino 4) had significant negative correlations at 4-6 month lag, with positive
correlations at other time lags and with other Nino regions (Nino 1+2, 3 and 3.4). The
significant negative correlations at 4-6 month lags suggest that through an
atmospheric teleconnection with the Arctic, tropical Pacific Ocean cooling is driving
Arctic warming. This is an interesting finding, and expands on that of Ding et al.
[2014], who stated that Greenland and NE Canada warming has been driven by
cooling in the tropical Pacific Ocean, as this study has highlighted that the whole of
the Arctic temperature warming is linked to tropical Pacific cooling (specifically the
west tropical Pacific Ocean).
The fact that the Summer Arctic temperatures, along with 4-6 month lags,
both give significant negative correlations to tropical Pacific SSTs in the summer,

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strongly suggests the presence of the teleconnection, possibly due to Rossby waves
taking time to propagate to the Arctic high-latitudes. The timescale of such an
atmospheric dynamic process would match with 3-6 months [Wang and
Magnusdottir, 2012], and suggests a role from ENSO events. The NAM-ENSO
results from section 4.5 also tie in with these conclusions, with Rossby wave trains
linking the tropical Pacific to the Arctic during strong El Nino/NAM- pairings.
5.2. ENSO link with Arctic temperatures
Due to the seasonal and lagged correlations between Nino SST and Arctic
temperature returning significant results for Nino 3.4 and 4 regions in Spring and
Summer, and at 4-6 month lag, composite analysis was carried out in order to further
examine the potential Arctic-tropics teleconnection.
Composite analysis was used to find the influence of ENSO event SSTs on
Arctic temperatures. At the 0-3 month lags, for every Arctic station, there is a
significant difference between Arctic temperatures during an ENSO event, and Arctic
temperatures not during an ENSO event. These outcomes were not found for the 6-
month lags, which returned no significant differences. The findings indicate that El
Nino and La Nina events, which have their peak strength around winter (December-
January-February) [NOAA, 2015], have an influence on Arctic temperatures. It should
be noted that the polarity of difference in Arctic temperatures (i.e. whether
temperatures increase or decrease during an ENSO event) between ENSO events or
non-ENSO events is not specified. These findings, however, do tie in to the previous
analysis of establishing an Arctic-tropics teleconnection [Ding et al., 2014], as the
influence of ENSO takes time to propagate to the Arctic through Rossby waves
[Wang and Magnusdottir, 2012], thus the summer negative correlations with western
tropical Pacific SSTs could be linked with the ENSO influence on Arctic
temperatures.

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5.3. ENSO-NAM teleconnection
A study by Fogt et al. [2011], where ENSO was found to correlate with and
influence the Southern Annular Mode (SAM) around Antarctica, was used as an aid
with regards to the Arctic-tropics teleconnection. Keeping in mind the previous
results and analysis in section 5.1 and 5.2, that highlighted a potential teleconnection
between ENSO SSTs and Arctic temperatures, this study sought to establish whether
a relationship was present with the Northern Annular Mode (NAM) around the Arctic.
As outlined in the introduction, a positive NAM is thought to allow increased warm-
air transport into the Arctic region, thus decreasing sea-ice extent (SIE) and
reinforcing Arctic warming [Kim et al., 2014].
Pearson correlation results highlighted that El Nino and La Nina SSTs do not
significantly correlate with the NAM index values, at either 0, 3 or 6-month lags.
Despite a significant negative correlation between La Nina SSTs and the NAM index
values, there is no clear ENSO-NAM teleconnection being established. Composite
analysis results, using 2-tailed paired sample t-tests, indicate that there are significant
differences in the NAM index values during El Nino events, and during La Nina
events (p < .05) at 0 and 3-month lags, but not at the 6-month lag. A negative NAM
index is associated with El Nino events, and a positive NAM index is associated with
La Nina events. These results indicate that, despite the correlations being mostly
insignificant and suggesting there is no teleconnection between ENSO and the NAM
index, there is a clear difference in NAM values. This suggests that there is a non-
linear relationship between the two variables, which is different to the SAM-ENSO
connection studied through the composite analysis in Fogt et al. [2011]. This result
indicates the inherent importance of insignificant results, i.e. not just significant
results are meaningful. These results link back to the composite analysis, where a
clear relationship exists between the tropical Pacific and the Arctic, with the NAM-
ENSO teleconnection reinforcing these ideas.
As shown in section 5.1, the summer Arctic temperatures have a significant
negative correlation with Nino 3.4 SST, indicating that Arctic temperatures may be
increasing subsequent to changes in tropical Pacific SST, due to Rossby wave trains.

