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ORIGINAL PAPER
Multiscale evolution of surface air temperature in the arid
regionof Northwest China and its linkages to ocean oscillations
Zhongsheng Chen1,2 & Yaning Chen1 & Ling Bai2 &
Jianhua Xu2
Received: 18 May 2015 /Accepted: 2 February 2016 /Published
online: 16 February 2016# Springer-Verlag Wien 2016
Abstract The global climate has experienced unprecedentedwarming
in the past century. The multiscale evolution of thewarming is
studied to better understand the spatial and tem-poral variation
patterns of temperature. In this study, based onthe yearly surface
air temperature from the gridded CRU TS3.22 dataset and the
ensemble empirical mode decompositionmethod (EEMD), we investigated
the multiscale evolution oftemperature variability in the arid
region of Northwest China(ARNC) from 1901 to 2013. Furthermore, the
possibleinfluences on the ARNC temperature change from theAtlantic
Multidecadal Oscillation (AMO), Pacific DecadalOscillation (PDO),
and dipole mode index (DMI) were alsodiscussed. The results
indicated that in the past century, theoverall temperature in the
ARNC has showed a significantnon-linear upward trend, and its
changes have clearly exhib-ited an interannual scale (quasi-2–3 and
quasi-6–7-year) andan interdecadal scale (quasi-14, quasi-24, and
quasi-70-year).Compared with the reconstructed interannual
variation, thereconstructed interdecadal variability plays a
decisive role inthe ARNC warming and reveals the climatic pattern
transfor-mation from the cold period to the warm period before
andafter 1987. Additionally, there were also regional differencesin
the spatial patterns of change trend in the ARNC tempera-ture at a
given time. We also found that the AMO and PDO
had significant impacts on the ARNC temperature fluctuationat an
interdecadal scale, whereas the DMI had a more impor-tant role in
warming at the annual scale, which suggests thatthe importance of
oceans cannot be ignored when consideringclimate change. Our
findings deepen the understanding of thetemperature changes all
over the ARNC in the context ofglobal warming.
1 Introduction
A rise in global mean temperature is a prime indicator ofglobal
warming (Foster and Rahmstorf 2011). The time seriesof the global
average temperature shows an obvious rise sincethe early 20th
century, most notably since the late 1970s(Foster and Rahmstorf
2011; IPCC 2013), and the global sur-face mean temperature has
increased by 0.85 °C (0.65–1.06 °C) over the period of 1880 to 2012
(IPCC 2013). Theincreasing temperature has caused many
warming-related is-sues, such as accelerating mountain glacier
loss, sea level rise,and frequent extreme climate events (IPCC
2013). Althoughthe global climate has experienced unprecedented
warming inthe past century, this warming is spatially and
temporally non-uniform (Ji et al. 2014). In recent decades, the
fastest warmingoccurred in mid-latitude regions of the Northern
Hemisphere(Huang et al. 2012; Ji et al. 2014).
The arid region of Northwest China (ARNC), one of themain arid
areas in the mid-latitude regions of the NorthernHemisphere, is
characterized by a typical temperate continen-tal arid climate. Due
to its unique natural geographic features,the temperature rise in
the ARNC has been synchronous withglobal warming but is
significantly higher in magnitude thanthat of the overall global
warming (Fan et al. 2011; Li et al.2012). Because of an apparent
mechanism of glaciers andsnow cover melt water acceleration in
response to recent
* Yaning [email protected]
1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang
Instituteof Ecology and Geography, Chinese Academy of
Sciences,Urumqi 830011, China
2 Key laboratory of GIScience of the Ministry of Education,
EastChina Normal University, Shanghai 200241, China
Theor Appl Climatol (2017) 128:945–958DOI
10.1007/s00704-016-1752-7
http://crossmark.crossref.org/dialog/?doi=10.1007/s00704-016-1752-7&domain=pdf
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climate warming in the ARNC, the glaciers and snow cover onthe
high mountains have been losing mass in recent years(Wang et al.
2010a). In addition, the recent climate warmingalso triggered a
series of other ecological and environmentalproblems, such as
vegetation decline, accelerating regionalwater cycle, and
intensified desertification (Chen 2010). It iscritical to
understand the temperature variation in the studyarea, which has
been of great concern (Shi et al. 2002; Liet al. 2012; Yang et al.
2014; Chen et al. 2014). These studiesmainly focused on average
warming over that time span usingtraditional statistical methods,
such as straight-line fitting andSen’s slope, which can extract
warming only at a constant rate(Ji et al. 2014). However, the
climate system is a complex non-linear system, and most of the
long-term variations in manyclimatic factors, such as temperature
and precipitation, exhibitnon-linear and non-stationary complex
processes, accompa-nied by a variety of time scales or periodic
oscillations (Wuet al. 2007; Xue et al. 2013; Franzke 2014). A
time-unvaryinglinear trend cannot effectively reveal the hidden
non-linearand non-stationary nature of temperature variability (Ji
et al.2014). In addition, the temperature fluctuation anywhere
inthe globe is not an accidental phenomenon but an
inherentreflection of the complex climate system (IPCC
2013).Previous studies have shown that the surface air
temperaturevariation in the ARNC is linked to solar activity, the
westerlycirculation, the Arctic Oscillation, the North
AtlanticOscillation, the Antarctic Oscillation, the Siberian
HighIntensity, the El Nino Southern Oscillation, the
TibetanPlateau, and human activities (Shi et al. 2007; Li et al.
