ArticlePDF Available

Major Climate Variability and Natural Factors in Boreal Winter

Authors:

Abstract and Figures

The role of natural factors, mainly solar 11-year cyclic variability and volcanic eruptions on two major modes of climate variability the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) are studied for about the last 150 years period. The NAO is the primary factor to regulate Central England Temperature (CET) during winter throughout the period, though NAO is impacted differently by other factors in various time periods. Solar variability during 1978–1997 indicates a strong positive in-phase connection with NAO, which is different in the period prior to that. Such connections were further explored by known existing mechanisms. Solar NAO lagged relationship is also shown not unequivocally maintained but sensitive to the chosen times of reference. It thus points towards the previously known mechanism/relationship related to the Sun and NAO. This study discussed the important roles played by ENSO on global temperature; while ENSO is influenced strongly by solar variability and volcanic eruptions in certain periods. A strong negative association between the Sun and ENSO is observed before the 1950s, which is positive though statistically insignificant during the second half of the twentieth century. The period 1978–1997, when two strong eruptions coincided with active years of strong solar cycles, the ENSO and volcano suggested a stronger association. That period showed warming in the central tropical Pacific while cooling in the North Atlantic with reference to various other anomaly periods. It indicates that the mean atmospheric state is important for understanding the connection between solar variability, the NAO and ENSO and associated mechanisms. It presents critical analyses to improve knowledge about major modes of variability and their roles in climate and reconciles various contradictory findings. It discusses the importance of detecting solar signal which needs to be robust too.
Content may be subject to copyright.
Major Climate Variability and Natural Factors in Boreal Winter
INDRANI ROY
1
Abstract—The role of natural factors, mainly solar 11-year
cyclic variability and volcanic eruptions on two major modes of
climate variability the North Atlantic Oscillation (NAO) and El
Nin
˜o Southern Oscillation (ENSO) are studied for about the last
150 years period. The NAO is the primary factor to regulate
Central England Temperature (CET) during winter throughout the
period, though NAO is impacted differently by other factors in
various time periods. Solar variability during 1978–1997 indicates
a strong positive in-phase connection with NAO, which is different
in the period prior to that. Such connections were further explored
by known existing mechanisms. Solar NAO lagged relationship is
also shown not unequivocally maintained but sensitive to the
chosen times of reference. It thus points towards the previously
known mechanism/relationship related to the Sun and NAO. This
study discussed the important roles played by ENSO on global
temperature; while ENSO is influenced strongly by solar variability
and volcanic eruptions in certain periods. A strong negative asso-
ciation between the Sun and ENSO is observed before the 1950s,
which is positive though statistically insignificant during the sec-
ond half of the twentieth century. The period 1978–1997, when two
strong eruptions coincided with active years of strong solar cycles,
the ENSO and volcano suggested a stronger association. That
period showed warming in the central tropical Pacific while cooling
in the North Atlantic with reference to various other anomaly
periods. It indicates that the mean atmospheric state is important
for understanding the connection between solar variability, the
NAO and ENSO and associated mechanisms. It presents critical
analyses to improve knowledge about major modes of variability
and their roles in climate and reconciles various contradictory
findings. It discusses the importance of detecting solar signal which
needs to be robust too.
Keywords: Solar cyclic variability, NAO, ENSO, volcanic
eruptions, multiple linear regression.
Abbreviations
AOD Aerosol optical depth
CET Central England Temperature
CMIP5 Coupled Model Inter-comparison Project 5
ENSO El Nin
˜o Southern Oscillation
EOF Empirical Orthogonal Function
GCR Galactic cosmic ray
NAO North Atlantic Oscillation
NH Northern hemisphere
RCP Representative concentration pathway
SH Southern hemisphere
SSN Sunspot number
SST Sea surface temperature
SLP Sea level pressure
UV Ultra violet
1. Introduction
The Sun is the main source of energy of the earth,
but the level of scientific understanding relating to its
influences on climate is still low (IPCC 2013).
Regarding energy output, there is only a 0.1% change
between maximum to minimum of the solar 11-year
cycle (Lean and Rind 2001), which is too negligible
to influence climate. However, studies identified
significant regional impacts which are felt seasonally
(Gray et al. 2010; Roy and Haigh 2010; van Loon
et al. 2007) and sometimes depend on the overall
period chosen (Roy and Haigh 2012; Roy 2014). In
understanding climate variability and in interpreting
signals of climate change, it is important to ascertain
the actual role of natural factors, so that any human
influence may be more accurately identified. More
caution is also required to detect signals relating to
natural variability (mainly the Sun) and the robust-
ness of identified signature need to be tested from a
critical viewpoint.
Nowadays, there is general agreement that the
direct effect of the changes in the ultraviolet (UV)
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s00024-020-02522-z) contains sup-
plementary material, which is available to authorized users.
1
IRDR, University College London (UCL), London, UK.
E-mail: indrani.roy@ucl.ac.uk
Pure Appl. Geophys. 177 (2020), 4983–5005
Ó2020 The Author(s)
https://doi.org/10.1007/s00024-020-02522-z Pure and Applied Geophysics
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
part of the spectrum (6–8% between maxima and
minima years of the 11-year cycle) leads to more
ozone and warming in the upper stratosphere in solar
maxima (Gray et al. 2010). The variation of ozone,
through absorption of solar UV spectrum around the
upper stratosphere, is one responsible factor for
controlling temperature gradient between summer
and winter hemisphere. Subsequently, through the
well-known thermal wind balance variability, the
solar cycle can regulate the strength of polar vortex,
suggesting a stronger stratospheric polar jet in active
solar years and vice versa. Baldwin and Dunkerton
(2001) showed perturbation in the polar vortex can
affect the polar troposphere during the same season.
Kodera and Kuroda (2002) proposed a mechanism,
whereby active solar years have the potential to
influence the polar vortex and subsequently down to
the lower tropical stratosphere, which also involves
upward propagating planetary waves. The solar sig-
nal from the lower tropical stratosphere was shown to
impact troposphere by modulating Hadley circulation
and Ferrel cell (Haigh 1996; Haigh et al. 2005). All
these mechanisms, by which, solar variability that
affects the stratosphere are transported downwards
and subsequently influences tropospheric climate is
known as solar ‘Top-Down’ mechanism. Solar
‘Bottom-Up mechanisms are also proposed (Meehl
et al. 2008,2009) those involve Sea Surface Tem-
perature (SST) of the Pacific Ocean to influence
tropospheric climate. In tropospheric climate, global
temperature is an important atmospheric variable
which was investigated by Lockwood and Froehlich
(2007) in terms of solar variability. They concluded
that the rise in global temperature since the last two
decades of the twentieth century correlates poorly
with solar variability although it suggests to the
contrary during periods prior to that.
Ineson et al. (2011) analysed the regional impact
of the solar 11-year cycle on North Atlantic Oscil-
lation (NAO) (the dominant mode of climate
variability during boreal winter around the North
Atlantic) by using UK Met Office Unified Model and
found an in-phase relationship. They imposed a vast
change in solar UV irradiance to produce their
observed response. According to them, in years of
low (high) UV activity, easterly (westerly) winds and
cold (warm) winters are favoured to northern Europe
indicating a negative (positive) NAO pattern. It is
interesting to study the robustness of such a proposed
association between the Sun and NAO (Ineson et al.
2011). One of the focuses of the current study is to
explore that area using observational data. Modelling
work of Sjolte et al. (2018) discussed that the solar
NAO link is not unequivocally supported by recon-
structions. Apart from zero lag case, recent studies
also explored the connection between solar 11-year
cycle and winter NAO on various lag scales. Those
include observational (Gray et al. 2013) as well as
Modelling works (Andrews et al. 2015; Thieblemont
et al. 2015; Scaife et al. 2013). The current study will
also investigate the robustness of such association.
Apart from variations in the Sun, another major
natural variability is volcanic eruptions (discussed in
detail by Robock 2003). The radiative effect of a
volcano is global cooling irrespective of the period
considered, but its actual influence around continents
of the Northern Hemisphere (NH) suggests winter
warming (Robock and Mao 1992) and needs atten-
tion. Using reanalysis data of the twentieth Century
version 2 (20CRv2), (Compo et al. 2011), a signifi-
cant surface warming over northern Europe and Asia
was detected by Driscoll et al. (2012). Results from
Coupled Model Inter-comparison Project 5 (CMIP5)
simulations (Driscoll et al. 2012), concluded that the
models fail to capture the NH dynamical response
following eruptions. The effect of volcanos on NAO
was also investigated (Sjolte et al. 2018) which
indicated a positive NAO pattern during the follow-
ing winter. However, they showed past major
explosive eruptions had more lasting effects during
the instrumental era. More studies are required to
explore the connection/mechanism relating to vol-
cano and NAO.
Volcanic aerosols also have the potential to
change stratospheric chemistry with most necessary
chemical changes in the stratosphere related to ozone
(Robock 2003). After 1991 Pinatubo eruption, ozone
column reduction of about 5% was noticed in
between mid-latitudes of both the NH and Southern
Hemisphere (SH). Considering ozone depletion in the
aerosol cloud, it was much larger (20%). Thus, if
intense volcano and atmospheric dynamics come
together with the right timing, they could reinforce
one another with different drastic results. During
4984 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1978–1997, the two strong eruptions (El Chicho
´n and
Pinatubo) coincided with the near peak years of very
active solar cycles. Knowing that the ozone in the
stratosphere is modulated by solar cycles and explo-
sive volcanos, it is interesting to study the role of the
last two eruptions, considering the phases of solar
cycles. This study shows that separating the period
‘1978–1997’ is important to better understand some
climate features. This was pointed out earlier (Roy
2016 [version 1, version 2]) and is supported by
recent studies (Polvani et al. 2017; Oliva et al. 2017).
El Nin
˜o Southern Oscillation (ENSO) is a major
climate phenomenon of the atmosphere and ocean
and through different teleconnections, affects almost
all parts of the globe. Various ENSO index time
series is formulated, based on slightly different geo-
graphical considerations of tropical Pacific SST. The
most widely used one is the Nin
˜o 3.4 Index, which
has geographic coverage of (5°N–5°S, 170°W–120°
W) and is used in this study.
Growing body of evidence suggests the low-fre-
quency variability of ENSO is primarily modulated
by decadal variability originated in the North Pacific
(Gu and Philander 1997; Latif et al. 1997). The
decadal mode of ENSO might be related to solar
11-year cycle. Applying Empirical Orthogonal
Function (EOF) technique on SST, White et al.
(1997, in their Fig. 6, Top) using data from the latter
period of the twentieth century showed that tropical
Pacific SSTs resemble positive phase of the ENSO
and is also in phase with the 11-year solar cycle.
Using the Method of Solar Maximum Compositing,
van Loon et al. (2007), and Meehl et al. (2008)
detected an enormous negative signature in tropical
Pacific SST for 150 years period, similar to that of
the negative phase of ENSO. However, using the
same dataset, over a similar time period, Roy and
Haigh (2010) could not detect negative solar signal
around tropical Pacific using Regression Technique.
Haam and Tung (2012), Roy and Haigh (2010,2012)
and Roy (2010,2014) addressed those contradictory
findings in detail and reconciled some of the contra-
dictions and discussed few mechanisms. Studies still
suggest that solar-ENSO behaviour is a major area of
dispute in climate science. More critical analyses and
thorough investigations are vital to understanding the
exact nature of those connections and its implication
to global-scale climate responses.