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How does the NAM-ENSO teleconnection link with this? And does the sea ice cover
in the Arctic play a role? Stroeve et al. [2012] explain that over the past decade or so,
the pattern of extreme September sea ice extent minima, hastening transition towards
an open Arctic ocean, suggests acceleration in response of Arctic sea ice cover
changes to external forcing. Due to the September decline in sea ice cover, Fowler et
al. [2004] explain a temporal shift in distribution of ice age-classes in spring towards
more thin, first-year ice, which is more prone to melting. This also means more
fragmented sea ice cover, and leads to an increased importance of the ice-albedo
feedback [Francis and Vavrus, 2012; Stroeve et al., 2012]. Stroeve et al. [2012]
believe that this decline is linked with the behaviour of the NAM, as it shifted to a
more positive index between the late 1980s and the early 1990s. A cyclonic anomaly
in the sea ice circulation pattern culminated from the alteration in the NAM, helping
to transport sea ice out of the Arctic Ocean, through the Fram Strait, and promote
increased first year ice production [Rigor and Wallace, 2004; Stroeve et al., 2012].
Since 1995, the NAM has altered between positive and negative [Stroeve et al., 2012;
Kim et al., 2014]. Climate model simulations suggest that, of the observed negative
trend in September sea ice cover, at least part is externally forced and it hence follows
that external forcing has contributed in part to the observed increase in first year ice
concentrations [Maslanik et al., 2007; Stroeve et al., 2012].
All this indicates that the decrease in September sea ice could be due to
changes in the NAM index and is externally forced [Stroeve et al., 2012]. The
external forcing is shown in this study, through the significant negative correlations in
the summer and significant positive correlations in the spring, between Nino 3.4 SSTs
and Arctic temperatures, the composite analysis results for Arctic temperatures
between ENSO and non-ENSO events, and the MCA analysis. This study also
highlighted that the ENSO events are found to affect the NAM, therefore highlighting
a teleconnection. These processes could clearly have an influential role in sea ice
cover behaviour in the Arctic, therefore accelerating Arctic warming over recent
decades; causality was not established in this study however. Li et al. [2014b] share a
similar idea and state that the NAM-ENSO relationship strengthened after the mid-
1990s, when the inter-annual variability of the previous September sea ice cover had
significantly increased, i.e. reduced September sea ice cover, and also that the NAM
is strongly coupled to the circulation in the Pacific Ocean.