2012;Chen et al. 2014). These findings help unveil the
underlyingcauses of changes in the ARNC temperature. The
AtlanticMultidecadal Oscillation (AMO) and Pacific
DecadalOscillation (PDO) are strong climate variability signals
atthe interdecadal time scale, which directly cause
interdecadalclimate change around the Atlantic and Pacific oceans
andhave an important role in modulation effects on
interannualclimate variability (Schlesinger and Ramankutty 1994;
Deseret al. 2010). Recent studies confirmed that the AMO and PDOare
dominant factors of oceanic influence on the global andregional
climate (Chylek et al. 2014; Varotsos et al. 2014;Mann et al. 2014;
McAfee 2014) and have significantly af-fected the Northern
Hemisphere’s temperature variability(Wyatt et al. 2012; Steinman et
al. 2015). Other studies haveproven that the Indian Ocean Dipole
(IOD), named the dipolemode index (DMI), also plays an important
role in australsurface air temperature anomalies (Saji et al. 2005)
andrainfall in Xinjiang (Zhou et al. 2015). However, the pos-sible
influences on the ARNC temperature change fromthe AMO, PDO, and DMI
have received relatively lessattention.
In this study, we focus on the spatial and temporal
charac-teristics of the surface air temperature in the ARNC and
theirlinkages to the PDO, AMO, and DMI at different temporal
scales. We conducted analyses based on three aspects:
(1)detecting the multiscale evolution of the surface air
tempera-ture trend in spatial and temporal patterns over the
past113 years; (2) studying the contributions of interannual
andinterdecadal oscillations of temperature to the overall
temper-ature change; and (3) quantifying the relationship between
theARNC temperature variation and the PDO, AMO, and DMI atdifferent
temporal scales. Our study deepens the understand-ing of the
temperature changes all over the ARNC in thecontext of global
warming.
2 Data and methods
2.1 Study area and data
The ARNC refers to inland arid areas (between73.5°∼107.2°E and
34.4°∼49.2°N) to the north of theKunlun Mountains and the Qilian
Mountains and to the westof the Helan Mountains, including the
whole territory of theXinjiang Uygur Autonomous Region, the Hexi
Corridor ofGansu Province, the Alashan Plateau of Inner Mongolia,
andthe Ningxia Hui Autonomous Region to the west of theNingxia
section along the Yellow River, of which the landarea accounts for
approximately 1/4 of China’s total area(Fig. 1). The study area is
far from the sea, located in thehinterland of Eurasia, belongs to a
temperate and warm tem-perate arid region with a typical
continental climate, abundantsunshine, large temperature changes,
scarce rain and snow,dry climate, and intense evaporation and is
one of the mostsevere arid areas in the world.
2.2 Datasets
The data used here are the yearly surface air temperature
fromthe gridded CRU TS 3.22 dataset produced by the
ClimaticResearch Unit (CRU) at the University of East Anglia
(http://badc.nerc.ac.uk/view/badc.nerc.ac.uk_ATOM_ACTIVITY_d3fc7c9c-3eb0-11e4-9ec9-00163e251233).
The CRU TS 3.22 dataset covers the period from Jan. 1901to Dec.2013
and provides temperature time series with ahorizontal resolution of
1° × 1°. Moreover, the CRUdataset is also appropriate for studying
the long-termspatial and temporal patterns of climate change in
west-ern China, where minimal observational data are avail-able
before 1951 (Wen et al. 2006). Because we areinterested in
centennial scale warming in the ARNC,the data span adopted for the
trend in this study is from1901 to 2013, with the historical
anomaly based on thenew climate standard period 1981–2010 proposed
by theWorld Meteorological Organization (WMO). Homogeneitytesting
is particularly important when assessing trends.Therefore, a series
of quality control checks was conducted
946 Chen Z. et al.
http://badc.nerc.ac.uk/view/badc.nerc.ac.uk_ATOM_ACTIVITY_d3fc7c9c-3eb0-11e4-9ec9-00163e251233http://badc.nerc.ac.uk/view/badc.nerc.ac.uk_ATOM_ACTIVITY_d3fc7c9c-3eb0-11e4-9ec9-00163e251233http://badc.nerc.ac.uk/view/badc.nerc.ac.uk_ATOM_ACTIVITY_d3fc7c9c-3eb0-11e4-9ec9-00163e251233
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through the RHTest software
(http://etccdi.pacificclimate.org/software.shtml), including checks
and adjustments formultiple change-points (shifts) existing in a
data series thatmay have first-order autoregressive errors (Wang et
al.2010b).
To investigate the oceanic influence on the ARNC temper-ature,
we also selected the PDO index, AMO index, and DMI.The values of
the PDO index and AMO index are used torepresent the strengths of
these two major decadal variabilitymodes in the Pacific and
Atlantic oceans, respectively. TheDMI is used to quantify the
coupled ocean–atmosphere phe-nomenon in the Indian Ocean. The PDO
index wasdownloaded from the University of Washington
(ftp://ftp.atmos.washington.edu/mantua/pnw_impacts/INDICES/PDO.latest),
and the AMO index was downloaded from the EarthSystem Research
Laboratory (ESRL) of the National Oceanicand Atmospheric
Administration (NOAA)
(http://www.esrl.noaa.gov/psd/data/timeseries/AMO/). The DMI is
availablefrom the website of the Japan Agency for
Marine-EarthScience and Technology
(http://www.jamstec.go.jp/frcgc/research/d1/iod/).
2.3 Methods
2.3.1 Ensemble empirical mode decomposition
Here, we used the ensemble empirical mode decomposition(EEMD)
method to diagnose the evolution of warming in theARNC. The EEMD
method proposed by Wu and Huang(2009) is a new time series signal
processing method, whichis suitable for non-stationary and
non-linear signal detectionand can gradually separate the
oscillations at different timescales (intrinsic mode function, IMF)
or the trend componentfrom the original signal (Wu and Huang 2009).
To better un-derstand the EEMD method, the EMD method should be
in-troduced first. The EMDmethod has been developed for non-linear
and non-stationary signal analysis, though only empir-ically. With
the EMD method, a signal is decomposed intoseveral IMFs, and after
EMD processing, the frequencies of
the IMFs are arranged in decreasing order (high to low),
wherethe lowest frequency of the IMF components represents
theoverall trend of the original signal or the average of the
timeseries data. Most importantly, each of these IMFs must
satisfytwo conditions: first, the number of extrema and the number
ofzero crossings must be equal or differ at most by one; second,at
any point, the mean value of the envelope defined by thelocal
maxima and local minima must be zero.