Emily-Geay et al. (2008) showed, regardless of
solar forcing, explosive volcanos with a radiative
forcing greater than 3.3–4 Wm
-2
(roughly the mag-
nitude of the El Chicho
´n and Pinatubo eruptions),
noticeably influence the modelled ENSO. Those can
even significantly raise the likelihood of an El Nin
˜o
event above the model’s internal variability level.
The response of ENSO from explosive volcanism
was studied by Adams et al. (2003) by using two
different paleoclimate reconstructions and two inde-
pendent, proxy-based chronologies from AD 1649.
They found a significant, multi-year, El Nin
˜o-like
response over the past several centuries and nearly
twice the probability of an El Nin
˜o occurrence in the
winter following a volcano. Ohba et al. (2013) also
studied the effect of massive eruptions in the Model
for Interdisciplinary Research on Climate (MIROC5).
It suggested about excitation of the anomalous west
Pacific westerly which subsequently causes an
increase in the probability of El Nin
˜o that agrees with
observational data of longer-term paleoclimate
records (Adams et al. 2003; McGregor and Tim-
mermann 2011). A similar response is also noticed by
Stenchikov et al. (2009) that used the Geophysical
Fluid Dynamics Laboratory (GFDL) CM2.1 model.
The model result of Ohba et al. (2013) suggests that
explosive volcanos during El Nin
˜o phase contribute
to the duration of El Nin
˜o, whereas the same during
La Nin
˜a counteract to its duration, shortening its
period. Using targeted climate model simulations,
Khodri et al. (2017) further explored the effect of
Pinatubo-like strong eruptions and showed those
indeed favour an El Nin
˜o-like response. The effect of
strong volcanos on El Nin
˜o is more substantial than
that in La Nin
˜a due to the amplification by the air-sea
coupled feedback (Stenchikov et al. 2009; Khodri
et al. 2017). All these indicate studies relating to the
effect of explosive volcanism on ENSO needs further
exploration.
The ENSO plays a key role in regulating global
temperature. The warming of the tropical Pacific
from 1990 to mid-1995 was unprecedented in the
observational record for more than a century (Tren-
berth and Hore 1996). Wang and Fiedler (2006)
discussed that there was a failure to recognize the
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4985
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1982–1983 El Nin
˜o (the strongest one over a hun-
dred-year period) until it was well developed. They
also discussed the unusual nature of warm events of
ENSO during 1990–1995 in the context of an
observed trend for fewer La Nin
˜a and more El Nin
˜o
after the late 1970s. The mean SO index for the post-
1976 period is statistically different (\0.05%) from
the overall mean and it is also true for the period
1990–mid-1995 (Trenberth and Hoar 1997). There is
a good correspondence between the ENSO and tem-
perature of the troposphere (Sobel et al. 2002), with
warm periods coincide with El Nin
˜o’s whereas, the
cold with La Nin
˜a’s. At the warm phase of ENSO, the
troposphere is capable of carrying more water
vapour. In a cloudless sky, water vapour is the most
important greenhouse gas that constitutes 60% of
total radiative forcing (Kiehl and Trenberth 1997).
Ranked by their direct contribution, the largest
greenhouse gas compounds are water vapour, and
clouds (36–72%) which have far higher contribution
than that of CO
2
(9–26%)
1
. Using satellite data,
Laken et al. (2012) showed that ENSO is also the
most accountable factor for changes in cloud cover.
All these studies indicate that the role of water vapour
and cloud, associated with the ENSO, has a vital role
in regulating global temperature and needs attention.
Interestingly, the SST trend along the equator in
the final 50 years of the twentieth century shows an
El Nin
˜o-like pattern which is robust for all observa-
tions even using different datasets. However, there
are controversies for the trend pattern of the long-
time historical SST observations, as it differs sub-
stantially among datasets and time periods (Liu et al.
2005). Qiong et al. (2008) showed the standard
deviation of Nin
˜o-3 SST increased to around
0.9–1.0 °C
1
during the last two decades of the
twentieth century and showed a nearly 50–60% rise
in the ENSO variability (significant at the 95% con-
fidence level). Though the decadal variation of the
tropical climate has a considerable contribution to the
ENSO variability (Fedorov and Philander 2000), the
amplitude of the ENSO variability still suggested a
robust, rising trend during that period, even removing
that decadal contribution (Zhang et al. 2008).
Using observational data, this paper addresses
many of these issues involving various modes of
climate variability. It reconciles some of the contra-
dictory findings and presents a critical viewpoint to
advance our understanding relating to major modes of
variability and their role in climate. It will help
improve understanding of various climate modes and
related teleconnections; which can subsequently lead
to improved future prediction skill.
The structure of this study is as follows. Section 2
discusses the Methodology and Data. Results are
detailed in Sect. 3and have four sub-sections. The
Sect. 3.1 covers analyses using time series of several
factors. The first part discusses the general behaviour
of different indices and their combined effect; while
the second part focuses on the results of regression
using various indices. Section 3.2 examines data to
identify the spatial signature. Few questions on Sun
NAO lagged relationship is raised in Sect. 3.3.
Results are summarised in Sect. 4.
2. Methodology and Data
The method used here is the method of Multiple
Linear Regression (MLR) analysis with AR (1) noise
model. Noise coefficients are calculated simultane-
ously with the components of variability such as the
residual matches with a red noise model order one.
Following this methodology, it is possible to min-
imise noise being interpreted as a signal. The method
is discussed in detail by Roy and Haigh (2010), Roy
and Haigh (2010), Roy (2014,2018a) and previously
used by many other studies (Gray et al. 2010,2013
among others).
Variables and climate indices used are Sea Level
Pressure (SLP), SST, monthly Sun Spot Number
(SSN), Nin
˜o3.4, Stratospheric Aerosol Optical Depth
(AOD), (indicative of volcanic eruptions), longer-
term linear trend (to represent anthropogenic influ-
ence), Central England Temperature (CET) and
NAO. In the spatial plots of MLR analyses, SLP is
the dependent parameter, while independent param-
eters are SSN, Nin
˜o3.4, AOD, and linear trend. For
the solar signature (spatial patterns), zero lag case, as
well as various lag situations, i.e., lag 1–3 (Gray et al.
2013), are also considered. For zero lag case, the
1
https://en.wikipedia.org/wiki/Greenhouse_gas.
4986 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
MLR technique is also applied on CET, NAO and
ENSO.
For SLP, the in-filled HadSLP2 dataset from
Allan and Ansell (2006), that covers the whole globe
and available as monthly means from 1850 to 2004
are used. It can also be found from https://www.
metoffice.gov.uk/hadobs/hadslp2. Unlike HadSLP1,
error estimates are mentioned for HadSLP2, to have
ideas about regions of low confidence. Measurement
and sampling errors are large in the high southern
latitude due to the lesser number of observations,
though it is small in other areas. The estimates lie
between the observational error estimates of Kent and
Taylor (1997) of 2.3 ±0.2 hPa and those of Ingleby
(2001) of 1 hPa over most of the ocean basins.
HadSLP2 data has been updated later upto 2012 using
HadSLP2r_lowvar data (https://www.metoffice.gov.
uk/hadobs/hadslp2/data/download.html). It is a ver-
sion of HadSLP2r and consistent with HadSLP2. I
have also extended the analyses for SLP upto 2012
using HadSLP2r_lowvar data (like Roy and Kripalani
2019), but the main findings did not change. In the
current analysis for SLP, I discussed results only upto
2004, that solely used the HadSLP2 data.
For SST, NOAA extended SST v4 (ERSST) data
is used (Liu et al. 2014), which is available from 1854
till date in a 2°92°latitude and longitude grid. It is
a revised version of ERSST v3b and much improved
one. The data quality is more reliable and consistent
after 1880 and we considered that period. It is
available from NOAA, Boulder, from their Web site
at https://www.esrl.noaa.gov/psd/ andalsofromhttps://
www.ncdc.noaa.gov/data-access/marineocean-data/
extended-reconstructed-sea-surface-temperature-
ersst-v4/. Quality of data and associated quantifi-
cation of uncertainty is discussed in detail in the
literature (Huang, et al. 2015). In this study, the
global surface air temperature is also analysed,
which is NOAA, Goddard Institute for Space
Studies (GISS) Land and Ocean Temperature data
from https://www.ncdc.noaa.gov/cag/global/time-
series. It is available from 1880. Uncertainty
quantification of this data is discussed in Lenssen
et al. (2019). To verify the result with another
global temperature data, Climate Research Unit
(CRU) temperature records are consulted which is
available since 1850. It can be downloaded from
https://www.metoffice.gov.uk/hadobs/hadcrut4/data/
4.4.0.0/time_series/HadCRUT.4.4.0.0.monthly_ns_
avg.txt. Sampling error, coverage uncertainty and
other details are described in Morice et al (2012).
SSN is used to represent solar cyclic variability,
which is available from https://www.sidc.be/silso/
versionarchive (version 1). The main advantage of
using SSN is that it is free from the influence of
trends and only captures the cyclic variability of the
Sun. SSN is strongly correlated with various solar
related parameters e.g., UV variability, visible solar
irradiance and solar F10.7 [which is solar flux that
can be measured in the ground and have bandwidth of
10.7 cm (2800 MHz)], while anti-correlated with
Galactic Cosmic Ray (GCR) and hence can be con-
sidered as a proxy for all those indices. It is the most
commonly used solar index for analysing long-term
climate data. Moreover, the eleven-year cyclic nature
of SSN can also be a very useful feature for future
prediction purposes.
For ENSO, Nin
˜o 3.4 index, obtained from Kaplan
et al. (1998) is used which is available since 1856 and
can also be found at https://climexp.knmi.nl. In the
regression, AOD has been employed to represent
volcanic eruptions and available from https://data.
giss.nasa.gov/modelforce/strataer/. It is also found
from KNMI Climate Explorer (https://climexp.knmi.
nl). Longer term trend is a rising linear line that
represents the increasing anthropogenic influence of
the twentieth century. In this analysis, NAO index
from Climate Research Unit (CRU), University of
East Anglia is used which is accessible since 1823. It
is developed by Jones et al. (1997) that considered
instrumental pressure observations from Gibraltar
and southwest Iceland and also available from https://
www.cru.uea.ac.uk/*timo/projpages/nao_update.
htm. The CET data is the longest instrumental record
of temperature in the world and described in various
publications (Manley 1974; Parker et al. 1992; Parker
and Horton 2005). Being historic in Nature, the
quality and reliability of this dataset is believed to be
of high standard; though more reliable for the period
of our analyses (since 1856). It is available since
1659 and can be downloaded from https://www.
metoffice.gov.uk/hadobs/hadcet/data/download.html.
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4987
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3. Results
3.1. Analyses Using Mainly Time Series of Various
Factors
3.1.1 The General Behaviour of Different Indices
and Their Combined Effect
Figure 1is the time series (DJF) plot of various
parameters. Figure 1a shows global surface air tem-
perature anomalies and Fig. 1b–e shows time series
of various independent factors those are likely to
influence that temperature. Figure 1b is the time
series of AOD; Fig. 1c, d are solar eleven-year cyclic
variability (here SSN) and Nin
˜o3.4 index (represent-
ing ENSO), respectively. Figure 1e is the linear trend
that represents longer-term climate change and
mainly arises out of anthropogenic influence.