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With the results of the 500hPa geopotential height anomalies during El Nino
and NAM combinations (see section 4.5), it is clear that a teleconnection exists
between the tropical Pacific and Arctic climate. Due to SST anomalies in the tropical
Pacific during El Nino/NAM pairings, but not for La Nina/NAM pairings, a Rossby
wave train of Z500 anomalies exist that propagate to the Arctic (see results figure
4.10). The plots highlight the presence of low Z500 anomalies over regions such as
Greenland and NE Canada in spring, for the El Nino/NAM- combination. The
presence of such a low anomaly has implications on the air advection into the Arctic.
The circulation of air around a low Z500 anomaly would be counter-clockwise,
therefore moving warm air from the American sector and the Pacific Ocean up into
the Arctic. This could be responsible, in part, for the positive correlation values
between Arctic temperature and Nino 3.4 SSTs in spring, as the warm air advection
due to the Z500 anomalies would increase the air temperatures in the Arctic. This
proposition clashes with those of Kim et al. [2014], who state that a positive NAM
increases warm air advection into the Arctic, rather than a negative NAM as
explained here. These results expand upon those of Ding et al. [2014] by
demonstrating that there is a clear Arctic-tropical Pacific teleconnection, but by using
a different technique. This technique uses the composite analysis ideas from Fogt et
al. [2011], who state that there is a SAM/ENSO teleconnection over the Antarctic
continent. The results indicate that the Arctic-low latitude teleconnection may be
heavily dependant on the NAM phase [Fogt et al., 2011] as only when El Nino occurs
with a negative NAM does the teleconnection exist, and the anomalous eddy flows
from the tropics reinforce with the higher latitudes, through the Rossby waves.
There is a potential teleconnection between the Arctic and the Indian Ocean,
present in the Z500 anomaly plots, in boreal summer, when an El Nino coincides with
a negative NAM. A positive geopotential height anomaly area exists over the Indian
Ocean, with a negative anomaly zone over Eastern Europe, and a positive anomaly
region over Greenland and NW Siberia (see results figure 4.10 (D)). This finding
links into previous literature, with Gao et al. [2014] explaining that a spring
relationship exists between the Arctic Oscillation and the East Asian monsoon, which
is thought to be unstable, with figure 4.10 (A) indicating here that the Arctic-Indian
Ocean teleconnection is not present in spring. Through model simulations, it has been
shown that the Indian Ocean SSTs, in a phenomenon called the Indian Ocean Dipole

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(IOD), affects ENSO variability by forcing an anomalous Walker circulation, which
in turn enhances the trade wind anomalies and causes a warmer El Nino event [Wu
and Kirtman, 2003; Gong et al., 2014]. This suggests that the Indian Ocean can
enhance ENSO events, which would then have a secondary impact on the Arctic
through enhanced Rossby wave trains of Z500 anomalies. This idea is present in the
Z500 anomaly plot (figure 4.10 D), with the positive Z500 anomalies present over the
Indian Ocean, and stretching out to the tropical Pacific. The plots used involve strong
El Nino events, so therefore this links with the literature that the Indian Ocean can
enhance a warmer El Nino event [Gong et al., 2014].
Li and Chen [2014] explain that there is a response in the strength of the
southern stratospheric polar vortex to Indian Ocean warming, in austral summer; if
the Indian Ocean has been found to affect the Antarctic, there may be a similar
influence on the Arctic. There is not much literature, at time of writing, examining the
potential Indian Ocean teleconnection to the Arctic through the Z500 anomaly field,
thus providing the opportunity for further study.
5.4. Link of NAO and Arctic temperatures
Many studies have explained that the Arctic has undergone rapid annual mean
surface and tropospheric warming since 1979, across the whole of the region [Screen
et al., 2012; IPCC, 2013; Cohen et al., 2014; Perlwitz et al., 2015], and in particular
Greenland and NE Canada, where much of the year-to-year variability in temperature
is associated with the NAO index [Ding et al., 2014]. Their paper shows that recent
Arctic warming is strongly linked with a negative trend in the NAO index, as a
response to anomalous Rossby wave trains from the tropical Pacific [Ding et al.,
2014], with these findings expanded upon in this study. The pattern of warming in the
Arctic can be characterised as an increase in geopotential height, combined with
negative polarity of the NAO index. This study found that a year-round NAO index
exhibits a strong negative correlation with 2m air temperatures across the Arctic. The
correlation values were strongest over Greenland (as shown in results table 4.4), with
the correlation values smaller over the other Arctic locations, but still significant (p
95% confidence level). Correlations between the NAO index and Arctic 2m air

