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A remarkable climate warming hiatus over Northeast China since 1998


Abstract and Figures

Characteristics and causes of global warming hiatus (GWH) phenomenon have received much attention in recent years. Monthly mean data of land surface air maximum temperature (Tmax), minimum temperature (Tmin), and mean temperature (Tmean) of 118 national stations since 1951 in Northeast China are used in this paper to analyze the changes of land surface air temperature in recent 64 years with an emphasis on the GWH period. The results show that (1) from 1951 to 2014, the warming trends of Tmax, Tmin, and Tmean are 0.20, 0.42, and 0.34 °C/decade respectively for the whole area, with the warming rate of Tmin about two times of Tmax, and the upward trend of Tmean obviously higher than mainland China and global averages; (2) in the period 1998–2014, the annual mean temperature consistently exhibits a cooling phenomenon in Northeast China, and the trends of Tmax, Tmin, and Tmean are −0.36, −0.14, and −0.28 °C/decade respectively; (3) in the GWH period, seasonal mean cooling mainly occurs in northern winter (DJF) and spring (MAM), but northern summer (JJA) and autumn (SON) still experience a warming, implying that the annual mean temperature decrease is controlled by the remarkable cooling of winter and spring; (4) compared to the global and mainland China averages, the hiatus phenomenon is more evident in Northeast China, and the cooling trends are more obvious in the cold season; (5) the Northeast China cooling trend occurs under the circulation background of the negative phase Arctic Oscillation (AO), and it is also closely related to strengthening of the Siberia High (SH) and the East Asian Trough (EAT), and the stronger East Asian winter monsoon (EAWM) over the GWH period.
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1 23
Theoretical and Applied Climatology
ISSN 0177-798X
Volume 133
Combined 1-2
Theor Appl Climatol (2018) 133:579-594
DOI 10.1007/s00704-017-2205-7
A remarkable climate warming hiatus over
Northeast China since 1998
Xiubao Sun, Guoyu Ren, Yuyu Ren,
Yihe Fang, Yulian Liu, Xiaoying Xue &
Panfeng Zhang
1 23
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A remarkable climate warming hiatus over Northeast
China since 1998
Xiubao Sun
&Guoyu Ren
&Yuyu Ren
&Yihe Fang
&Yulian Liu
&Xiaoying Xue
Panfeng Zhang
Received: 30 November 2016 / Accepted: 18 June 2017 /Published online: 5 July 2017
#Springer-Verlag GmbH Austria 2017
Abstract Characteristics and causes of global warming
hiatus (GWH) phenomenon have received much atten-
tion in recent years. Monthly mean data of land surface
air maximum temperature (Tmax), minimum temperature
(Tmin), and mean temperature (Tmean) of 118 national
stations since 1951 in Northeast China are used in this
paper to analyze the changes of land surface air temper-
ature in recent 64 years with an emphasis on the GWH
period. The results show that (1) from 1951 to 2014,
the warming trends of Tmax, Tmin, and Tmean are
0.20, 0.42, and 0.34 °C/decade respectively for the
whole area, with the warming rate of Tmin about two
times of Tmax, and the upward trend of Tmean obvi-
ously higher than mainland China and global averages;
(2) in the period 19982014, the annual mean tempera-
ture consistently exhibits a cooling phenomenon in
Northeast China, and the trends of Tmax, Tmin, and
Tmean are 0.36, 0.14, and 0.28 °C/decade respec-
tively; (3) in the GWH period, seasonal mean cooling
mainly occurs in northern winter (DJF) and spring
(MAM), but northern summer (JJA) and autumn
(SON) still experience a warming, implying that the
annual mean temperature decrease is controlled by the
remarkable cooling of winter and spring; (4) compared
to the global and mainland China averages, the hiatus
phenomenon is more evident in Northeast China, and
the cooling trends are more obvious in the cold season;
(5) the Northeast China cooling trend occurs under the
circulation background of the negative phase Arctic
Oscillation (AO), and it is also closely related to
strengthening of the Siberia High (SH) and the East
Asian Trough (EAT), and the stronger East Asian winter
monsoon (EAWM) over the GWH period.