Due to a large specific heat of the ocean, it can
retain heat for a longer time scale and thus the long-
term variation of solar output is captured and detected
mainly in the oceans. The Atlantic Multi-Decadal
Oscillation (AMO) and Pacific Decadal Oscillation
(PDO) are two primary modes of variability in the
Northern Hemisphere (NH) which are generated due
to the atmosphere and ocean coupling and substan-
tially can also modulate global temperature. In the
current study, the primary interest, however, is on the
role of climate variability on time scales of decades
and less and thus, PDO and AMO, which have a
longer-term variability of *20–30 years are not
included. Moreover, those modes are not considered
as an independent variable, rather they are the
regional manifestation of longer-term SST
variability.
Here is a brief discussion on global surface air
temperature variation as it corresponds to Fig. 1a.
Three phases (I, II and III) are marked (Fig. 1) based
on various combinations of the strength of volcanic
activity and SSN that also follow the variation of
temperature. Period (I) suggests a cooling in temper-
ature (1880–1917); period (II) suggests a rise
(1917–1944); whereas, period (III) shows an abrupt
increase in temperature (1978–1997).
The Sun and the volcano: During the latter half of
the twentieth century, the period dominated by the
sudden rise in global temperature, the solar cycles are
also seen to be stronger as noticed in Fig. 1c. In
addition, three major volcanos erupted during 1963,
1982 and 1991 and the last two even coincided with
the near peak of active solar cycles. Though massive
volcanic eruptions occurred in solar peaks are a pure
coincidence, but if it happens, it can change the mean
state of stratosphere as well as troposphere. Subse-
quently, it may alter the strength and nature of solar
so-called ‘Top-Down’ mechanism. Moreover, the
ocean–atmosphere coupling system is also disturbed.
Thus, both the direct (radiative) and indirect (dy-
namical) effects of the sun need to be considered to
judge its contribution to surface climate.
The late 1800s to the early 1900s (period ‘I’) was
a very active period of volcanic eruptions and those
also coincided with minimum/near minimum years of
solar 11-year cycles. Together with a quiet Sun
(Fig. 1c), period ‘I’ was cold as seen in Fig. 1a.
While the period from 1917 to 1944 (marked by ‘II’)
was a period of very little volcanic activity that
coincided with an increase in solar irradiance. It
experienced a rise in global temperature. However,
the primary focus here is the last two decades of the
twentieth century, when there is a steep rise in global
temperature (Fig. 1a, period III) and its relevance to
natural variability. There was an abrupt increase in
temperature during 1978–1997 (IPCC 2013). It
coincidentally happened when two very active vol-
canos erupted during the near peak of active solar
cycles.
To address more on global temperature variation
under different combinations of volcano and Sun, I
also analysed CRU global temperature data (Fig. S1).
The results remain the same adding the confidence to
our observation. Three periods (1860–1880,
1917–1944, 1978–1997) are marked with three
coloured demarcating lines (black, blue and red
respectively), when a rising trend of global temper-
ature is noticed. Due to the availability of additional
data of past periods in CRU, it is also possible to
compare the period ‘1860–1880’ with ‘1917–1944’ in
Fig. S1a–c. Both the periods noted a rise in global
temperature, absence of major volcanic activity and
small to moderate solar cycles. For ENSO, we did not
notice many changes and hence not included in
Fig.S1.
4988 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
We speculate that not only the strength of
eruption and the power of solar cycle necessary but
also their combined behaviour that includes the
timing of eruption relating to the phase of solar cycle
are important in regulating the climate. It is interest-
ing to investigate the role played by major variability
(the Sun and volcano) individually and in combina-
tion, considering the dynamical and radiative
influences with attention.
The role of ENSO: Figure 1d shows the time
series of ENSO, which has an inter-annual variability
of 2–7 years. It is usually of 2–3 years cycle during
Figure 1
Time series of various independent parameters (be), those affect global surface air temperature (a). aGlobal surface air temperature
anomalies (DJF), which is GISS Land Ocean temperature data, available since 1880. beTime series (DJF) of Stratospheric Aerosol Optical
Depth (AOD) (b), Sunspot number (SSN) (c), ENSO, represented by Nin
˜o3.4 Index (°C) (d), and normalised linear trend (e). Three periods (I,
II and III) are marked based on various combinations of strength of volcanic activity and solar irradiance: (I) cooling of temperature
(1880–1917); (II) rise in (1917–1944, marked by red boundaries) and (III) abrupt rise in temperature (1978–1997, marked by blue boundaries)
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4989
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
the overall analyses, apart from the latter period of
the twentieth century when it even reached 5–7 years
cycle. Period III is also seen dominated by warm
events of ENSO. Thus, ENSO showed some differ-
ences in period III and discussed below.
Using 5 months running mean of Nin
˜o3.4 SST
index, to represent ENSO, Trenberth and Hoar (1997,
in their Fig. 1) clearly identified the highest ENSO
signal during 1982 and that with the longest duration
during 1992. They mentioned that from March 1991
to March 1995, the average ENSO did not change
sign suggesting those peaks were clearly linked.
Those features of ENSO in 1982 and 1991 are also
evident in this work in Fig. 1d. Last two decades of
twentieth century, experienced nearly 50–60%
increase in the ENSO variability (Qiong et al.
2008). Figure 1d agrees with such observations and
supports Adam et al. (2003) and Ohba et al. (2013),
that indicates volcanic forcing drives the coupled
ocean–atmosphere system more subtly towards a
preferential direction, where multi-year El Nin
˜o-like
situations are favoured, which is subsequently fol-
lowed by a weaker rebound into a La Nin
˜a-like
condition. Now the number of El Nin
˜o outnumbers to
that of La Nin
˜a during 1978–1997 with a significant
rise of its variability and duration (Qiong et al. 2008;
Trenberth and Hore 1996). It thus explains why SST
trend along the equator in the final 50 years of the
twentieth century shows a robust El Nin
˜o-like pattern
for all observations using different datasets (Liu et al.
2005).
Water vapour is the most important greenhouse
gas that constitutes 60% of total radiative forcing
(Kiehl and Trenberth 1997). Moreover, note that
there are fewer La Nin
˜a and more El Nin
˜o events
after the late 1970s (period III). Thus, the rise in
global temperature during that period and the differ-
ent behaviour of El Nin
˜o that includes increase in
amplitudes and time period (Trenberth and Hoar
1997; Trenberth and Hore 1996; Wang and Fiedler
2006) could be one interesting area which needs to be
revisited.
The overall analyses indicate it is important to
study the combined effect of ENSO, volcanic erup-
tions, and solar variability. In addition to their
individual influences, such combination is vital to
understand the overall climate impact regionally as
well as globally.
Figure 2shows the anomaly of SST during
1978–1997 (where two explosive volcanos erupted
in active solar phases) to that from two different
periods 1920–1940 and 1999–2017, respectively
(when there are no massive volcanos). Those two
anomaly periods (1920–1940, 1999–2017) are arbi-
trarily chosen and a slight change of years does not
make any difference to the main result. Surprisingly
an El Nin
˜o like pattern is noticed in both plots where
central tropical Pacific SST has risen by around
0.4–0.8 °C during 1978–1997. On the contrary, over
the same period, there is a cooling around North
Atlantic where SST cooled even more than 1 °C. It is
interesting to note the uniformity despite two differ-
ent reference periods (one is even the very recent
time). A similar detailed analysis is also carried by
the author in recent research (Roy 2018b). In spite of
the presence of well-discussed longer-term global
warming trend throughout the period, central tropical
Pacific and North Atlantic showed deviations. Con-
sidering various arbitrary reference periods (Fig. S2),
similarly signed signals are still noticed during
1978–1997, which is warm in the central tropical
Pacific, but cold in the North Atlantic. This period
(1978–1997) only differs from rest other reference
periods regarding explosive volcanos matching with
high years of active solar cycles. Hence in subsequent
analyses we focus on those two regions and focus on
ENSO and NAO, considering solar variability and
volcanos.
3.1.2 Results of Regression Using Various Indices
This section presents discussion based on MLR
technique. MLR technique is applied to three differ-
ent parameters CET, NAO and ENSO in three
subsequent columns (1, 2 and 3) and presented in
Table 1. The first column (1) shows the value of
regression coefficients using CET as the dependent
parameter, with the independent factors as the NAO,
SSN, AOD, ENSO, and trend. The second column (2)
is the result due to SSN, AOD, ENSO and trend as
independent parameters with NAO being the depen-
dent factor. Whereas, the third column (3) calculates
regression coefficients for ENSO as the dependent
4990 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Figure 2
SST anomaly (DJF, in 8C) from NOAA Extended SST V4 (ERSST), top (a) for 1978–1997 minus 1999–2017, and bottom (b) 1978–1997
minus 1920–1940. Positive anomalies are shown by green, yellow and red colours and negative by blue. Zero contours are also labelled.
SST anomaly higher than 0.5 8C are usually significant at 95% level
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4991
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
variable with SSN, AOD, and trend as independent
factors. In Table 1, I also considered different time
periods for various reasons as follows:
Period ‘1958–1997’: The period from the 1950s–
1997s was identified by several authors (e.g. Vecchi
and Soden 2007) as the period of a weakening of both
the Walker and Hadley circulations; more in the
Walker than the Hadley circulation. On the other
hand, over the same time, the shallow ocean Merid-
ional Overturning Circulation in the Tropical Pacific
was also weakened (Zhang and McPhaden 2006),
suggesting that both the atmosphere and the ocean
system was in an anomalous state. Hence, I separated
that period (1958–1997) from (1856–1997) to find the
influence of various factors.
Period ‘1978–1997’: Numerous studies indicated
the year 1976/1977 as climatic ‘regime shift’ (Miller
et al. 1994; Meehl and Teng 2014) because many
physical conditions in the atmosphere and ocean
including global temperature changed abruptly during
that period. Scafetta (2013) proposed that it could be
due to a constructive interference among a number of
natural oscillations. Substantial evidence also sup-
ports the idea that physical conditions changed
around atmosphere (Minobe 2000; Bond et al.
2003) and ocean (McPhaden and Zhang 2004; Vecchi
and Soden 2007) during 1998. Moreover, the last two
very active volcanos erupted during that intervening
period (1978–1997) coinciding the active phase of
strong solar cycles. Hence, those two decades are also
separated.
In Table 1, period ‘A’ is for the entire period of
consideration up to 1997 which is (1856–1997);
whereas, ‘B’ only considers the time before 1957; ‘C’
is after 1958, while period ‘D’ focuses the time
1978–1997. There could be issues related to lesser
data points for the period ‘D’; hence we also repeated
the analysis of period D up to 2004 covering 26 years
period- but the main findings did not change. In
addition to that, results for period ‘A’ and ‘C’ were
also extended upto 2004 (not shown here, as main
findings remain the same). The primary purpose of
depicting Table 1is to show that period D
(1978–1997) is different from the rest other periods
under consideration. Hence results only upto end
period of 1997 is presented. Results of significance in
different degrees (90%, 95%, and 99%) are marked
by various symbols (*, ** and *** respectively).
However, major results are based only on significant
levels of 95% and 99%.