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temperatures were also carried out seasonally, to expand upon the study by Ding et al.
[2014], and found that the stations situated around Greenland showed significant
negative correlations with the NAO index for every season, whereas across the Arctic,
the results were more varied (as shown in figure 4.6).
The NAO, when in its negative phase, causes the jet stream to have increased
meandering [Ding et al., 2014]. When the correlations for particular seasons return
insignificant values between NAO and Arctic temperatures at different stations, the
effect of the meandering jet stream and advection of warmer southerly winds into the
Arctic, may not reach all stations across the Arctic.
Arctic air temperature change is known to affect geopotential height values;
Ding et al. [2014] explain that the correlation value (r) between the two variables is
0.9 in the Arctic region. The correlation analysis carried out using Climate Reanalyzer
showed that Arctic geopotential height at 500hPa (Z500) is correlated with the NAO
index, and also was able to show that for each season, the correlation values between
Arctic temperature and NAO values matched up spatially with the correlation values
between Z500 and NAO values. Ding et al. [2014] explain that the warming over
Greenland and NE Canada is associated with a negative trend in the NAO index,
which is in response to anomalous Rossby wave train activity originating from the
tropical Pacific Ocean. This study expands upon this idea, through the use of station
Figure 5.1 MCA mode results between northern hemisphere Geopotential Height at 500hPa anomalies,
and tropical Pacific SST anomalies. The colour bar indicates the change in values for the two plots, with
decreased Geopotential Height represented by orange colours on the plot.
L
H
H
L

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data highlighted previously and Climate Reanalyzer correlation output, and shows
that warming across the whole of the Arctic region is associated with a negative trend
in the NAO.
Hanna et al. [2014] highlight the role the NAO plays in Arctic warming, by
analysing the atmospheric forcing of the exceptional Greenland ice sheet (GrIS) melt
in summer 2012. They explain, along with a study by [Belleflamme et al., 2015], that
in 2012, as in other recent summers since 2007, a high blocking feature, that is
associated with a negative NAO, was present in the mid-troposphere over Greenland
[Hanna et al., 2014]. A ‘heat dome’ was formed over Greenland due to the circulation
pattern advecting relatively warm southerly winds over the western flank of the ice
sheet [Hanna et al., 2014]. This ‘heat dome’ idea could be present in this study from
1979-2015, in the NAO-Arctic temperature correlation maps, that highlight
significant negative correlations for most of the stations over western Greenland for
all seasons, and in the Climate Reanalyzer plots showing strong correlations between
Z500 and Arctic temperatures over Greenland. Stroeve et al. [2012] explain the
warming in summer 2012 was found to be greatest in the mid-troposphere, with sea
surface temperatures and sea ice anomalies playing a minimal role. This, therefore,
suggests the potential of external forcing [Hanna et al., 2014], which is supported by
the teleconnection ideas present in this study. A model-simulation study by Peings
and Magnusdottir [2014] found that the NAO response to tropical and north Atlantic
SSTs, along with Arctic sea ice and Siberian snow anomalies, accounted for
approximately 30% of the NAO anomaly, highlighting a teleconnection between the
tropics and the NAO index.
The QBO index values, correlating significantly to Arctic temperatures, could
also play a role in effecting the NAO. The data in this study show that the QBO is
negatively correlated with Arctic temperatures, thus suggesting an easterly QBO
could be in turn enhancing the negative phase of the NAO [Watson and Gray, 2014].
Watson and Gray [2014] also states that the AMO has had a significant role in
altering the NAO; an idea tested in the Atlantic-Arctic teleconnection (MCA)
analysis. With the QBO correlation values being smaller for each station than the
AMO values, this could suggest that the QBO influence is lower than the AMO role
on Arctic warming, through the effect on the NAO index.