For the original signal x(t), first find out all the local
max-ima and minima, and then use cubic spline interpolation meth-od
to form the upper envelope u1(t) and the lower envelopeu2(t); the
local mean envelope m1(t) can be expressed as
m1 tð Þ ¼ 12 u1 tð Þ þ u2 tð Þ½ � ð1Þ
The first component h1(t) can be obtained by subtractingthe
local mean envelope m1(t) from the original signal x(t),with the
mathematical expression as follows
h1 tð Þ ¼ x tð Þ−m1 tð Þ ð2Þ
If h1(t) does not satisfy the IMF conditions, regard it as
thenew x(t) and repeat the steps in Eqs. (1) and (2) k times
untilh1k(t) is obtained as an IMF.
h1k tð Þ ¼ h1 k−1ð Þ tð Þ−m1k tð Þ ð3Þ
Designate C1 = h1k and select a stoppage criterion definedas
SD ¼XT
t¼0
h1 k−1ð Þ tð Þ−h1k tð Þ�� ��2
h21 k−1ð Þ tð Þ
" #ð4Þ
Here, the standard deviation (SD) is smaller than apredetermined
value. If the above process is repeated toomany times, the IMF will
become a pure frequency modula-tion signal with constant amplitude
in the actual operation,
Fig. 1 Map showing thedistribution of grid points in CRUTS3.22
dataset for the ARNC
Multiscale response of temperature to ocean oscillations 947
http://etccdi.pacificclimate.org/software.shtmlhttp://etccdi.pacificclimate.org/software.shtmlftp://ftp.atmos.washington.edu/mantua/pnw_impacts/INDICES/PDO.latestftp://ftp.atmos.washington.edu/mantua/pnw_impacts/INDICES/PDO.latestftp://ftp.atmos.washington.edu/mantua/pnw_impacts/INDICES/PDO.latesthttp://www.esrl.noaa.gov/psd/data/timeseries/AMO/http://www.esrl.noaa.gov/psd/data/timeseries/AMO/http://www.jamstec.go.jp/frcgc/research/d1/iod/http://www.jamstec.go.jp/frcgc/research/d1/iod/
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possibly resulting in loss of its actual meaning. According
toHuang et al. (1998), SD (generally 0.2–0.3) can be adopted asthe
criterion to stop sifting process. In this study, wedecomposed the
data using EEMD with different SD values(i.e., 0.2 and 0.3) but
found little differences between both. Tofacilitate the analysis,
we only displayed the result of SD = 0.2.Once the first IMF
component is determined, the residue R1(t)can also be obtained by
separatingC1 from the rest of the data,i.e.,
R1 tð Þ ¼ x tð Þ−C1 ð5Þ
By taking the residue R1(t) as new data and repeating
steps(1)–(5), a series of IMFs, namely, C2, C3, …, Cn can be
ob-tained. The sifting process finally stops when the
residue,Rn(t), becomes a monotonic function or a function with
onlyone extremum from which no more IMF can be extracted.Finally,
the original signal x(t) can be reconstructed by nIMFs (i.e.,
Ci(t)) and a residue Rn(t) as follows
x tð Þ ¼Xn
i¼1Ci tð Þ þ Rn tð Þ ð6Þ
Although EMD has many merits, there is a shortcoming ofmode
mixing in EMD. Mode mixing is defined as a singleIMF either
consisting of signals of widely disparate scales or asignal of a
similar scale residing in different IMF components(Wu and Huang
2009). Mode mixing not only causes seriousaliasing in the
time–frequency distribution but also makes theindividual IMF devoid
of physical meaning. To overcome themode mixing problem, the
ensemble empirical mode decom-position (EEMD) method has been
recently developed fornon-linear and non-stationary signal
analysis. The principleof EEMD is simple: adding white noise to the
data, whichdistributes uniformly in the whole time–frequency space,
thebits of signals of different scales can be automatically
de-signed onto proper scales of reference established by the
whitenoise. Although each individual trial may produce verynoisy
results, the noise in each trial is canceled out inthe ensemble
mean of enough trails (Wu and Huang2009). Furthermore, the EEMD
algorithm is straightfor-ward and can be described as follows:
first, add a whitenoise series to the original signal
xi tð Þ ¼ x tð Þ þ ni tð Þ ð7Þ
where xi (t) is the new signal after adding ith white noise to
theoriginal signal data x(t); ni(t) is the white noise. Then,
decom-pose the signal with added white noise into IMFs using
EMDaccording to the steps of Eqs. (1), (2), (3), (4), and (5),
thecorresponding IMF components Cij(t) and residue componentRi(t)
of the decompositions were obtained. Finally, adopt the
means of the ensemble corresponding to the IMFs of the
de-compositions as the final result, namely
C j tð Þ ¼ 1NXN
i¼1Cij tð Þ ð8Þ
where Cj(t) is the final jth IMF component, N is the number
ofwhite noise series, and Cij(t) denotes the jth IMF from theadded
white noise trial. Wu and Huang (2009) noted that theamplitude size
of the added noise exerts little influence on thedecomposition
results on the condition that it is limited, is notvanishingly
small or very large, and can include all possibili-ties. Therefore,
the application of the EEMDmethod does notrely on subjective
involvement; it is an adaptive data analysismethod.