1 Introduction
Intergovernmental Panel on Climate Change (IPCC) fifth as-
sessment report indicated that the global land surface is
warming at a rate of 0.0950.107 °C/decade during 1901
2012 (Stocker et al. 2013). Global warming is therefore a
consensus in climatological community. However, Cater
(2006) found that global warming appears to stop or slow
down since 1998. Easterling and Wehner (2009) analyzed
the observational data and indicated that the global land sur-
face air temperature is not significantly warming as expected
during the last decade, with some areas even cooling, and this
phenomenon is called global warming hiatus (GWH). The
GWH, in both global and regional scales, has received a wide-
spread concern in the last years (e.g., Kerr 2009; Franzke
2014; Fyfe and Gillett 2014;Lietal.2015;Anetal.2016).
By analyzing the seasonalcharacteristics of the GWH, Kosaka
and Xie (2013) and Trenberth et al. (2014b) found that the
warming trends in the northern hemisphere significantly slow-
down in winter but continually increase in summer. A recent
analysis showed that the GWH mainly appears in the low and
middle latitudes of global lands, with surface air temperature
*Guoyu Ren
College of Atmospheric Science, Nanjing University of Information
Science &Technology, Nanjing 210044, China
Department of Atmospheric Science, School of Environmental
Studies, China University of Geosciences, Wuhan 430074, China
Laboratory for Climate Studies, National Climate Center, China
Meteorological Administration, Beijing 100081, China
Liaoning Meteorological Bureau, Shenyang 110001, China
Heilongjiang Meteorological Bureau, Haerbin 150001, China
Theor Appl Climatol (2018) 133:579594
DOI 10.1007/s00704-017-2205-7
Author's personal copy
at most stations there witnessing a stable or decreased trend
after 1998 (Sun et al. 2017). However, further studies revealed
that the GWH seems not detectable in change of global ex-
treme temperature event frequency all the time (Seneviratne
et al. 2014). Karl et al. (2015), based on a new land and ocean
dataset, recently also indicated that global surface and land
surface warming had never slowed down, challenging the ex-
istence of the GWH.
In recent years, however, more researchers confirmed
the GWH phenomenon on global and regional scales, and
many works were focused on the possible reasons of the
GWH (Fyfe et al. 2016). A number of studies indicated
that the mechanisms of the GWH may be related to the
external forcing and natural variability. The influence
from external forcing may include the prolonged solar
minimum (Hansen et al. 2011), the increased volcanic
eruptions (Balmaseda et al. 2013;Santeretal.2014),
the reduced water vapor in stratospheric (Solomon et al.
2010,2011), and the increased emissions of anthropogen-
ic aerosols (Lean and Rind 2009). On the other hand,
mechanisms of the natural variability proposed to explain
the GWH phenomenon include the increased ocean heat
uptake especially the layer below 700 m (e.g., Chen and
Tung 2014;Hansenetal.2011; Meehl et al. 2013;
Trenberth et al. 2014a), the cool waters of the equatorial
Pacific surface (Kosaka and Xie 2013), and the Pacific
Decadal Oscillation (PDO) into the negative phase since
1998 (Tollefson 2014). Several recent studies have attrib-
uted the GWH to the 60-year-quasi-periodic natural cli-
mate variability and have provided a theoretical frame-
work for the dynamical processes and the decadal-scale
prediction of temperature variation. Li et al. (2013a,b)
indicated that North Atlantic Oscillation (NAO) leads
the multidecadal variability in Northern Hemispheric sur-
face temperature by about 1520 years through a delayed
effect on the North Atlantic Ocean, and the recent NAO
decadal weakening can be a useful predictor of the hiatus
in both Atlantic Multidecadal Oscillation (AMO) and
Northern Hemispheric mean surface temperature. Sun
et al. (2015) proposed a new Bdelayed oscillator theory^
of the North Atlantic decadal-scale air-sea coupling to
understand the underlying physical mechanisms of the
60-year-quasi-periodic natural climate variability.
For mainland China, annual mean temperature anoma-
lies remains at a high level since 2001, but the warming
trend has also slowed down (Tang et al. 2012). Li et al.
(2015) found that the annual mean maximum temperature
China since 1998, and although the Tmax significantly
increased in summer (JJA), the trend of minimum temper-
ature (Tmin) obviously decreased in winter (DJF). Yan
and Liu (2014) indicated that most of the Tibetan
Plateau still showed strong warming trend since 1998.
Recently, based on the ice-core record, An et al. (2016)
also founded a remarkable cooling phenomenon in north-
western Tibetan Plateau region.