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5.5. Teleconnection of the tropical Pacific Ocean to Arctic climate
To establish if the teleconnection suggested in earlier sections (5.1-5.4) is
significant, Maximum Covariance Analysis (MCA) decomposition was carried out.
The leading MCA mode explains 91% of the squared covariance between the
Northern Hemisphere Z500 and the tropical Pacific SST fields. As shown in figure
5.1, the MCA mode captures the typical El Nino Southern Oscillation (ENSO)
signature in the SST field, and also the related atmospheric teleconnection pattern
over the Pacific and North American sectors. By this, it is meant that the MCA mode
figure for the monthly anomalies of Z500 captures the Rossby wave train from the
tropical Pacific Ocean, propagating northwards to Greenland and the Arctic Ocean.
The Rossby wave is shown by the regions of positive and negative Z500 anomalies
originating from the tropical Pacific, propagating northwards over the American
sector and across to NE Canada and Greenland, into the Arctic Ocean (indicated by
the circles laid over figure 5.1). This analysis differs from Ding et al. [2014] as this
study concentrates on just the tropical Pacific Ocean, rather than the global tropical
Ocean regions, thus establishing the true link between this region and the Arctic, and
with the use of a different layer in the atmosphere (500hPa rather than 200hPa). The
first MCA mode results from Ding et al. [2014] explain 68% of the squared
covariance between Northern Hemisphere Z200 and the tropical Ocean SSTs; this
study therefore shows that by restricting the tropical region to just the tropical Pacific
Ocean, it explains a higher percentage of the covariance between the two datasets. A
way of extending the MCA study, in order to improve it, would be to carry out the
second mode of the MCA decomposition. This would enable the time series of each
field to be correlated to each other, which would allow analysis of the temperature
trend in the Arctic, with its relation to the NAO index, against the tropical SST
anomalies over both interannual and interdecadal timescales. From this, together with
model analysis, it would be possible to infer if the trends in the tropical SST are
responsible for and cause the negative trend in the NAO, which in turn influence
Arctic temperatures [Ding et al., 2014].
This study highlights that the movement of air (displayed in figure 5.1 of
MCA results) shows an arc-shaped trajectory of Rossby waves that link the tropical

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Pacific to the western North Atlantic Ocean and Greenland region. Just like ocean
currents, Rossby waves are large ribbons of fast-moving air masses caused by
meanders in high-altitude winds, i.e. the jet stream [Ding et al., 2014]. The poleward
projection of these Rossby waves connects the tropical Pacific to the high latitudes of
the Arctic region, and often creates atmospheric pockets of unusually warm or cold
air. Therefore, this study highlights that Rossby waves travelling to the Arctic
contribute to not only Arctic warming over Greenland and NE Canada, as suggested
by Ding et al. [2014], but they also contribute to Arctic-wide warming. This study
suggests that the tropical Pacific influences Arctic circulation, through propagation of
Rossby wave trains, rather than Arctic sea ice cover modifying the dynamical
atmospheric field to connect the Arctic to low-latitudes [Budikova, 2009; Li and
Wang, 2013]. Previous literature has explained that it is external forcing of Arctic
climate that has been the dominant factor in tropospheric warming in that region
[Perlwitz et al., 2015].
These findings are strongly correlated to the NAO index results highlighted
previously (section 5.4). This study expands on Ding et al. [2014] to show that annual
Arctic-wide warming is characteristic of the negative trend in the NAO. With the SST
pattern in the tropical Pacific being highly correlated to the NAO index (as shown by
Ding et al. [2014] and the CCA results here), this study suggests that the Rossby
waves originating in the tropical Pacific are driving this connection.
These results highlight the profound presence of a tropical Pacific
teleconnection to Northern Hemisphere Z500. To establish a causal link between
tropical Pacific SST and the Z500 pattern, numerical simulations using a state-of-the-
art atmospheric model, such as CESM1 CAM5, would be required.
5.6. Link of AMO to Arctic temperatures
Li et al. [2014a] explain that tropical and Northern Atlantic SST drives
Antarctic Peninsula warming, through the AMO altering the surface pressure in the
Amundsen Sea region of West Antarctica, with this in turn advecting warm air onto
the Peninsula and contributing to the 6K warming in this region. At time of writing,