Influence on CET (column 1): In column ‘1’ of
Table 1, it is clearly seen that the NAO explains most
of the variance for CET and the result is 99%
significant in all the periods (A–D). Interestingly,
during period D, volcano suggests cooling (95%
significant) in central England, while linear trend
indicates warming (90% significant). Irrespective of
time periods considered, NAO plays a dominant role
in regulating the temperature of England. Though the
influence of NAO on CET is robust, NAO is seen to
be influenced by other independent factors differently
during different periods considered. Time series plot
of NAO and CET in also presented in Fig. S1d, e.
Influence on NAO (column 2): Identifying the
importance of NAO in controlling the temperature of
Europe, Central England being one of the represen-
tative regions, it is now interesting to identify how
Table 1
Regression coefficients for various indices during DJF in different
time periods
Independent
parameters
Dependent parameters
(1) CET (2) NAO (3) ENSO
Period A
(1856–1997)
Trend 0.76 -0.67 0.14
AOD -0.46 1.18* 1.07**
SSN -0.28 0.39 -0.20
ENSO 0.06 -0.17
NAO 4.57***
Period B
(1856–1957)
Trend 0.31 -0.11 0.13
AOD -0.39 0.86 0.15
SSN -0.12 -0.04 -0.84**
ENSO 0.22 -0.11
NAO 4.7***
Period C
(1958–1997)
Trend -0.05 1.12 -0.24
AOD -0.38 1.07 2.01**
SSN -0.34 1.03 0.62
ENSO -0.27 0.28
NAO 4.01***
Period D
(1978–1997)
Trend 1.04* 1.74 -0.57
AOD -1.50** 1.29 2.18***
SSN 0.15 1.70* -0.13
ENSO -0.02 0.52
NAO 3.93***
Values significant at 90%, 95% and 99% level derived using a
Student t-test are shown by *, ** and ***, respectively
4992 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
NAO can be influenced by other factors and shown in
column 2. Studies suggested that there are prefer-
ences of positive phases of NAO in climate change
scenarios (Visbeck et al. 2001). This is consistent
with the observation for trend during period C and D,
though only significant at 80% level. NAO and
Volcanos show positive correlation throughout the
period (A–D), but only significant at 90% level for
period A. It is likely that the number of total
occurrence of volcanos in period A might have a
role. The regression coefficient in period D (?1.29)
is seen to be higher than that in period A (?1.18),
but because of lesser data points, it fails to indicate
the significant result. Such positive connection
between volcano and NAO is also noted by Gray
et al. (2013, their Fig. 3) using MLR technique for
over 150 years of observational data.
Now the focus is on solar NAO behaviour. During
the post-1957 period, the relationship between SSN
and NAO has strengthened. From column 2, Table 1,
it is seen that the NAO is influenced positively by
SSN during period D (level of significance 90%) and
C (only 80%, hence not marked) and the nature of
this signature is consistent with the recent study by
Ineson et al. (2011). Considering the period of the
latter half of twentieth century Roy (2010,2014) also
detected such signal in observation. A recent work of
Roy and Kriplani (2019, their Fig. 2c) considered
SLP data of ‘1971–2012’ that covered a total of
42 years data from recent period. It also showed a
clear positive NAO pattern for the solar signature in
zero lag case. Interestingly, when the focus is on
period A and B, the NAO solar relationship not only
weakened but sometimes reversed in sign. Gray et al
(2013, their Figs. 3 and 4) using data from longer
time found a reverse association between the Sun and
NAO. Furthermore, Roy and Haigh (2010, their
Fig. 1) also detected similar features for a longer time
period. In both those studies, Sun and NAO suggested
a negative (insignificant) association matching to that
of Period B. Such opposing nature of Sun and NAO
during different periods though was shown in various
popular papers but did not receive attention. This
analysis thus questions robustness of the study of
Ineson et al. (2011), which suggested that inactive
phase of the Sun (as represented by solar UV
variability in their model) is linked with the negative
phase of NAO and vice versa. This work indicates the
robustness of such association requires further explo-
ration. The mean atmospheric state also needs to be
taken into account with additional care in detecting
the role of the Sun on major climate variability and
proposing a mechanism based on any detected
signals.
In Table 1, ENSO, however, does not show any
significant influence on NAO in any of the periods.
Influence of ENSO (column 3): It is interesting
to identify whether ENSO, i.e. the source of the
dominant variability in tropics, is also influenced by
other dominant factors. Possible candidates consid-
ered are longer-term trend, to represent a linear rise in
temperature associated with anthropogenic influence,
and natural variability represented by volcanic erup-
tions and SSN. The discussion mainly follows about
ENSO and natural variability.
The results using ENSO as the dependent param-
eter (column 3) give one interesting finding relating
to volcanic eruptions and ENSO. A volcanic eruption
is seen to strongly favour positive phase of ENSO,
but only true during the last half of the twentieth
century. The signal is strongest (99% significant)
during period D (1978–1997), while weak in period
B. Result during period B (1856–1957) is probably
dominated by lesser/weaker volcanos during the first
half of the twentieth century. Without many volcanic
eruptions, ENSO still followed its own inter-annual
variability. When considering the overall period of
analysis in period ‘A’ (1856–1997), volcano and
ENSO relationship is found to be dominated by
period D and suggests a level of significance up to
95%.
The period ‘D’ is consistent with various works as
discussed earlier (Emily-Geay et al. 2008; Ohba et al.
2013; McGregor and Timmermann 2011; Stenchikov
et al. 2009; Adams et al. 2003; Khodri et al. 2017).
Those studies discussed ENSO is noticeably influ-
enced (in preference of its positive phase) by very
strong explosive volcanic eruptions. It is also consis-
tent with the observation of Fig. 1d and the fact that
SST trend along the equator in the final 50 years of
the twentieth century shows a robust El Nin
˜o-like
pattern for all observations using different datasets
(Liu et al. 2005).
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4993
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
A little discussion about the possible mechanism
is added here (schematics are in Fig. 3). Warming in
the central equatorial Pacific in response to explosive
volcanos (Table 1, column 3) can be explained by a
hypothesis known as the dynamical thermostat
hypothesis (Clement et al. 1996). It states for a
uniform reduction of incoming surface solar radiation
to a large degree, (which is associated here with
explosive volcanos) the response of SST is different
on two sides of the tropical Pacific. Due to ocean
advection theory, the western Pacific cools faster than
the east. It initially reduces the climatological zonal
SST gradient, which subsequently influences trade
winds in the central Pacific. The resultant positive
SST gradient can activate El Nin
˜o phase, reversing
trade winds. McGregor and Timmermann (2011)
proposed another theory that states, the anomalous
westerly in tropical Pacific could be attributed to the
rapid response over the maritime continent, in
response to uniform reduction of incoming surface
solar radiation (here due to volcano). It is mainly
linked to the land-sea contrast, where the surface
cooling around equatorial Pacific and its timescale for
adjustment are leading responsible factor. It is the fast
cooling around the maritime continent that contribute
to the positive zonal SST gradient. It is noteworthy
that the role of dynamical thermostat hypothesis
(Clement et al. 1996) and the proposition of McGre-
gor and Timmermann (2011) both can act together to
reinforce the mechanism relating to volcano and
ENSO.
Another interesting finding from column 3 is
about solar-ENSO behaviour. In period C
(1958–1997), there is a positive solar signal detected
in ENSO, which is only significant up to 80% level.
Though it is less significant, the sign of such
signature agrees with White et al. (1997, their
Fig. 6) who used data of second half of the twentieth
century and detected a similar in-phase relationship
between the Sun and ENSO. When the focus is on
Period B (1856–1957), the relationship not only
reversed, but even showed a significant result up to
95% level. Interestingly Roy and Haigh (2012),
focusing on a similar period of analysis, also
observed the preferential alignment of the negative
phase of ENSO in active phases of the solar cycle
(i.e., when SSN [80) during northern winter.
Updating that record with current data (upto 2015)
also confirmed such observation (Fig. 4). The recent
four years of 2016–2019, being low solar years does
Figure 3
Schematic describing the role of the Sun for around last 150 years period: afocuses an earlier period when the Sun and ENSO negative
association was significant; for b, the emphasis is on the period 1978–1997 and the role of explosive volcanos. More details on Mechanism I
and II are discussed in the Supplementary text
4994 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
not make any difference to this result. Considering
spatial pattern, Roy (2010,2014) however could not
identify a detectable signature in tropical Pacific Sea
Surface Temperature (SST) using Hadley Sea Surface
Temperature version 2 (HadSST2) data during the
same period. This could be because that work
considered every month of the year rather than only
boreal winter. van Loon et al. (2007) and Meehl et al.
(2008) using the method of a solar peak year
compositing detected similarly signed (cold) signa-
ture during December January February (DJF),
though it only focused on solar peak years rather
than active/inactive phases of the solar cycle. Due to
two opposite nature of signature in period B and C, it
is difficult to discern any solar-ENSO relationship for
the entire period of analysis. That expected insignif-
icant result is observed in period A.
During an earlier period (1856–1957, DJF), the
significant negative association between the Sun and
ENSO can also be explained by the hypothesis of a
dynamical thermostat (Clement et al. 1996) and the
proposition of McGregor and Timmermann (2011)
(discussed in Fig. 3a). Interestingly, during the inac-
tive phase of the Sun, there is a uniform reduction of
surface heat fluxes and hence, the same principle as
discussed for the volcano is equally applicable. It
could trigger El Nin
˜o like situation, while active solar
years will trigger La Nin
˜a like situation following the
reverse mechanism. The decadal solar signature for
inciting trade wind is clearly detected in Fig. 5a (left)
(and discussed in Sect. 3.2), which is though small in
magnitude (lower than -1.5 hPa), but significant up
to 95% level. Such an association is also seen in
Table 1, Period B, column 3, as reflected by Sun
ENSO connection (b=-0.84, significant at 95%
level). In general, such analysis indicates that solar
cycle drives the coupled ocean–atmosphere system
more subtly towards a state in which La Nin
˜a
conditions are usually favoured for active solar years;
whereas, El Nin
˜o’s for low solar years. At low solar
years, the usual interannual variability of ENSO
(governed by Kelvin and Rossby wave movement)
dominates, but during high solar years, the solar
signature overpowers as seen in Fig. 4(top plot). In
that plot, the ENSO suggested cold phase during peak
solar years of active cycles (SSN [80). Even after
the peak solar year, cold ENSO phase was also
noticed after 1 to 2 years lag (Roy 2014; Roy and
Haigh 2012). Overruling all speculations, the last
solar peak year of 2014 again suggested the cold
phase of ENSO. It is noteworthy that three other solar
peak years from later decades of the last century
(bottom plot, Fig. 4) which are not influenced by
volcanos (year 1968, 1979, 1989) are also showing
preference towards cold event side. Interestingly, the
Sun ENSO connection was reverted again like earlier
period since 1998 (Roy and Haigh, 2012, also seen in
Fig. 4, top plot).
Figure 4
Scatter plot of ENSO (DJF) against annual average SSN from 1856
to 2015 inclusive. Red squares mark the peak years of solar cycles.
All years during 1856–1957 and 1998–2015 are aligned to the
negative phase of ENSO during active phases of the solar cycle
(i.e., when SSN [80), while intervening periods (1958–1997)
show no such preferences for a high Sun
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4995
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3.2. Spatial signatures
Focusing attention on the spatial pattern, the MLR
technique for zero lag case is applied to SLP with
independent parameters as AOD, linear trend, ENSO,
and SSN (Fig. 5). Shaded regions are estimated
significant at the 95% level using a t-test. The major
findings are discussed below.