In EEMD, the significance test can be carried out by meansof
white noise ensemble disturbance, to get each IMF credi-bility
(Huang and Shen 2005; Wu and Huang 2009). To de-termine different
scales of IMF components, we examined themore detailed distribution
of the energy with respect to theperiod in the form of the spectral
function. The energy densityof the ith IMF components (Ei) can be
defined as
Ei ¼ 1LXL
j¼1I i jð Þj j
2
ð9Þ
where L is the length of the IMF component and Ii(j) denotesthe
ith IMF component (i.e., Cj). The white noise sequence istested by
the Monte Carlo method (Wu and Huang 2004).Next, before examining
the periods of IMFs, we should listthe properties of an IMF as
follows: an IMF is any functionhaving symmetric envelopes defined
by the local maxima andminima separately, and also having the same
number of zero-crossings and extrema. Based on this definition, we
can deter-mine the mean period of the function by counting the
numberof peaks (local maxima) of the function (Wu and Huang2004).
The mean period of the ith IMF (Ti) can be defined as
Ti ¼ LNPi ð10Þ
where NPi denotes the number of peaks for each IMF. Then,
asimple equation that relates the energy density (�Ei ) and
theaveraged period ( �Ti ) is obtained by
lg�Ei þ lg �Tið Þα ¼ 0 ð11Þ
If we plot lg �Tið Þ α as the x-axis and lg�Ei as the y-axis,
therelation between the energy density and the averaged periodcan
be expressed by a straight line whose slope is −1. The IMF
948 Chen Z. et al.
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component of the white noise series should be distributed onthe
line in theory; however, the actual application produceslittle
deviation, so the confidence interval for the energy spec-trum
distribution of white noise is presented as
lg�Ei ¼ −lg �Tif ga � αffiffiffiffiffiffiffiffiffi2=N
pelg
�Tið Þα=2½ � ð12Þ
In the formula, α is the significance level. At a given
sig-nificance level (e.g., α = 0.05), the energy of IMFs
throughdecomposition is located above the confidence curve,
indicat-ing the periodic oscillation has passed the significance
test; onthe contrary, it is considered less significant.
In addition, to solve the overshooting and
undershootingphenomenon of the impact of the boundary on the
decompo-sition process, mirror symmetric extension (Xue et al.
2013)was used to address the EEMD decomposition
boundaryproblem.
2.3.2 Lagged correlation
Lagged relationships are characteristic of many natural
phys-ical systems. Lagged correlation is important in studying
therelationship between two time series shifted in time relative
toone another. The cross-correlation function of two time seriesis
the product moment correlation as a function of the lag,
ortime-offset, between the series. It is helpful to begin by
defin-ing the cross-correlation function with a definition of
thecross-covariance function. Consider N pairs of observationson
two time series, xt and yt. Following Chatfield (2013), thesample
cross-covariance function is given by
cxy kð Þ ¼ 1NXN−k
t¼1xt−x−
� �ytþk−y−
� �k ¼ 0; 1; ⋅⋅⋅; N−1ð Þ½ �
cxy kð Þ ¼ 1NXN
t¼1−kxt−x−
� �ytþk−y−
� �k ¼ −1;−2; ⋅⋅⋅;− N−1ð Þ½ �
ð13Þwhere N is the series length, x and y are the sample means,
andk is the lag. The cross-correlation function as described byEq.
(13) can be described in terms of Blead^ and Blag^ rela-tionships.
The first part of the equation applies to yt shiftedforward
relative to xt, where yt lags xt. The second part ofequation
describes the reverse situation and summarizeslagged correlations
when yt leads xt. The correlation coeffi-cient rxy(k) can be
defined as
rxy kð Þ ¼ cxy kð
Þffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffifficxx
0ð Þcyy 0ð Þ
p ð14Þ
wherecxx(0) and cyy(0)are the sample variances of xt and yt. Ata
given 5 % significance level, the confidence interval for
rxy(k)is �2=ffiffiffiffiffiffiffiffiffiN−k
p.
3 Results and discussion
3.1 Multi-time scale variations of the regional-averagedsurface
air temperature
3.1.1 Surface air temperature anomaly
As seen in Fig. 2, the surface air temperature in the ARNCover
the past 113 years presents an overall increasing trend.According
to the historical anomaly based on the new climatestandard period
1981–2010, a turning period appeared in thelate 1980s and early
1990s, before which the temperature waslower and after which the
temperature was higher. With re-spect to the time period, the
temperature in the ARNC waslower during 1901–1986, except for
individual years, and hasshown a gradual increase since 1987,
suggesting that the tem-perature in the ARNC has experienced a rise
in the low-temperature period. The temperature in the ARNC was,
over-all, significantly higher during the period 1997–2013
thanbefore, during which the temperature showed a large
interan-nual difference of up to 1.5 °C, implying that the
incidence ofextreme temperature events increased significantly.
Referringto the 5-year moving average of the temperature anom-aly
(Fig. 2), the temperature change is not linear andshows a strong
non-linear variation trend. Furthermore,the stationary test of
temperature series also indicates anon-stationary change in
temperature (test details notshown). Therefore, a non-linear method
should be usedto analyze the non-linear and non-stationary changes
inthe ARNC temperature.
3.1.2 Multi-timescale variations
The EEMDmethod has characteristics of self-adaptability
andlocality in time, which is suitable for time–frequency
analysisof non-linear, non-stationary time series. Therefore,
theEEMD method can be used to decompose a time series ofthe
temperature anomaly in the ARNC during 1901–2013,and five IMF
components (C1-5) and one trend component(R) can be obtained (Fig.
3). Each IMF component reflectsthe fluctuation characteristics from
high frequency to low fre-quency at different time scales, and the
final trend componentrepresents the trend of the original data over
time. Generally,each IMF component has a physical meaning,
reflecting theoscillation of inherently different time scales in
the originalseries. The inherently different time scales can be
determinedby theMonte Carlomethod, and the confidence level
indicates
Multiscale response of temperature to ocean oscillations 949
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the strength of inherently different time scales. As seen inFig.