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there is no similar study researching the potential teleconnection between tropical and
North Atlantic SST and Arctic climate. Due to the AMO driving the warming over the
Antarctic Peninsula, this study first carried out correlation coefficient analysis
between Arctic temperatures and the AMO index. The results show that significant
positive correlations (p 95% confidence level) exist between every Arctic station and
the AMO, thus highlighting the AMO could be having an influence on Arctic climate
like that over the Antarctic.
Li et al. [2014a] explains that the AMO manifests itself as an upward trend in
Atlantic SSTs, therefore giving it the potential to drive Arctic climate. Due to the
significant positive correlations between Arctic temperature and the AMO, this study
suggests that the tropical Atlantic SSTs could be related to Arctic climate.
5.7. Teleconnection of tropical and North Atlantic Ocean to Arctic climate
To establish if the teleconnection between the Atlantic Ocean and the Arctic is
significant, MCA decomposition was carried out. The responses of Northern
Hemisphere Z500 to tropical Atlantic SST show a 92% value of covariance, whereas,
with an MCA result of 85%, the Z500 response to the mid-latitude North Atlantic
SST forcing is weaker (see results figures 4.13 and 4.14). As in Li et al. [2014a] for
Antarctic-low latitude links, this implies that the tropical Atlantic has a primary role
in the teleconnection between Atlantic SST and Arctic circulation. Figure 4.13 (A)
shows negative Z500 anomalies over Greenland, and the rest of the Arctic, meaning
that warm air advection into the Arctic from lower latitude areas such as the North
American and Atlantic sectors occurs, contributing to the warming of Arctic air
temperatures. It should be noted, however, that it is not known whether the Z500
anomalies are significantly different from the norm. The MCA results highlight that
north Atlantic SST warming generates an impact on Arctic warming, albeit smaller
than that of the tropical Atlantic SSTs, suggesting that there could still be a significant
influence from the mid-latitude SSTs on Arctic climate. However, these analyses do
not account for air-ice-ocean interactions [Li et al., 2014a], so further study is
required in order to establish the importance of mid-latitude north Atlantic SSTs. A
study by Simpkins et al. [2014] examined the tropical connection to climate change in

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the high-latitude southern hemisphere, through the use of an atmospheric general
circulation model (AGCM). The only forcing from different global ocean regions that
replicated the geopotential height field seen in the reanalysis datasets is that from the
Atlantic. They found that increased Rossby waves from the Pacific ocean towards the
Antarctic come about by forcing from positive Atlantic SST trends, which in turn
influence the geopotential heights around the Antarctic Peninsula and West Antarctica
[Simpkins et al., 2014]. A similar idea could be taking place in relation to the Arctic-
tropics teleconnection highlighted in this study, where the positive trend in Atlantic
SST could be influencing the Rossby wave production in the tropical Pacific, which
would then influence Arctic climate.
These MCA decomposition results and analysis highlight the presence of a
tropical Atlantic teleconnection to Northern Hemisphere Z500. To establish a causal
link between Atlantic SSTs and the Arctic Z500 pattern in this study, numerical
simulations using a state-of-the-art atmospheric model, such as CESM1 CAM5,
would need to be carried out.