ENSO(1856-1977) ENSO(1978-1997)
(a) Solar
(b) ENSO
Figure 5
Amplitudes of the components of variability for SLP (in hPa) during DJF. The top panel (a) is due to SSN using different independent factors
as longer term trend, AOD and ENSO; whereas, the bottom panel (b) is due to ENSO using independent factors as trend, AOD and SSN. The
bottom panel is an equatorial stereographic map centered on the north pole, as it mainly focuses on the Arctic. The right panel is for period
(1978–1997) and the left panel for (1856–1977). Positive values are shown in brown colour and negative by blue. Shaded regions are
estimated significant at the 95% level using a t-test
4996 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Signature due to the Sun:Fig.5a shows the spatial
pattern of solar signature in SLP. It excluded other
major factors (volcano, ENSO, and trend), those are
very likely to influence results. The right panel is for
1978–1997; whereas, left panel for the earlier period of
available data series. In both the figures (Fig. 5a), a
strong positive signal around Aleutian Low (AL) is
clearly detected suggesting the robustness of that signal
as also noticed by Roy and Haigh (2010). The right
panel of Fig. 5a (and Fig. S3) suggests a positive Arctic
Oscillation (AO) pattern; whereas, a clear dissimilarity
is noticed around the far North Atlantic in the left panel.
A positive NAO-like signature is distinguished in the
right panel, in accordance with the results of Table 1
(Period D, column 2) and the study of Ineson et al.
(2011). Surprisingly, that signature around the pole is
reversed in the left panel (Fig. 5a) with significant
region mainly localised around the places of Greenland.
That reversal of signature is also noticed in Roy
(2010,2014), those used slightly different years; though
neither discussed solar NAO connections, nor consid-
ered/compared various time periods over 150 years.
MLR is also performed with or without the
inclusion of some of those chosen independent
indices; however, the main results did not change.
It supports the robustness of detected signals. Current
analyses also explain why Roy and Haigh (2010, their
Fig. 1) could not identify any NAO-like pattern using
data of longer time period as is also seen and
discussed in Table 1(Period A, column 2). All such
analyses raise doubt about the proposed relationship
as discussed in Ineson et al. (2011) and other related
studies based on similar mechanisms. It indicates that
the robustness of solar NAO known connection needs
exploring further.
A brief discussion is also presented on one
possible mechanism relating to the Sun and NAO
connection, which can be explained by the proposi-
tion of Bjerknes (1966). During later decades, the
broader maxima of solar cycles (1–2 year after peak
solar years) are inclined to warm events of ENSO
(Roy and Haigh 2012; Roy 2014). Following Bjerk-
nes (1966), (also discussed in detail in Fig. 3,
Mechanism II), the anomalously great heat of warm
equatorial ocean enters into the rising branch of the
regional Hadley circulation and strengthens the cell.
It generates above the normal flux of angular
momentum to the westerly winds around mid-
latitude. Thus, warming in tropical Pacific can
strengthen mid-latitude westerly jets during boreal
winter and subsequently can favour a positive phase
of NAO. Such response is strongest in the winter
hemisphere due to greater baroclinicity. During the
period (1856–1957, DJF), the Sun showed prefer-
ences towards La Nin
˜a phases (Roy and Haigh 2012;
also, Table 1, Column 3, Period B) in all active solar
years. Following similar mechanisms (Bjerknes
1966), it can suggest a weakening of mid-latitude
westerlies in boreal winter, indicating a negative
association between the Sun and NAO (Fig. 5a, left).
In Fig. 5a, apart from the north Atlantic, the solar
signal around tropical Pacific is also seen to be
different in the left panel to that from the right. In the
left panel, the significant signal observed in the
tropics (though small in amplitude but 95% signifi-
cant) might be responsible to incite trade wind and
thus can favour cold event like situations of ENSO
via indirect dynamical coupling (Roy 2014). Such
signature is missing in the right panel, which suggests
it might be one factor that could also be responsible
for different Sun and ENSO connection; positive
during the 2nd half of twentieth century though
negative and significant in the earlier period (Table 1,
Period B, column 3). The sign of respective signals
around tropical Pacific and the north Atlantic remains
similar for Fig. 5a (left), if the period is switched
from ‘1856–1977’ to ‘1856–1957’. It supports Mech-
anism I (Fig. 3) in connection with the Sun and
ENSO, involving anomalous westerly in the tropical
Pacific.
It is noteworthy that Roy (2018b) hypothesized a
mechanism that is initiated in the mid-latitude and
influences tropics and ENSO. Here I hypothesize that
the tropical Pacific could also be a source of the
initial trigger to the coupling mechanism between the
tropics and mid-latitude. Interestingly, both the
mechanisms,(either originated in the tropical Pacific
or north Atlantic) reinforce each other and working in
a similar direction to set the strong coupling between
NAO, ENSO and the Sun during the latter two
decades of the last century.
Different Sun ENSO behaviour (Fig. 4) during the
later period (1958–1997) to that from earlier periods
(1856–1957) reconciles some conflicting findings
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4997
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
relating to the Sun, global temperature and ENSO
(Lockwood and Froehlich 2007; Laken et al. 2012).
Signature due to the ENSO: Figure 5b shows the
spatial signature of ENSO on SLP around the north
polar region, excluding SSN, AOD, and linear trend.
The right-hand panel is for 1978–1997 and left hand
for 1856–1977. Both panels identify negative NAO
pattern for ENSO around the pole. For solar signature
(Fig. 5a), tropical Pacific trade wind was involved
and hence the plot that covered tropical Pacific was
shown. However, for ENSO signature in SLP, the
focus of present discussion is mainly around polar
regions of NH and hence a stereographic map centred
on the north pole is presented (Fig. 5b). The positive
phase of ENSO is responsible for the warming of
polar vortex during northern winter, as detected in
observation (Thompson et al. 2002) and model
(Toniazzo and Scaife 2006). It subsequently suggests
a negative phase of polar annular modes in the
troposphere (Baldwin and Dunkerton 2001). One
possible route how the ENSO influences polar vortex
and then down to Arctic region can be through well-
known Brewer-Dobson circulation. There is another
mechanism explaining the influence of ENSO on the
polar vortex, through planetary waves. During the
warm phase of ENSO more planetary waves are
generated and interact with polar vortex. Significant
signature is noticed around the Arctic in Fig. 5b left-
panel, but the variability of SLP is increased in the
right panel (not significant). Lesser number of data
points during the later period might be one cause for
the reduced significance level. The increased ampli-
tude of signals around the Arctic, however, could be
linked to the fact that ENSO variability has increased
(Fig. 1d) and unevenly high number of warm events
occurred to that from cold events during the period
1978–1997, as discussed earlier.
All these studies indicate that due to change in the
mean state of the atmosphere, the influence of major
climate factors is felt differently in different parts of
the globe.
3.3. Question on Sun NAO Lagged Relationship
Recent studies (Gray et al. 2013; Scaife et al. 2013;
Andrews et al. 2015; Thieblemont et al. 2015)noted
solar lagged relationship around places of the north
Atlantic and discussed mechanisms. The current study
investigates the robustness of such association. Here,
the MLR technique is applied during the same two
different time periods as considered in Fig. 5and solar
lagged relationship (lag year 1 to lag year 3, Fig. 6a–c)
is performed. The results are also compared with the
overall 150 years of data as is done in Gray et al. (2013)
and shown in the bottom panel of each figure.
Figure 6a shows results for lag year 1. The top
two figures show a clear difference around the north
pole which is similar in nature like Fig. 5a respec-
tively, though much weaker. Signature in Fig. 6a,
bottom panel, matches to that of the top left panel in
places of North Atlantic probably due to longer data
records of the earlier period. The result of the bottom
panel is similar to that of Gray et al. (2013).
Figure 6b and c, are for lag year 2 and 3
respectively. For the North Atlantic region in lag
year 2 (Fig. 6b), the bottom panel is dominated by
strong signal during the period 1978–1997; whereas,
for Fig. 6c, it is overpowered by the earlier period
(1856–1977). Strong positive solar signature for
150 years record is around Azores high for lag year
2 and 3 (strongest for lag year 3) which is consistent
with the study by Gray et al. (2013). The main
findings in Fig. 6, bottom panel (all plots) concerning
longer record is in agreement with Gray et al. (2013).
The little deviation, if noticed, might be related to a
slight difference in the period considered.
The key observation from these analyses is that
the lagged relationship is also not robust but sensitive
to the period chosen. This analysis supports Sjolte
et al. (2018) who discussed that solar NAO link in
modelling works is not unequivocally supported by
reconstructions. The result of the overall 150 years is
mainly dominated by a period that has stronger
signals. Hence, caution should be taken in proposing
any mechanism based on any detected signal unless
that signal is very robust.
There could be arguments about the SLP data
qualities which could be poor in polar regions. In
methodology, it discussed uncertainty issues and its
ranges for SLP data. Moreover, it can also be stated
that various popular research (especially, those we
compared here e.g., Ineson et al. 2011; van Loon
et al. 2007; Gray et al. 2013 etc.) used the same data
around polar regions.
4998 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
4. Discussion and Summary
This paper addresses issues to improve under-
standing of the role of natural factors on surface
climate by using observational data and discussing
mechanisms. The analyses indicate that caution is
required to detect signals relating to natural vari-
ability (mainly the Sun) and the robustness of
identified signature need to be tested with a critical
viewpoint.
A rising trend of global temperature is noticed
during periods of 1860–1880, 1917–1944 and
1979–1997 which suggests that the Sun, explosive
volcanos and ENSO have roles in regulating global
temperature. This shows that it is important to
understand their individual as well as combined
behaviour. I discussed that SST anomaly around
(a) Lag1:
Figure 6
Amplitudes of the components of variability for SLP due to solar cycle variability (max–min), in hPa during DJF for alag of 1 year, blag of
2 years and clag of 3 years. Independent factors used are longer term trend, AOD and ENSO. Positive values are shown in brown colour and
negative by blue. Shaded regions are estimated significant at the 95% level using a t-test
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 4999
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
central tropical Pacific and North Atlantic during
period 1979–1997 was different compared to other
arbitrary base periods. Warming was observed in the
central Pacific, though cooling in the north Atlantic.
Hence, I focused more on those regions by addressing
NAO and ENSO in subsequent analyses.
Winter CET can be explained mainly by the
variability of NAO and true irrespective of chosen
periods of reference (Table 1, column 1). However,
NAO is influenced by other factors differently at
various times (Table 1, column 2).
The last two solar cycles of previous century
suggest a strong positive association between the
volcanic eruptions and ENSO (Table 1, column 3).
Various modelling and observational results also
support such connection (Ohba et al. 2013; Adams
et al. 2003; McGregor and Timmermann 2011;
Stenchikov et al. 2009; Khodri et al. 2017). It
(b) Lag 2:
Figure 6
continued
5000 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
discussed hypothesized mechanisms (Fig. 3b) to
explain volcano and ENSO observed connection.
On the other hand, during ‘1856–1957’, a signif-
icant negative relationship between the SSN and
ENSO is identified during DJF. The signal is positive
though insignificant during the second half of the
twentieth century (Table 1, column 3). I discussed
hypothesized mechanisms (Fig. 3a) to explain cool-
ing (due to active Sun) in the central tropical Pacific
during the earlier period.