3 and Fig. 4a, the temperature changes from 1901 to2013 in the ARNC
show inherently different time scales,namely, relatively stable
quasi periodicity; during the studyperiod, the temperature
variation has weak quasi-2–3-year(C1) and significant
quasi-6–7-year (C2) periods at a shortertime scale and unobvious
quasi-14-year (C3) and quasi-24-year (C4) and the strongest
quasi-70-year (C5) periods at alarger time scale. The R shows that
the overall temperaturein the ARNC has been experiencing a
long-term and strongincreasing trend. To validate the credibility
of the abovescales, we also performed REDFIT spectral analysis on
thetemperature anomaly and found that the temperature
showedsignificant periodic variations of 2–3, 6–7, and 70+
years(Fig. 4b). This result is mostly consistent with the 6–7
and
70-year scale characteristics determined by the EEMD meth-od,
with some differences on the 2–3-year scale that is in
closeproximity to the 5 % significance level, as shown in theEEMD
method. In addition, we selected different waveletbases and
decomposition levels for the multiscale decomposi-tion of
temperature anomalies in the ARNC and found that ifdifferent
wavelet bases and decomposition levels are selected,the
decomposition results exhibit apparent differences (notshown),
indicating that the wavelet decomposition is not adap-tive (Bai et
al. 2015). Compared with other methods, such asFourier transform,
wavelet analysis, curvelet transform, andthe moving average method,
the EEMD method has strongerflexibility and adaptability and can
more efficiently extracttrend and periodic information (Huang et
al. 2009; Shaoet al. 2011; Feng and Wu 2014; Ji et al. 2014).
Fig. 2 Change in surface airtemperature anomaly
during1901–2013
Fig. 3 The IMF and trendcomponents of surface airtemperature
anomaly during theperiod from 1901 to 2013
950 Chen Z. et al.
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The multiscale oscillations of temperature in the ARNCreflect
not only the periodic changes of the climatic systemunder external
forcing but also the non-linear feedback of theclimatic system. The
tropospheric biennial oscillation (TBO,with roughly a 2–3-year
period) is the basic characteristic ofthe interannual variation of
the atmospheric circulation, andthe TBO has been significantly
demonstrated in the precipita-tion change in mid-latitude Asia
(Huang et al. 2013). Ourstudy confirmed that the temperature
variability also has asignificant TBO. The fluctuation of the
quasi-6–7-year periodis associated with the El Niño Southern
Oscillation (ENSO)and the North Atlantic Oscillation (NAO),
suggesting that theENSO and NAO events have an obvious impact on
the tem-perature variation in the ARNC. The quasi-14-year and
quasi-24-year periods are consistent with the periodic oscillations
inthe millennial global mean temperature (Qian and Lu 2010)due to
natural changes resulting from external forcing. Thetwo periodic
oscillations are related to solar activity, such asthe
quasi-11-year sunspot activity cycle and the sun’smagnetic field
reversal with the quasi-22-year period,because solar radiation is
the most direct forcing factorresulting in temperature change. The
quasi-70-year peri-od is of the greatest significance at all
assessed timescales and is closely related to the maximum
amplitudeof the earth’s rotation period (approximately 60
years),volcanic activity period (approximately 70 years), andthe
PDO with two general periodicities, one from 15to 25 years and the
other from 50 to 70 years in the20th century (Minobe 1997). The R
indicates an implicit228-year period which is similar to one full
cycle ofhigh tide and low tide approximately every 200
years(Keeling and Whorf 2000). A recent study (Scafetta2014a)
revealed that surface temperature cycling is as-tronomically
induced by gravitational oscillations of thesolar system. This
discovery provides fresh perspectiveon the understanding of
external forcing on global cli-mate change.
To further explain the temporal evolution of the surface
airtemperature in the ARNC, we reconstructed the interannualand
interdecadal oscillations of the ARNC temperature anom-alies.
Figure 5 shows the interannual and interdecadal
temperature variations in comparison with the original
tem-perature anomaly, in which the interannual temperature is
ob-tained by intrinsic mode functions C1 and C2, whereas
theinterdecadal temperature is obtained by intrinsic mode
func-tions C3, C4, and C5 plus R. The reconstructed
interannualvariation represents the fluctuations of the original
tempera-ture anomaly over the study period. Compared to the
originaltemperature anomaly, the reconstructed interdecadal
tempera-ture variation fully reflects the overall trend of the
temperaturevariation from 1901 to 2013 and reveals two important
details,including the high temperature in the 1940s and the
warmestperiod since the 1990s, which are important characteristics
ofthe global land surface temperature change over the past cen-tury
(IPCC 2013).We also evaluated the variance contributionrates of the
interannual and interdecadal temperature oscilla-tions to the
overall temperature variability. The contribution ofthe interannual
oscillation to the overall temperature varianceis 32.45 %, whereas
the interdecadal oscillation contributes upto 67.55 % of the
variance, indicating that the interdecadaloscillation has played a
larger role in warming than the inter-annual oscillation. In
particular, the ARNC has experienced amore notable temperature
change on the interdecadal timescale than the interannual time
scale since the late1990s. As an important background, interdecadal
tem-perature variability is an important time scale and hasa
significant modulating impact on the interannual tem-perature
change. Similarly, interannual disturbance alsoaffects the
interdecadal temperature change. Furthermore, thereconstructed
interdecadal temperature variation also effec-tively shows that the
temperature variation process withinthe study period can be divided
into two distinct variationperiods, with 1987 as the boundary,
before which thetemperature rises slowly and after which the
temperaturerises rapidly, suggesting that the climate mode in
theARNC before and after 1987 changed significantly fromthe cold
period to the warm period, which is coherentwith the results of Shi
et al. (2002). Moreover, althoughthe ARNC rapid warming has a
positive response to globalwarming, the ARNC temperature
fluctuation is not fullyconsistent with the temperature curve all
over the world(IPCC 2013), even in China (Liao et al. 2010).
Fig. 4 a Significance test for theIMF components and b
REDFITspectral analysis on thetemperature anomaly
Multiscale response of temperature to ocean oscillations 951
-
3.1.3 Spatial and temporal varying trend
To reflect how the trend has evolved in spatial and
temporalpatterns, according to the idea proposed by Ji et al.