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6. Conclusion
This study demonstrates that the Arctic-low latitude tropical teleconnection is
present, on both annual and seasonal timescales, with results expanding on previous
studies, and also with new findings not previously known.
An atmospheric response is detected in the Arctic, following from SST
changes in the tropical Pacific. The concept of the tropical teleconnection was present
in the correlation of Nino SSTs and Arctic air temperatures. Seasonally, there were
differences in the polarity of the trend, with significant positive correlations in spring,
and negative in summer. Lagged correlations also highlighted the potential
teleconnection, with a peak in correlations at around the 4-6 month lag mark, thereby
indicating an influence on Arctic temperatures from tropical Pacific SSTs. The
composite analysis found an influence on Arctic air temperatures, due to ENSO
events in the tropical Pacific. Significant differences were found between Arctic
temperatures during an ENSO event, and not during an ENSO event. These findings
expanded upon those of Ding et al. [2014], through the use of the whole of the Arctic
for the climatic data, rather than just Greenland and NE Canada; Arctic-wide
temperature warming is linked to changes in the tropical Pacific SST field, across
seasonal and annual timescales.
New findings were discovered with regards to the ENSO-NAM
teleconnection. Correlation results highlighted that El Nino SSTs do not significantly
correlate with the NAM index values, at any of the time lags, with a similar picture
for the La Nina SST and NAM correlations. T-tests results found a significant
difference in NAM values during El Nino and La Nina events however; a negative
NAM index is associated with El Nino events, and a positive NAM is associated with
La Nina events. With the insignificant correlations, together with significant t-test
results, this suggests a non-linear relationship between the NAM index and ENSO
SST. Geopotential height (Z500) anomaly plots clearly highlighted a teleconnection
between the tropical Pacific and the Arctic. Due to SST anomalies in the tropical
Pacific during El Nino events, but not for La Nina events, a Rossby wave train of
Z500 anomalies exist that propagate to the Arctic. The Rossby wave of Z500

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anomalies is thought to then affect the air advection patterns into the Arctic, and
contribute to Arctic warming. These findings are new, and also expand upon the ideas
of Ding et al. [2014] by stating that there is a clear Arctic-low latitude teleconnection,
but use a different technique of composite analysis, like that of Fogt et al. [2011] for
the Antarctic-low latitude teleconnection. A teleconnection was also found in the
Z500 anomaly plots that suggest a potential Arctic-Indian Ocean teleconnection, in
boreal summer, when an El Nino coincides with a negative NAM index. The literature
reinforces the proposition that a warming Indian Ocean can affect the El Nino
strength, in turn affecting the Arctic. With Li and Chen [2014] explaining that the
Antarctic polar vortex is influenced by Indian Ocean warming, perhaps this new
finding suggests a similar link to the Arctic.
As Ding et al. [2014] explain that recent Greenland and NE Canada warming
is strongly linked with a negative trend in the NAO, this study expanded on such
findings, and established that Arctic-wide warming is linked to the negative NAO.
Seasonal correlations were also carried out, to expand upon Ding et al. [2014], and
found that Greenland and NE Canada warming was significantly correlated to a
negative NAO for each season, with a more varied picture elsewhere across the Arctic
and much less significant correlations. The effect of the meandering jet stream, during
a negative NAO, clearly does not influence all parts of the Arctic across different
seasons.
The MCA and CCA decomposition results, between the tropical Pacific and
the Arctic, expand upon Ding et al. [2014] and highlight the true nature of the
teleconnection, by concentrating on just the tropical Pacific Ocean rather than the
global tropics. The results clearly indicate that a tropical Pacific-Arctic teleconnection
exists through the affect of Rossby wave propagation.
New findings were highlighted with regards to the Atlantic link to Arctic
climate. Firstly, following the Atlantic-Antarctic link found in Li et al. [2014a], this
study highlighted that the AMO correlated significantly to Arctic air temperatures,
suggesting that the AMO could be driving Arctic warming, due to the AMO
manifesting itself as an upward trend in Atlantic SSTs [Li et al., 2014a]. The
influence of the tropical Atlantic on the Arctic is significant, and is greater than that

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of the North Atlantic, and implies that the tropical Atlantic could have a primary role
in the possible teleconnection between Atlantic SST and Arctic atmospheric
circulation.
There is ample scope for further development of this study. If numerical
model simulations were used, following from the MCA decomposition and NAM-
ENSO teleconnection, the findings could be established to be causal or not with
regards to forcing Arctic warming. Due to analysis not accounting for air-ice-ocean
interactions, further study is required in order to establish the importance of Atlantic
and Pacific SSTs in forcing Arctic climate. Further analysis of the potential Arctic-
Indian Ocean teleconnection is important, in order to establish if this link is
significant.