The results shown here support the fact that shows
explosive volcanos are often associated with the
positive phase of NAO (Table 1, Period A) and thus
agrees with winter north polar warming features
(Robock and Mao 1992). Hypothesized mechanisms
initiated in the tropical Pacific is mentioned (Fig. 3b).
Warming along the central and equatorial Pacific
during DJF (warm ENSO due to volcano) could
favour a positive phase of NAO, following a non-
linear alignment.
The in-phase relationship between the NAO and
the Sun as identified by other studies, though
observed during the last two decades of twentieth
century, is missing in earlier periods (Table 1,
(c) Lag 3:
Figure 6
continued
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 5001
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
column 2 and Fig. 5a). The connection is strongest
during later decades (Table 1, period D and Fig. 5a,
right) when explosive volcanos coincidentally mat-
ched with the active phase of solar cycles. It can also
be explained by known mechanisms (Fig. 3b). Dur-
ing later decades, the broader maxima of solar cycles
(1–2 year after peak solar years) are inclined to warm
events of ENSO (Roy and Haigh 2012; Roy 2014).
Such warming via regional Hadley cell is often
accompanied by strengthening the midlatitude west-
erlies and subsequently, may be responsible for
positive Sun NAO connection. Following similar
mechanism, active Sun and cold ENSO, as it the case
for the earlier period, will suggest a weakening of
midlatitude westerlies in boreal winter, indicating a
negative association between the Sun and NAO
(Figs. 3a; 5a, left).
Apart from issues concerning the robustness of
earlier proposed connection on the Sun and NAO, the
present study also questioned on their known lagged
relationship of various scales and shows that solar-
NAO lagged connection is not unequivocally sup-
ported by observational records. Strong volcanos
those coincidentally erupted near the peak of active
solar cycles can have an impact on the mean state of
the atmosphere (both the stratosphere and ocean).
Thus, the solar, NAO and ENSO relationship needs to
be investigated with additional care.
This work aims at solving and reconciling various
contradictory findings. Such critical analysis will be
helpful for an improved understanding of various
modes of variability and their complicated role on
climate. This, in turn, will lead towards a better
understanding of complex natural climate variability.
Acknowledgements
This work was motivated by third-year undergraduate
project supervision in the University of Exeter, with
project title ‘Factors affecting winter temperature
around Europe’ with codes ‘ECM3735’. Figure 2and
Fig. S2 are generated by the NOAA/ESRL Physical
Sciences Division, Boulder Colorado from their Web
site at https://www.esrl.noaa.gov/psd/. The preprint
form of the earlier two versions of this manuscript are
also available in the web https://www.preprints.org/
manuscript/201608.0025/v1 (https://doi.org/10.20944/
preprints201608.0025.v1)andhttps://www.preprints.
org/manuscript/201608.0025/v2 (https://doi.org/10.
20944/preprints201608.0025.v2) and included in the
reference list.
Funding
This research did not receive any funding.
Compliance with ethical standards
Conflict of interest There are no financial and non-financial
competing interests.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits use,
sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The
images or other third party material in this article are included
in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/.
Publisher’s Note Springer Nature remains neutral
with regard to jurisdictional claims in published maps
and institutional affiliations.
REFERENCES
Adams, J. B., Mann, M. E., Ammann, C. M., et al. (2003). Proxy
evidence for an El Nin
˜o-like response to volcanic forcing. Na-
ture.https://doi.org/10.1038/nature02101.
Allan, R., & Ansell, T. (2006). A new globally complete monthly
historical gridded mean sea level pressure dataset (HadSLP2):
1850–2004. Journal of Climate,19(22), 5816–5842.
Allan, R. J. (2000). ENSO and climate variability in the last 150
years. In H. F. Diaz & V. Markgraf (Eds.), El Nin
˜o and the
Southern Oscillation: multiscale variability, global and regional
impacts (pp. 3–55). New York: Cambridge Univ. Press.
Andrews, M. B., et al. (2015). A simulated lagged response of the
North Atlantic Oscillation to the solar cycle over the period
1960–2009. Environmental Research Letters, 10, 054022.
Baldwin, M. P., & Dunkerton, T. J. (2001). Stratospheric harbin-
gers of anomalous weather regimes. Science, 294(5542),
581–584. https://doi.org/10.1126/science.1063315.
5002 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Bjerknes, J. (1966). A possible response of the atmospheric Hadley
circulation to equatorial anomalies of ocean temperature. Tellus,
18(4), 820–829. https://doi.org/10.1111/j.2153-3490.1966.
tb00303.x.
Bond, N. A., Overland, J. E., Spillane, M., & Stabeno, P. (2003).
Recent shifts in the state of the North Pacific. Geophysical
Research Letters, 30(23), 2183. https://doi.org/10.1029/
2003GL018597.
Clement, A. C., Seager, R., Cane, M. A., & Zebiak, S. E. (1996).
An ocean dynamical thermostat. Journal of Climate, 9,
2190–2196. https://doi.org/10.1175/1520-0442.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N.,
et al. (2011). The twentieth century reanalysis project. Quarterly
Journal Royal Meteorological Society, 137(654), 1–28. https://
doi.org/10.1002/qj.776.
Driscoll, S., Bozzo, A., Gray, L. J., Robock, A., et al. (2012).
Coupled Model Inter-comparison Project 5 (CMIP5) simulations
of climate following volcanic eruptions. Journal of Geophysical
Research, 117, D17. https://doi.org/10.1029/2012JD017607.
Emile-Geay, J., Seager, R., Cane, M. A., Cook, E. R., & Haug, G.
H. (2008). Volcanoes and ENSO over the Past Millennium.
Journal of Climate, 21, 3134–3148.
Fedorov, A., & Philander, S. G. (2000). Is El Nin
˜o changing?
Science, 288(5473), 1997–2002. https://doi.org/10.1126/science.
288.5473.1997.
Gray, L. J., Beer, J., Geller, M., et al. (2010). Solar influences on
climate. Reviews of Geophysics, 48, RG4001. https://doi.org/10.
1029/2009RG000282.
Gray, L. J., Scaife, A. A., Mitchell, D. M., et al. (2013). A lagged
response to the 11 years solar cycle in observed winter Atlantic/
European weather patterns. Journal of Geophysical Research:
Atmospheres, 118, 13405–13420. https://doi.org/10.1002/
2013JD020062.
Gu, D., & Philander, S. G. (1997). Inter-decadal climate fluctua-
tions that depend on exchanges between the tropics and extra-
tropics. Science, 275(5301), 805–807.
Haam, E., & Tung, K. K. (2012). Statistics of Solar Cycle–La Nin
˜a
connection: Correlation of two auto-correlated time series.
Journal of the Atmospheric Sciences, 69, 2934–2939.
Haigh, J. D. (1996). The impact of solar variability on climate.
Science, 272(5264), 981–984.
Haigh, J. D., Blackburn, M., & Day, R. (2005). The response of
tropospheric circulation to perturbations in lower-stratospheric
temperature. Journal of Climate, 18(17), 3672–3685.
Huang, B., Thorne, P., Smith, T., Liu, W., Lawrimore, J., Banzon,
V., et al. (2015). Further exploring and quantifying uncertainties
for extended reconstructed sea surface temperature (ERSST)
Version 4 (v4). Journal of Climate.https://doi.org/10.1175/JCLI-
D-15-0430.1.
Ineson, S., Scaife, A. A., Knight, J. R., et al. (2011). Solar forcing
of winter climate variability in the Northern Hemisphere. Nature
Geoscience.https://doi.org/10.1038/NGEO1282.
Ingleby, N. B. (2001). Comments on ‘A statistical determination of
the random observational errors present in voluntary observing
ships’ meteorological reports’. Journal of Atmospheric and
Oceanic Technology, 18, 1102–1107.
IPCC, Climate Change (2013). The Physical Science Basis. Con-
tribution of Working Group I to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change [Stocker, T.F.,
D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A.
Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge
University Press, Cambridge, United Kingdom and New York,
NY, USA, 1535 pp.
Jones, P. D., Jonsson, T., & Wheeler, D. (1997). Extension of the
North Atlantic Oscillation using early instrumental pressure
observations from Gibraltar and southwest Iceland. International
Journal of Climatology, 17, 1433–1450.
Kaplan, A., Cane, M. A., Kushnir, Y., Clement, A. C., Blumenthal,
M. B., & Rajagopalan, B. (1998). Analyses of global sea surface
temperature 1856–1991. Journal of Geophysical Research C:
Oceans,103(C9), 18567–18589. https://doi.org/10.1029/
97JC01736.
Kent, E. C., & Taylor, P. K. (1997). Choice of a Beaufort equiv-
alent scale. Journal of Atmospheric and Oceanic Technology, 14,
228–242.
Kiehl, J. T., & Trenberth, K. E. (1997). Earth’s annual global mean
energy budget. Bulletin of the American Meteorological Society,
78(2), 197–208.
Kodera, K., & Kuroda, Y. (2002). Dynamical response to the solar
cycle. Journal of Geophysical Research, 107(D24), 4749. https://
doi.org/10.1029/2002JD002224.
Khodri et al. (2017). Tropical explosive volcanic eruptions can
trigger El Nin
˜o by cooling tropical Africa. Nature Communica-
tions, 8.(Article number: 778).
Laken, et al. (2012). A decade of the moderate resolution imaging
spectroradiometer: Is a solar-cloud link detectable. Journal of
Climate.https://doi.org/10.1175/JCLI-D-11-00306.1.
Latif, M., Kleeman, R., & Eckert, C. (1990s). Greenhouse warm-
ing, decadal variability, or El Nin
˜o? An attempt to understand the
anomalous 1990s. Journal of Climate, 10(9), 2221–2239.
Lean, J., & Rind, D. (2001). Earth’s response to a variable Sun.
Science, 292(5515), 234–236.
Lenssen, N., Schmidt, G., Hansen, J., Menne, M., Persin, A.,
Ruedy, R., et al. (2019). Improvements in the uncertainty model
in the Goddard Institute for Space Studies Surface Temperature
(GISTEMP) analysis. Journal of Geophysical Research: Atmo-
spheres.https://doi.org/10.1029/2018JD029522.
Liu, Z., Vavrus, S., He, F., Wen, N., & Zhong, Y. (2005).
Rethinking tropical ocean response to global warming: The
enhanced equatorial warming. Journal of Climate, 18,
4684–4700. https://doi.org/10.1175/JCLI3579.1.
Liu, W., Huang, B., Thorne, P. W., Banzon, V. F., Zhang, H.-M.,
Freeman, E., et al. (2014). Extended Reconstructed Sea Surface
Temperature version 4 (ERSST.v4): Part II. Parametric and
structural uncertainty estimations. Journal of Climate, 28,
931–951. https://doi.org/10.1175/JCLI-D-14-00007.1.
Lockwood, M., & Fro
¨hlich, C. (2007). Recent oppositely directed
trends in solar climate forcings and the global mean surface air
temperature. Proceedings of the Royal Society A, 463(2086),
2447–2460. https://doi.org/10.1098/rspa.2007.1880.
Manley, G. (1974). Central England Temperatures: Monthly means
1659 to 1973. Quarterly Journal of the Royal Meteorological
Society, 100, 389–405.