(2014), wedefined the instantaneous warming rate of the EEMD
trend(WRtrend) at a given time with respect to the reference time
of1901 (Eq. 15).
WRtrend tð Þ ¼ Rn tð Þ−Rn 1901ð Þ½ �= t−1901ð Þ ð15Þ
The instantaneous warming rate can be used to diagnosetemporally
and spatially local quantities. This definition over-comes the
deficiency in the averaged warming rate(slope) of the trend over a
specific time interval inef-fectively reflecting the evolution of
the temperaturetrend and facilitates the comparison of the warming
rate ofthe EEMD trend with the corresponding linear warming rate(Ji
et al. 2014).
The warming rates are shown in Fig.6a–g. Before 1950,there were
both moderate warming and weak cooling regions.Noticeable warming
(>0.1 °C per decade) was mainly locatedin the Taklimakan Desert
of the southwest ARNC, one of themost arid regions of the world.
The most remarkable cooling(approximately −0.05 °C per decade) was
sporadically distrib-uted in the northern ARNC. During the next
three decades, thewarming regions expanded from southwest to north
and east,with the cooling regions shrinking, consistent with the
resultsof Shi et al. (2002). By 1980, almost all of the regions
werewarming. After 1980, the warming regions continued to ex-pand
from southwest to north and east, especially to the north.The
strongest warming occurred in the Taklimakan Desert ofthe southwest
ARNC until 2013. The warming rate and spatialpattern of the EEMD
trend during the period 1901–2013 weresignificantly different from
those of the linear trend over thesame period. The linear trend did
not show the evolution of thewarming pattern, particularly the
later warming, as the EEMDtrend did in the Taklimakan Desert of the
southwest ARNC(Fig. 6h). We found that the temperature variation
trends aredifferent among all the regions and the transition times
of the
variations are also different, indicating that
temperaturechanges are not fully synchronized in the whole
studyarea (Fig. 6a–g). Generally, the temperature changes
arecontrolled by the inherent change mechanism of theclimate system
and the local environment, such as thecomplex topography,
circulation type and strength, andother factors. However, we have
no explanation for whythe temperature variability trend evolved as
shown inFig. 6a–g.
3.2 Possible influences on the ARNC temperature changefrom ocean
temperature anomalies
3.2.1 Multi-timescale responses of temperature to the AMOand
PDO
The PDO index is the leading empirical orthogonal function(EOF)
of the monthly sea surface temperature (SST) anoma-lies over the
North Pacific after the global mean SST has beenremoved; the PDO
index is the standardized principal compo-nent time series (Deser
et al. 2010). The AMO signal is usu-ally defined from the patterns
of SST variability in the NorthAtlantic after linear trends have
been removed (Schlesingerand Ramankutty 1994). As the temperature
was processed, wealso decomposed the PDO and AMO index by the
EEMDmethod. During the study period, the PDO variation had
weakquasi-3-year and quasi-6-year periods at the interannual
timescale in comparison with the interdecadal time scale com-posed
of unobvious quasi-12–13-year, the strongest quasi-42–43-year, and
significant quasi-57-year periods; the AMOshowed significant
quasi-3-year, unobvious quasi-7-year andquasi-16-year, the
strongest quasi-49-year, and significantquasi-57-year periods. To
detect correlations between thetemperature in the ARNC and the PDO
and AMO in-dex, we reconstructed the interannual and
interdecadaloscillations of the PDO and AMO. Figure 7 shows
theinterannual and interdecadal variations of the PDO andAMO, as
well as their linkages to the temperatureanomalies at interannual
and the interdecadal time scalesbased on the cross-correlation
method.
Fig. 5 Interannual andinterdecadal variations of surfaceair
temperature and theircomparisons with the originaltemperature
anomaly
952 Chen Z. et al.
-
As seen in Fig. 7a, during the study period, the rhythms ofthe
temperature fluctuation and the PDO and AMO index areinconsistent
at the interannual time scale, as shown by thecross-correlation
analysis, which indicates that the tempera-ture change lagged the
PDO by four years, with a significantlypositive correlation (Fig.
7b). At the interdecadal time scale,there are only two full PDO
cycles in the past century, includ-ing the Bcool^ PDO regimes
prevailing from 1890 to 1924 andagain from 1947 to 1976 and the
Bwarm^ PDO regimes dom-inating from 1925 to 1946 and from 1977
through the late1990s. The AMO presents a warm phase from 1926 to
1963and two full cold phases covering the periods 1903–1925
and1964–1995(Fig. 7c). Along with the PDO and AMO, the tem-perature
indicates a clear fluctuation of alternating spells ofwarmth and
cold. The warm phase of the PDO and AMOcorresponds to the
interdecadal temperature increase and the
cold phase of the PDO and AMO contributes to theinterdecadal
temperature decline (Fig. 7c). Nevertheless, thetemperature
variation lags the PDO by 3–21 years, with astrong positive
correlation, and the 15–16-year lagged positivecorrelation
coefficient is maximal, reaching 0.347 (Fig. 7d).Even more
surprising, in addition to the significant positivecorrelation year
by year, there is a significant lead–lag positiverelationship
between the temperature variability and theAMO, with lagging of 1–6
and leading of 1–14 years, andthe maximal positive correlation
coefficient is 0.432 for theleading 2nd year (Fig. 7d). Hence, we
concluded that a com-bination of the PDO and AMO played an
important role in theoscillation in the ARNC average temperature.