Northeast China has been one of the most significant
warming regions in mainland China and East Asia for
the last more than five decades (Ren et al. 2012;Sun
et al. 2016). Investigation into the spatial and temporal
patterns of the long-term land surface air temperature
change during the GWH period in Northeast China will
help in understanding of the climate forcing mechanism
of the larger-scale GWH. Furthermore, the Northeast
China Plain is one of the most productive regions of
grain in the world. The grain yield in this region
amounts to ~20% of Chinas total. Previous studies
found that the crop production has strong dependence
on the growth season mean temperature and heat condi-
tions (Lobell et al. 2011). The cooling of the warm
season will probably increase the instability of agricul-
tural production in Northeast China (Jin et al. 2002;Liu
et al. 2013a,b). It is thus also important to understand
the characteristics of annual and the warm-season tem-
perature changes in the GWH period in Northeast China,
and to predict the future change in surface air tempera-
ture and heat condition so that an adaption strategy can
be developed to reduce the risk of the summer low
temperature disasters.
In this paper, we analyze the changes in Northeast
China land surface air temperature in recent 64 years,
with an emphasis on the GWH period. The paper is orga-
nized as follows. We describe the data and methods used
in this paper in Section 2after the introduction. Results of
the analysis are presented in Section 3.Abriefanalysisof
circulation background of the warming slowdown is of-
fered in Section 4. We offer a discussion of the results in
Section 5. Finally, main conclusions are presented in
Section 6.
2.1 Data sources and study region
The source of the monthly mean measurements used in
our current analysis is the China Homogenized Historical
Temperature Dataset (CHHTD-V1.0), including 2419 sta-
tions, with a record length of 64 years (19512014),
which included a quality control and homogenization pro-
cedure (Cao et al. 2016). The inhomogeneity problem can
be considered to have little influence on the dataset. The
study area comprises the east region of Inner Mongolia,
Liaoning, Heilongjiang, and Jilin provinces in Northeast
China. In this paper, we initially selected only those na-
tional stations that had at least 70% data coverage since
580 X. Sun et al.
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1951 and at least 20 years of records in the base period
19611990 in the study region. We mainly focused on the
hiatus phenomenon, and therefore we removed the sta-
tions with records of less than 15 years in length in the
GWH period. We also considered the problem of missing
data by discarding the annual (season) value with monthly
records less than 8 (3) months in calculating annual
(seasonal) temperature anomalies. Finally, we obtained a
dataset consisting of 118 stations across the study area for
use in our analysis. There were only 26 stations in 1951,
the number increased to 70 in 1955, and substantially
increased to 118 after 1960. The study region (gray areas),
station distribution (various color points), and the alti-
tudes of the stations are shown in Fig. 1.
This study utilized six circulation indices to analyze the
abnormal characteristics of the atmospheric general circu-
lation (Section 4). The monthly mean indices were from
the National Climate Center (NCC) of the China
Meteorological Administration (CMA), which were man-
aged and regularly updated by the Climate Diagnostics
and Prediction Division of the NCC. The National
Center for Environmental Prediction/National Center for
Atmospheric Research (NCEP/NCAR) reanalysis data, in-
cluding the monthly wind, sea-level pressure (SLP), and
geopotential heights at 500 hPa and 1000 hPa since 1951
with a spatial resolution of 2.5° × 2.5°, were used for
calculating the indices.
2.2 Methods
We constructed regional series by the reference method of
Jones et al. (2001). Firstly, the study area was divided into
a total of 42 grid boxes with the spatial resolution of
2° × 2°, and each grid boxes had at least one station as
showninFig.1(gray areas). Then, gridding of the
temperature anomalies are made by averaging all values
within 2° × 2° grid boxes. Finally, the time series were
constructed by area-weight averaging all the grid boxes
with data using the cosines of the central latitudes of the
grid boxes as weight coefficients.
The analysis used 19611990 as the base period,
mainly because of the better spatial coverage of stations
in the period, and the comparability with previous stud-
ies for surface air temperature. China lies in the north-
ern hemisphere, and therefore seasons were divided into
spring (MAM), summer (JJA), autumn (SON) and win-
ter (DJF, December to February of the following year).