McGregor, S., & Timmermann, A. (2011). The effect of explosive
tropical volcanism on ENSO. Journal of Climate, 24,
2178–2191. https://doi.org/10.1175/2010JCLI3990.1.
McGregor, S., Timmermann, A., & Timm, O. (2010). A unified-
proxy for ENSO and PDO variability since 1650. Climate Past,
5, 1–17.
McPhaden, M. J., & Zhang, D. (2004). Pacific Ocean circulation
rebounds. Geophysical Research Letters, 31, L18301. https://doi.
org/10.1029/2004GL020727.
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 5003
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Meehl, G. A., & Teng, H. (2000s). CMIP5 multi-model hindcasts
for the mid-1970s shift and early 2000s hiatus and predictions for
2016–2035. Geophysical Research Letters, 41(5), 1711–1716.
Meehl, G. A., Arblaster, J. M., Branstator, G., & van Loon, H.
(2008). A coupled air–sea response mechanism to solar forcing
in the Pacific region. Journal of Climate, 21(12), 2883–2897.
Meehl, G. A., Arblaster, J. M., Matthes, K., Sassi, F., & van Loon,
H. (2009). Amplifying the pacific climate system response to a
small 11-year solar cycle forcing. Science, 325, 1114–1118.
https://doi.org/10.1126/science.117287.
Miller, J., Cayan, D. R., Barnett, T. P., Graham, N. E., & Ober-
huber, J. M. (1994). The 1976–1977 climate shift of the Pacific
Ocean. Oceanography, 7,1.
Minobe, S. (2000). Spatio-Temporal Structure of the pentadecadal
variability over the North Pacific. Progress in Oceanography, 47,
381–408.
Mitchell, D. M., Gray, L. J., & Charlton-Perez, A. J. (2011). The
structure and evolution of the stratospheric vortex in response to
natural forcings. Journal of Geophysical Research.https://doi.
org/10.1029/2011JD015788.
Morice, C. P., Kennedy, J. J., Rayner, N. A., & Jones, P. D. (2012).
Quantifying uncertainties in global and regional temperature
change using an ensemble of observational estimates: The
HadCRUT4 dataset. Journal of Geophysical Research, 117,
D08101. https://doi.org/10.1029/2011JD017187.
Ohba, M., Shiogama, H., Yokohata, T., & Watanabe, M. (2013).
Impact of strong tropical volcanic eruptions on ENSO simulated
in a coupled GCM. American Meteorological Society, 26,
5169–5182. https://doi.org/10.1175/JCLI-D-12-00471.1.
Oliva, et al. (2017). Recent regional climate cooling on the
Antarctic Peninsula and associated impacts on the Cryosphere.
Science of the Total Environment, 580, 210–223.
Parker, D. E., & Horton, E. B. (2005). Uncertainties in the Central
England Temperature series since 1878 and some changes to the
maximum and minimum series. International J. Climatology, 25,
1173–1188.
Parker, D. E., Legg, T. P., & Folland, C. K. (1992). A new daily
Central England Temperature Series, 1772–1991. International
Journal of Climatology, 12, 317–342.
Pierce, D. W., Barnett, T. P., & Latif, M. (2000). Connections
between the Pacific Ocean tropics and midlatitudes on decadal
timescales. Journal of Climate, 13(6), 1173–1194.
Polvani, L. M., et al. (1990s). The impact of ozone-depleting
substances on tropical upwelling, as revealed by the absence of
lower-stratospheric cooling since the late 1990s. Journal of
Climate, 30, 2523. https://doi.org/10.1175/JCLI-D-16-0532.1.
Qiong, Z., Yue, G., & Haijun, Y. (2008). ENSO amplitude change
in observation and coupled models. Advances in Atmospheric
Sciences, 25(3), 361–366.
Robock, A. (2003). Volcanoes: Role in climate. In J. Holton, J.
A. Curry, & J. Pyle (Eds.), Encyclopedia of atmospheric sciences
(pp. 2494–2500). London: Academic Press. https://doi.org/10.
1006/rwas.2002.0169.(Invited paper).
Robock, A., & Mao, J. (1992). Winter warming from large volcanic
eruptions. Geophysical Research Letters, 19(24), 2405–2408.
https://doi.org/10.1029/92GL02627.
Roy I (2010) Solar signals in Sea Level Pressure and Sea Surface
Temperature. Ph.D. Thesis, Department of Space and atmo-
spheric Science, Imperial College London.
Roy, I. (2014). The role of the Sun in atmosphere ocean coupling.
International Journal of Climatology, 34(3), 655–677. https://
doi.org/10.1002/joc.3713.
Roy, I. (2016). The role of natural factors on major climate vari-
ability in northern winter. Preprints.https://doi.org/10.20944/
preprints201608.0025.v2.
Roy, I. (2018a). Solar cyclic variability can modulate winter Arctic
climate. Scientific Reports, Nature Publication, 8, 4864. https://
doi.org/10.1038/s41598-018-22854-0.
Roy, I. (2018b). Addressing on abrupt global warming, warming
trend slowdown and related features in recent decades. Frontiers,
6, 136.
Roy, I., & Haigh, J. D. (2010). Solar cycle signals in sea level
pressure and sea surface temperature. Atmospheric Chemistry
and Physics, 10(6), 3147–3153.
Roy, I., & Haigh, J. D. (2012). Solar cycle signals in the pacific and
the issue of timings. Journal of Atmospheric Science, 69(4),
1446–1451. https://doi.org/10.1175/JAS-D-11-0277.1.
Roy, I., & Kriplani, R. (2019). The role of natural factors (Part 1):
Addressing on Mechanism of different types of ENSO, related
teleconnections and solar influence. Theoretical and Applied
Climatology, 137, 469–480. https://doi.org/10.1007/s00704-018-
2597-z.
Sato, M., Hansen, J. E., McCormick, M. P., & Pollack, J. B. (1993).
Stratospheric aerosol optical depths (1850–1990). Journal of
Geophysical Research, 98, 22987–22994.
Scafetta, N. (2013). Discussion on climate oscillations: CMIP5
general circulation models versus a semi-empirical harmonic
model based on astronomical cycles. Earth-Science Reviews,
126, 321–357.
Scaife, A., Ineson, S., Knight, J. R., Gray, L., Kodera, K., & Smith,
D. M. (2013). A mechanism for lagged North Atlantic climate
response to solar variability. Geophysical Research Letters,
40(2), 434–439.
Sigmond, M., & Scinocca, J. F. (2010). The influence of basic state
on the Northern Hemisphere circulation response to climate
change. Journal of Climate, 23, 1434–1446.
Sigmond, M., Scinocca, J. F., & Kushner, P. J. (2008). Impact of
the stratosphere on tropospheric climate change. Geophysical
Research Letters, 35, 12. https://doi.org/10.1029/
2008GL033573.
Sjolte, et al. (2018). Solar and volcanic forcing of North Atlantic
climate inferred from a process-based reconstruction. Climate of
the Past, 14(8), 1179–1194. https://doi.org/10.5194/cp-14-1179-
2018.
Sobel, A. H., Held, I. M., & Bretherton, C. S. (2002). The ENSO
Signal in Tropical Tropospheric Temperature. J. Climate, 15,
2702–2706.
Stenchikov, G., Delworth, T. L., Ramaswamy, V., Stouffer, R. J.,
Wittenberg, A., & Zeng, F. (2009). Volcanic signals in oceans.
Journal of Geophysical Research, 114, D16104. https://doi.org/
10.1029/2008JD011673.
Thieblemont, R., et al. (2015). Solar forcing synchronizes decadal
North Atlantic climate variability. Nature Communications, 6,
8268.
Thompson, D. W. J., Baldwin, M. P., & Wallace, J. M. (2002).
Stratospheric connection to northern hemisphere wintertime
weather: Implications for prediction. Journal of Climate, 15(12),
1421–1428.
5004 I. Roy Pure Appl. Geophys.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Toniazzo, T., & Scaife, A. A. (2006). The influence of ENSO on
winter North Atlantic climate. Geophysical Research Letters, 33,
L24704. https://doi.org/10.1029/2006GL027881.
Trenberth, K. E., & Hoar, T. J. (1996). The 1990–1995 El Nin
˜o-
Southern Oscillation Event: Longest on record. Geophysical
Research Letters, 23, 57–60.
Trenberth, K. E., & Hoar, T. J. (1997). El Nin
˜o and climate change.
Geophysical Research Letters, 24(23), 3057–3060.
van Loon, H., Meehl, G. A., & Shea, D. J. (2007). Coupled
air–sea response to solar forcing in the Pacific region during
northern winter. Journal of Geophysical Research: Atmo-
spheres, 112, D02108. https://doi.org/10.1029/2006JD00
7378.
Vecchi, G. A., & Soden, B. J. (2007). Global warming and the
weakening of the tropical circulation. Journal of Climate, 20,
4316–4340.
Visbeck, M. H., Hurrell, J. W., Polvani, L., & Cullen, H. M. (2001).
The North Atlantic Oscillation: Past, present, and future.
Proceedings of the National Academy of Sciences, 98(23),
12876–12877. https://doi.org/10.1073/pnas.231391598.
Wang, C., & Fiedler, P. C. (2006). ENSO variability and the
eastern tropical Pacific: A review. Progress in Oceanography,
69, 239–266.
Wang, T., Ottera, O. H., Gao, Y., & Wang, H. (2012). The response
of the north Pacific decadal variability to strong tropical volcanic
eruptions. Climate Dynamics, 39(12), 2917–2936.
White, W. B., Lean, J., Cayan, D. R., et al. (1997). Response of
global upper ocean temperature to changing solar irradiance.
Journal of Geophysical Research: Oceans, 102(2), 3255–3266.
Zhang, D., & McPhaden, M. J. (2006). Decadal variability of the
shallow Pacific meridional overturning circulation: Relation to
tropical sea surface temperatures in observations and climate
change models. Ocean Modelling, 15(3–4), 250–273.
Zhang, Q., Guan, Y., & Yang, H. (2008). ENSO amplitude change
in observation and coupled models. Advances in Atmospheric
Sciences, 25(3), 361–366.
(Received August 26, 2019, revised May 18, 2020, accepted May 21, 2020, Published online June 8, 2020)
Vol. 177, (2020) Major Climate Variability and Natural Factors in Boreal Winter 5005
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”),
for small-scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are
maintained. By accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use
(“Terms”). For these purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or
a personal subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or
a personal subscription (to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the
Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data
internally within ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking,
analysis and reporting. We will not otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of
companies unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that
Users may not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to
circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil
liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by
Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer
Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates
revenue, royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain.
Springer Nature journal content cannot be used for inter-library loans and librarians may not upload Springer Nature journal
content on a large scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any
information or content on this website and may remove it or features or functionality at our sole discretion, at any time with or
without notice. Springer Nature may revoke this licence to you at any time and remove access to any copies of the Springer Nature
journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express
or implied with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or
warranties imposed by law, including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be
licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other
manner not expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
... Those elaborately discussed contradictory findings (van Loon et al. 2007;White et al. 1997). Solar related possible mechanisms, around the tropical Pacific, which is different in earlier and later periods are also hypothesized [Roy 2014, 2020, Fig. 3 (preprint version 2016] considering both the atmosphere-ocean feedback. Further to clarify the result of observation, two different segments of periods are discussed here in terms of SSN-ENSO behaviour considering the last 160 years. ...