Moreover, thePDO used to be the powerful controlling factor, but
its impor-tance to temperature variation has decreased since the
early1990s. The temperature change pattern has since been
Fig. 6 Warming rate of surfaceair temperature. a–gInstantaneous
warming rate of thesecular trend in 1950, 1960, 1970,1980, 1990,
2000, and 2013,respectively. h Spatial structure ofthe warming rate
based on thetime-unvarying linear trend overthe whole data domain
from 1901to 2013
Multiscale response of temperature to ocean oscillations 953
-
controlled by the AMO, which contrasts the PDO tendingsharply
downward in recent decades (Fig. 7c). The coolPDO regimes, however,
also offset the AMO influence to alarge extent. Natural cooling in
the Pacific is a principal con-tributor to the recent slowdown in
interdecadal warming in theARNC. Our finding is consistent with
other recent studies,including a study by Trenberth and Fasullo
(2013) showingthat there has been increased subsurface heat burial
in thePacific Ocean during this time frame. Risbey et al.
(2014)demonstrated that the El Niño and La Niña events, tied tothe
predominance of cool PDO regimes, produced thewarming slowdown over
the last decade. However,Karl et al. (2015) recently argued that
there has beenno slowdown in the rise of global mean surface
temper-ature. This study readjusted the data in a way that madethe
reduction in warming disappear, indicating a steadyincrease in
temperature instead. However, the study’sreadjusted data conflict
with many other climate mea-surements, including data taken by
satellites and modelexpectations, so climate scientists do not
agree with theclaim (Trenberth 2015).
The mechanisms for the AMO influence on the climate inEurasia
are not clear; further investigation is needed. At pres-ent, there
are two views: one is that the AMO is causing theair–sea feedback
of the western Pacific (Lu et al. 2006) and thesecond is that the
AMO is leading to Eurasian tropospherictemperature changes by
influencing the atmospheric circula-tion (Li and Bates 2007). Our
study supports the latter; that is,the warm (positive) phase of the
AMO induces a north−southdipole anomaly similar to the summer NAO
pattern, whichmakes the Eurasia troposphere warm. Tropospheric
warmingstrengthens the thermal difference between the ocean
andEurasia, which enhances the westerly circulation and weakens
the East Asian winter monsoon. As a result, the ARNC
tem-perature is higher than the general condition. Previous work(Fu
et al. 2008) showed that the PDO-associated atmosphericcirculation
anomalies in East Asia and climate anomalies inChina are
significant. During a warm PDOphase in the winter,the Mongolian
High tends to be enhanced, whereas theSiberian High is weakened,
indicating an interdecadalseesaw-like oscillation between the two
pressure systems.This pattern suggests that the cold winter wind
from Siberiatends to be weaker, and the air temperature is higher
in north-west China (Li et al. 2012). In summer, when the PDO is in
apositive or warm phase, the western Pacific subtropical high
isweakened and moves toward the southeast. The 500-hPageopotential
height is increased in Mongolia and northwestChina, and a
significant northerly anomaly at 850 hPa prevailsover China, along
with a weakening Asian summer monsoon(Fu et al. 2008). This leads
to anomalous cooling in northwestChina. Furthermore, previous
studies have found that due tothe PDO cold phase being linked to
the La Niña event and thePDO warm phase being associated with the
El Niño event(Gershunov and Barnett 1998; Wang et al. 2012), their
meet-ing makes the La Niña and El Niño events stronger,
whichsignificantly affects climate change (Chen et al. 2013; Wanget
al. 2014). The AMO is closely connected with the NAO;both jointly
contribute to climatic variation (Grossmann andKlotzbach 2009;
Scafetta 2014b). What causes the PDO andAMO by the upwelling and
blocked upwelling of cold deepwater in the Pacific and Atlantic
oceans? In numerous previ-ous studies, researchers (Wyatt and Curry
2014; Goddard2014) found that the phases of two of the Earth’s
major cli-mate systems, the PDO and AMO, are related to changes
inthe Earth’s rotation rate. Other studies (England et al.
2014;Goddard 2014) showed that the PDO and AMO are caused by
Fig. 7 A comparison of a theevolution of the temperature,PDO,
and AMO and b their lead–lag correlations at interannual
andinterdecadal time scales
954 Chen Z. et al.
-
wind divergence over the Pacific and Atlantic oceans,
whereasanother two studies (Keeling and Whorf 2000; Scafetta2014b)
argued that from the decadal to the secular scales,the tide gauge
accelerations oscillate significantly from posi-tive to negative
values mostly followed by the PDO andAMO, especially a large
quasi-60–70-year natural oscillation.In sum, the PDO and AMO result
from a combination ofmultiple physical factors.
3.2.2 Response of temperature to DMI
The DMI is the coupled ocean–atmosphere phenomenon inthe Indian
Ocean, which is normally characterized by anom-alous cooling of the
SST in the southeastern equatorial IndianOcean and anomalous
warming of the SST in the westernequatorial Indian Ocean. We
processed the DMI by theEEMD method and found that the DMI involves
an aperiodicoscillation of the SST in the Indian Ocean, which is
supportedby some other studies (Webster et al. 1999; Saji et al.
1999).Thus, the correlation between the ARNC temperature andDMI was
only investigated by their original sequences. Asnoted in Fig. 8a,
the positive phase with a correspondingcooling of waters in the
eastern Indian Ocean tends to causegreater-than-usual temperature
in the ARNC, whereas thenegative phase of the DMI results in the
opposite conditions,followed by cooler-than-usual temperature in
the ARNC. Thecross-correlation analysis shows that there is an
obviouslypositive relationship between temperature and DMI, with
bothzero delay and lead–lag time (Fig. 8b). Previous studiesshowed
that the DMI affects the surrounding weather condi-tions due to
atmospheric circulation resulting from thecoupled ocean–atmosphere
phenomenon in the Indian Ocean(Ummenhofer et al. 2011; Fierro and
Leslie 2014). When theDMI is in the positive phase with warmer sea
water in the
western equatorial Indian Ocean than in the
southeasternequatorial Indian Ocean, a northward wind is
strength-ened, which picks up moisture carrying heat from
thewestern equatorial Indian Ocean and then passes overthe Tibetan
Plateau toward the ARNC to release heat.In the DMI negative phase,
the pattern of ocean tem-peratures is reversed, which weakens the
northwardwind and reduces the amount of moisture containingheat
picked up and transported toward the ARNC. Theconsequence is that
the ARNC temperature is lower than nor-mal during periods of
negative DMI.