Annual mean values were those from January to
The linear trends of the anomaly series were obtained by
using least squares method to calculate the linear regression
coefficients between temperature and ordinal numbers of time
(e.g., i=1,2,364 for 19512014). The significance of the
linear trends of temperature series was judged by using two-
tailed studentsttest method. In this study, a trend was con-
sidered to be statistically significant if it is significant at the
5% (P< 0.05) level. We also used a nonparametric
KendallstaubasedSens slope estimator (Sen 1968)to
calculate trends, since this method does not assume a dis-
tribution for the residuals and is robust to the effect of
studies of hydrological and extreme climate change (e.g.,
Alexander et al. 2006;Zhaoetal.2016; Kosaka and Xie
2013). The significance of the Sens slope is judged by
using Mann-Kendall test method (Kosaka and Xie 2013;
Zhao et al. 2016).
In Section 4, empirical orthogonal function (EOF)
(Lorenz 1956) and singular value decomposition (SVD)
method (Golub and Reinsch 1970; Lathauwer et al.
2000) were used to analyze the possible influence of the
Fig. 1 Map of study region (gray
shade in bottom right corner)and
location of 118 stations (various
color points). The elevation of
stations is indicated by the
different colors. The grid boxes at
a spatial resolution of 2° × 2°used
for the estimation of regional
average are shown by the gray
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circulation factors on the observed temperature change in
Northeast China.
2.3 Circulation indices
We used six circulation indices in Section 4, and their defini-
tions are as follows:
Arctic Oscillation (AO) index (I
): normalized time
coefficient series of the EOF1 of 1000 hPa height
anomaly field (relative to the 19812010) (2090° N)
(Thompson and Wallace 1998).
Siberia High (SH) index (I
): winter mean sea level pres-
sure anomaly (relative to 19812010) in SH region (4570°
N, 80110° E). The index is the normalized value.
East Asian Trough (EAT) index (I
): difference between
the maximum height and the minimum height on the 500 hPa
geopotential heights in the area of 110170° E and 3055° N.
Station of the East Asian trough (I
): the mean position
of the EAT line on the 500 hPa geopotential heights in the area
of 110170° E and 3055° N.
East Asian winter monsoon (EAWM) strength index
IEAWM ¼U500 25-35-N;80-120-EðÞ
U500 50-60-N;80-120-EðÞ
where U500 25-35-N;80-120-EðÞ
and U500 50-60-N;80-120-EðÞ
the mean values of 500 hPa zonal wind in their respective
regions. The index is the normalized value.
Meridional index over Asia (I
): we firstly divided the East
Asian region (4565° N, 60150° E) into three areas (6090°
E, 90120° E, 120150° E), and then calculated I
for each
area according to formula (2):
Fig. 2 Linear trends of
temperature series for different
periods (19512014, 19511997,
and 19982014). Figure on the
left shows the annual mean time
series (°C, black lines)ofTmax
(a), Tmin (b), and Tmean (c),
anomalies relative to 19611990.
Figure on the right shows the
linear trends for the period 1951
2014 (°C/decade, red bars),
19511997 (°C/decade, orange
bars), and 19982014 (°C/
10 years, blue bars). Error bars
indicate two times the standard
deviation. Statistically significant
(P< 0.05) trends are marked with
shows the interdecadal mean
temperature anomalies of Tmax
(red lines), Tmin (blue lines), and
Tmean (black lines)(d)
582 X. Sun et al.
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where nis the number of partitions within the study region; m
is the number of φ(here m=3);Δλ= 15° longitude; φ
and φ
are 45° N, 55° N, and 65°N respectively; and Δz
is the
difference between the height of every 15° (Δλ) longitude.
Fig. 3 Trends of Northeast China
temperature in specific periods
(19512014 and 19982014).
Figure on the left shows the
spatial distribution of the Tmax
(a), Tmin (c) and Tmean (e)
trends for 1951201. Figure on
the right shows the spatial
distribution of the Tmax (b),
Tmin (d) and Tmean (f) trends for
19982014.The details above
each plot indicate the different
periods and the trends of regional
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3.1 Annual temperature changes
The annual mean time series of Tmax, Tmin, and mean tem-
perature (Tmean) anomalies are shown in Fig. 2ac. For the
study period (19512014), annual mean temperature anoma-
lies reflect significant positive trends (P<0.05),andthe
warming rates of Tmax, Tmin, and Tmean are 0.20, 0.42,
and 0.34 °C/decade, respectively, over the study region (red
bars in Fig. 2a, b, c). The warming rate of Tmin is about twice
that of Tmax, which suggests that the warming in recent de-
cades can be attributed to stronger increases in Tmin. It is also
clear that the warming trend of Tmean in the study region is
larger than that of mainland China (Ren et al. 2005;Lietal.