... 4), and not weakens and hence wrong referencing. Such intensification of SLP around ITCZ, central Pacific, is also present in observational record of 1-year-lag for 150 years record [Roy 2020, Fig. 6a (preprint version 2016; Gray et al. 2013, Fig. 4]. However, it is sensitive to the time period chosen (earlier or later). ...
... However, it is sensitive to the time period chosen (earlier or later). Interestingly, though earlier period suggests strengthening of ITCZ, the later period indicates an insignificant influence of the SSN on tropical Pacific SLP [Roy 2020, Fig. 6a (preprint version 2016]. It is true for 1-year lag as well as zero lag. ...
Article
Full-text available
It is a commentary following a published paper in PNAS titled, 'Slowdown of the Walker circulation at solar cycle maximum', by Misios et al. (PNAS 116(15): 7186-7191, 2019). The article of Misios et al. (2019) claims that there is a slowdown of the Walker circulation during maximum periods of solar cycles. In support, they provided model results. They also gave directions of improved future predictive skill involving that knowledge of solar cycles. However, their work does not comply with various observational results. This contribution highlights those areas and pinpoints discrepancies. Knowing the limitations of models, if any model results match some very limited part of observations, it is not possible to make similar claims. It raises doubt on any improvement of future predictive skill.
... It was interesting to study the robustness of such a proposed association between the sun and NAO (Ineson et al. 2011). Various studies already addressed and explored that issue in detail using observation (Roy 2018a(Roy , 2020Roy and Kriplani 2018). Those noted that such inphase connection is clearly noticed since 1977, though inconsistent over the last 150 years (Roy and Haigh 2010) and suggested variations in earlier periods (Roy 2014). ...
... However, those hypotheses are yet to be verified and need exploring further by modelling work. Recent research (Roy 2020(Roy , 2018c) discussed the importance of taking proper account of atmospheric mean background state to understand the Sun-NAO connection better. ...
Article
Full-text available
This is a Correspondence following the undermentioned paper: Chiodo et al. (Nat Geosci 12:94–99, 2019).
Chapter
Full-text available
The Indian summer monsoon (ISM) plays a crucial role in the well-being of billion Indians. This study discusses teleconnection between ISM with two dominant tropical sea surface temperature modes, namely, the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Results are analyzed for observation and CMIP5 simulations in both historical and future scenarios. ENSO and IOD exert an offsetting impact on ISM. Due to an overly strong control by ENSO, the majority of CMIP5 models simulate an unrealistic IOD and ISM rainfall correlation, that might contribute to major uncertainties in ISM simulations in both historical as well as in future projections. For ENSO, the further focus was on the East Pacific type or Canonical ENSO and the Central Pacific type or ENSO Modoki, and their regional teleconnection was explored. Regions of Central North East India suggest strong teleconnection in models that matches with observation, though for ENSO Modoki case that completely disappears in the future. In terms of mechanism, tropic and mid-latitude connection, the influence of regional Hadley circulation, and the role of the Sun were addressed. Finally, a hypothesized mechanism was proposed for disruption of ISM–ENSO teleconnection in the latter two decades of the last century.
Article
Full-text available
We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 87% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).
Article
Full-text available
The puzzle of recent global warming trend slowdown has captured enough attention, though the underlying cause is still unexplained. This study addresses that area segregating the role of natural factors (the sun and volcano) to that from CO2 led linear anthropogenic contributions. It separates out a period 1976–1996 that covers two full solar cycles, where two explosive volcanos erupted during active phases of strong solar cycles. The similar period also matched the duration of abrupt global warming. It identifies that dominance of Central Pacific (CP) ENSO and associated water vapor feedback during that period play an important role. The possible mechanism could be initiated via a preferential alignment of NAO phase, generated by explosive volcanos. Inciting extratropical Rossby wave to influence the Aleutian Low, it has a modulating effect on CP ENSO. Disruption of Indian Summer Monsoon and ENSO during the abrupt warming period and a subsequent recovery thereafter can also be explained from that angle. Interestingly, CMIP5 model ensemble, and also individual models, fails to comply with such observation. It also explores possible areas where models miss important contributions due to natural drivers.
Article
Full-text available
The role of natural factors, mainly the sun, is explored on major tropospheric modes of variability in a holistic way. It formulates a flow chart, depicting coupling in the ocean-atmosphere system, initiated by solar decadal variability that involves El Niño Southern Oscillation (ENSO). Possible mechanisms for Canonic ENSO, Modoki ENSO and Canonic-Modoki ENSO are proposed considering their relevance to the decadal variation of Hadley, Walker circulation and mid-latitude jets. The upper stratospheric feature of the polar vortex is included too. Teleconnections by the ENSO on Indian Summer Monsoon (ISM) with a special emphasis on the later two decades of the last century is discussed. The disruption of usual ENSO-ISM teleconnection during that period is also attended. Subsequent analyses presented some results of solar signature which could possibly trigger different types of ENSO, agreeing with proposed mechanisms of the flow chart. It addressed the changing pattern of ENSO behaviour since the 1970s. The overall study can benefit the modelling community by an improved representation of ENSO in models and a better representation of ISM teleconnection via regional Hadley cell.
Article
Full-text available
The effect of external forcings on atmospheric circulation is debated. Due to the short observational period, the analysis of the role of external forcings is hampered, making it difficult to assess the sensitivity of atmospheric circulation to external forcings, as well as persistence of the effects. In observations, the average response to tropical volcanic eruptions is a positive North Atlantic Oscillation (NAO) during the following winter. However, past major tropical eruptions exceeding the magnitude of eruptions during the instrumental era could have had more lasting effects. Decadal NAO variability has been suggested to follow the 11-year solar cycle, and linkages have been made between grand solar minima and negative NAO. However, the solar link to NAO found by modeling studies is not unequivocally supported by reconstructions, and is not consistently present in observations for the 20th century. Here we present a reconstruction of atmospheric winter circulation for the North Atlantic region covering the period 1241–1970 CE. Based on seasonally resolved Greenland ice core records and a 1200-year-long simulation with an isotope-enabled climate model, we reconstruct sea level pressure and temperature by matching the spatiotemporal variability in the modeled isotopic composition to that of the ice cores. This method allows us to capture the primary (NAO) and secondary mode (Eastern Atlantic Pattern) of atmospheric circulation in the North Atlantic region, while, contrary to previous reconstructions, preserving the amplitude of observed year-to-year atmospheric variability. Our results show five winters of positive NAO on average following major tropical volcanic eruptions, which is more persistent than previously suggested. In response to decadal minima of solar activity we find a high-pressure anomaly over northern Europe, while a reinforced opposite response in pressure emerges with a 5-year time lag. On centennial timescales we observe a similar response of circulation as for the 5-year time-lagged response, with a high-pressure anomaly across North America and south of Greenland. This response to solar forcing is correlated to the second mode of atmospheric circulation, the Eastern Atlantic Pattern. The response could be due to an increase in blocking frequency, possibly linked to a weakening of the subpolar gyre. The long-term anomalies of temperature during solar minima shows cooling across Greenland, Iceland and western Europe, resembling the cooling pattern during the Little Ice Age (1450–1850 CE). While our results show significant correlation between solar forcing and the secondary circulation pattern on decadal (r = 0.29, p < 0.01) and centennial timescales (r = 0.6, p < 0.01), we find no consistent relationship between solar forcing and NAO. We conclude that solar and volcanic forcing impacts different modes of our reconstructed atmospheric circulation, which can aid in separating the regional effects of forcings and understanding the underlying mechanisms.
Article
Full-text available
This study investigates the role of the eleven-year solar cycle on the Arctic climate during 1979-2016. It reveals that during those years, when the winter solar sunspot number (SSN) falls below 1.35 standard deviations (or mean value), the Arctic warming extends from the lower troposphere to high up in the upper stratosphere and vice versa when SSN is above. The warming in the atmospheric column reflects an easterly zonal wind anomaly consistent with warm air and positive geopotential height anomalies for years with minimum SSN and vice versa for the maximum. Despite the inherent limitations of statistical techniques, three different methods - Compositing, Multiple Linear Regression and Correlation - all point to a similar modulating influence of the sun on winter Arctic climate via the pathway of Arctic Oscillation. Presenting schematics, it discusses the mechanisms of how solar cycle variability influences the Arctic climate involving the stratospheric route. Compositing also detects an opposite solar signature on Eurasian snow-cover, which is a cooling during Minimum years, while warming in maximum. It is hypothesized that the reduction of ice in the Arctic and a growth in Eurasia, in recent winters, may in part, be a result of the current weaker solar cycle.
Article
Full-text available
A correction to this article has been published and is linked from the HTML version of this article.
Article
Full-text available
Stratospheric aerosols from large tropical explosive volcanic eruptions backscatter shortwave radiation and reduce the global mean surface temperature. Observations suggest that they also favour an El Niño within 2 years following the eruption. Modelling studies have, however, so far reached no consensus on either the sign or physical mechanism of El Niño response to volcanism. Here we show that an El Niño tends to peak during the year following large eruptions in simulations of the Fifth Coupled Model Intercomparison Project (CMIP5). Targeted climate model simulations further emphasize that Pinatubo-like eruptions tend to shorten La Niñas, lengthen El Niños and induce anomalous warming when occurring during neutral states. Volcanically induced cooling in tropical Africa weakens the West African monsoon, and the resulting atmospheric Kelvin wave drives equatorial westerly wind anomalies over the western Pacific. This wind anomaly is further amplified by air–sea interactions in the Pacific, favouring an El Niño-like response.
Article
Full-text available
The impact of ozone-depleting substances on global lower-stratospheric temperature trends is widely recognized. In the tropics, however, understanding lower-stratospheric temperature trends has proven more challenging. While the tropical lower-stratospheric cooling observed from 1979 to 1997 has been linked to tropical ozone decreases, those ozone trends cannot be of chemical origin, as active chlorine is not abundant in the tropical lower stratosphere. The 1979–97 tropical ozone trends are believed to originate from enhanced upwelling, which, it is often stated, would be driven by increasing concentrations of well-mixed greenhouse gases. This study, using simple arguments based on observational evidence after 1997, combined with model integrations with incrementally added single forcings, argues that trends in ozone-depleting substances, not well-mixed greenhouse gases, have been the primary driver of temperature and ozone trends in the tropical lower stratosphere until 1997, and this has occurre...
Article
Full-text available
This work studies the role of natural factors mainly solar eleven-year cycle variability, and volcanic eruptions on two major modes of climate variability the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) for around last 150 years period. The NAO is the primary factor to regulate Central England Temperature (CET) during winter throughout the period, though NAO is impacted differently by other factors in different time periods. Solar variability has a positive influence on NAO during 1978-1997, which is opposite before that period. Solar NAO lag relationship is also sensitive to the chosen times of reference. Such analyses raise a question about previously proposed mechanism and relationship related to the sun and NAO. The ENSO is seen to be influenced strongly by solar variability and volcanic eruptions in certain periods. This study observes a strong negative association between solar variability and ENSO before the 1950s, which is even opposite during the second half of 20th century. The period 1978-1997, when two strong eruptions coincided with active years of strong solar cycles, the ENSO, and volcanic eruptions suggested the stronger association. Here we show that the mean atmospheric state is important for understanding the connection between solar variability, the NAO and ENSO and associated mechanism.