Because the DMI significantly affects the long-term tem-perature
variation in the ARNC, the warming region expan-sion from southwest
to north and east may be associated withthe growing DMI. To verify
this idea, we investigated thecorrelations between the ARNC
temperature and the DMI indifferent study periods. As seen in Table
1, the correlationbetween the temperature in the ARNC and the DMI
becamestronger with expanding study period. Since the 1990s,the
atmospheric moisture and heat content increased sig-nificantly due
to rapid warming in the western equato-rial Indian Ocean, which
enhanced the northward trans-portation of moisture and heat (Shi et
al. 2003). Thischange explains why the warming regions expandedfrom
southwest to north and east in the ARNC. Moreover,the greatest
warming occurred in the TaklimakanDesert of the southwest ARNC,
which is controlled bythe small heat capacity in these regions
(Huang et al.2012; Ji et al. 2014). The warming in the ARNC
isaffected by multiple factors associated with local environmen-tal
driving and remote forcing. We have attempted to explorewhy the
warming regions expanded from southwest to northand east in the
ARNC, but convincing explanations have notbeen discovered.
Fig. 8 A comparison of a theevolution of the temperature andDMI
and b their lead–lagcorrelations
Table 1 Correlations betweenthe temperature in the ARNC andDMI
in different study periods
1901–1950 1901–1960 1901–1970 1901–1980 1901–1990 1901–2000
1901–2013
R 0.002 0.036 0.087 0.059 0.138 0.310 0.411
p 0.990 0.786 0.472 0.601 0.195 0.002 0.000
R correlation coefficient, p level of significance
Multiscale response of temperature to ocean oscillations 955
-
4 Summary and conclusion
The climate system is a complex non-linear system accompa-nied
by a variety of time scales or periodic oscillations. In thisstudy,
we found that the overall temperature in the ARNCshowed a
significant non-linear upward trend during the peri-od 1901–2013,
and its changes clearly exhibited a shorter timescale (quasi-2–3
and quasi-6–7 years) and a larger time scale(quasi-14, quasi-24,
and quasi-70 years). These multiscalefluctuations reflect not only
the periodic changes of the cli-matic system under external forcing
but also the non-linearfeedback of the climatic system. The
variance contributionrates of the reconstructed interannual and
interdecadal temper-ature variations to the overall temperature
fluctuation are32.45 and 67.55 %, respectively, which indicates
that theinterdecadal variation plays a decisive role in warming
andhas a significant modulating impact on interannual
variability.In addition, the reconstructed interdecadal variability
revealedthat the climatic pattern in the ARNC changed
significantlyfrom a cold period to a warm period before and after
1987.
This study confirmed that during the latest five decades,
thewarming regions expanded from southwest to north and
east,especially to the north, and the strongest warming occurred
inthe Taklimakan Desert of the southwest ARNC. The temper-ature
changes are not fully synchronized in the whole studyarea. However,
there is no convincing explanation for why thetemperature
variability trend evolved as shown in this study;further
investigation is needed.
This study showed that the PDO and AMO, which are thestrongest
interdecadal and multidecadal signatures of theocean–atmosphere
systems in the North Pacific and NorthAtlantic, not only have
direct or indirect impacts on theinterdecadal temperature
variabilities in the ARNC but alsoare able to modulate interannual
variabilities (e.g., ENSO andNAO’s impacts), whereas the DMI plays
a more importantrole in warming at the annual scale. In addition,
the PDO usedto be the most powerful controlling factor, but its
importanceto temperature variation has decreased since the early
1990s.The temperature change pattern has since been controlled
bythe AMO. The recent increasing importance of the AMO tothe
temperature rise should lead to a continuation of globalwarming.
The cool PDO regimes, however, also offset theAMO influence to a
large extent. Our findings on the correla-tions between the ARNC
temperature and the PDO, AMO,and DMI suggest that the importance of
oceans cannot beignored when considering climate change. However,
thisstudy only presents some relevant evidence without exploringthe
deeper mechanisms.
Moreover, our study result proved that EEMD is applicableto
non-linear and non-stationary signals and is superior to
thetraditional time–frequency analysis method, such as the wide-ly
applicable Fourier transform and wavelet transform. In cli-mate
change research, the EEMD method can extract reliable
and real signals from the climate time series, especially
theintrinsic time scales of climate change. Furthermore, theEEMD
method can derive the interannual and interdecadalvariation trends
from observation sequences in several yearsand separate the general
trend of climate change from the timeseries of climatological
observations for several years. For anon-linear and non-stationary
sequence of climate change, thelinear trend does not reflect the
sequence change process andfails to reflect the characteristics of
the phase change of timeseries. When trend changes and large-scale
oscillations aremixed, the EEMD method can effectively identify the
large-scale circulation and the non-linear change trends, which
willaid in exploring global and regional climate change issues.
Acknowledgments The research was supported by the National
BasicResearch Program of China (973 Program, No. 2010CB951003),
theNational Natural Science Foundation of China (No. 41501211), and
thePostgraduate Scholarships for Academic Innovation of East
ChinaNormal University (No. xrzz2013023).
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958 Chen Z. et al.
http://dx.doi.org/10.7498/aps.62.109203
Multiscale evolution of surface air temperature in the arid
region of Northwest China and its linkages to ocean
oscillationsAbstractIntroductionData and methodsStudy area and
dataDatasetsMethodsEnsemble empirical mode decompositionLagged
correlation
Results and discussionMulti-time scale variations of the
regional-averaged surface air temperatureSurface air temperature
anomalyMulti-timescale variationsSpatial and temporal varying
trend
Possible influences on the ARNC temperature change from ocean
temperature anomaliesMulti-timescale responses of temperature to
the AMO and PDOResponse of temperature to DMI
Summary and conclusionReferences