2015) and global average (Jones et al. 2001). Based on the
time series, we further found that 2007 was the warmest year
in the study region since 1951. For the period 19511997, the
warming rates of annual temperature are larger than that in
whole study period (orange bars in Fig. 2a, b, c). The decadal
mean of temperature anomalies are shown in Fig. 2d, which
clearly indicates the inter-decadal variation in temperatures.
The decadal mean anomalies of Tmin are slightly lower than
those of Tmax before 1980, but considerably higher than
Tmax after 1980. The anomaly of Tmin clearly increased from
the late 1970s, while Tmax and Tmin significantly increased
from the late 1980s. Previous studies have indicated that sig-
nificant warming began at the majority of the stations in main-
land China in the mid-1980s (Ren et al. 2005; Tang et al.
2005). All of the above analyses reveal that the significant
warming occurred in the study region later than in most parts
of China.
In the GWH period, annual temperatures show clear nega-
tive trends (blue bars in Fig. 2ac). The cooling trends since
1998 in Tmax, Tmin, and Tmean respectively are 0.36,
0.14, and 0.28 °C/decade. These negative trends may be
largely attributed to several cold years around 2010, particu-
larly 2010 and 2012, as shown in Fig. 2a, b, c. Remarkably, all
of the trends in the hiatus period are not significant (P<0.05),
with a higher level of uncertainty (black error bar in Fig. 2a, b,
c), which can mainly be attributed to the short series length.
Overall, a remarkable cooling phenomenon in the GWH peri-
od occurred over the study region, and Tmax showed more
obvious cooling trends than Tmin and Tmean.
Spatial patterns of the decadal trends in temperature in the
study region are shown in Fig. 3. From 1951 to 2014 (Fig. 3a
e), all of the grids consistently show a warming spatial pattern,
however, with different warming rates. The warming rates of
Tmin and Tmean were above 0.4 °C/decade, and the larger
change occurred in the region of north of 46° N and the
Mongolian Plateau. Although Tmax also exhibits widespread
warming, the warming rate was considerably lower than Tmin
and Tmean. The spatial pattern of Tmax indicates an increase
in the warming rate of Tmax with altitude increase. This result
is in agreement with Dong et al. (2015), who showed that the
temperature trend increased from 200 to 2000 m with altitude
In the GWH period, the majority of grid boxes exhibit
widespread cooling trends, except some grid boxes in the
north of the study region (Fig. 3bf). Moreover, the trends
of Tmax and Tmean show a wider cooling range than Tmin.
More than ~90% of the grid boxes show decreasing trends in
Tmax and Tmean, while ~30% of the grid boxes show
warming trends of Tmin. In general, the above evidence sug-
gests that the hiatus phenomenon that has occurred since 1998
can be more strongly attributed to the substantial decreases in
Tmax than to decreases in Tmin.
3.2 Seasonal temperature changes
Decadal trends of seasonal temperature during the different
periods are shown in Fig. 4. During 19512014 (Fig. 4red
bar), Tmax, Tmin, and Tmean consistently show significant
warming trends (P< 0.05), and the warming rate in winter and
spring are larger than in summer and autumn. The warming
rate of Tmin was significantly higher than that of Tmax. In the
GWH period (Fig. 4, blue bar), hiatus phenomena were ob-
served in spring, summer, and particularly in winter with a
winter cooling rate lower than 0.8 °C/decade. Conversely,
the warming rate in autumn was higher relative to the recent
64 years. In general, the cooling trends of annual temperature
in Northeast China were mainly controlled by the cooling
trends in winter and spring during the GWH period, indicating
that the warm and cold seasons have become more polarized.
In addition, all the trends in the GWH period are associated
with a higher level of uncertainty (black error bar in Fig. 4).
Spatial patterns of Tmean seasonal trends in the GWH
period and the whole period are shown in Fig. 5.Becauseof
the similar spatial distribution among Tmax, Tmin, and
Fig. 4 Decadal trends (°C/decade, red and blue bars)ofTmax(left),
Tmin (middle), and Tmean (right). Statistically significant (P< 0.05)
trends are marked with asterisks.Error bars indicate two times the
standard deviation
584 X. Sun et al.
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Tmean, we discuss only the spatial pattern of Tmean. From
1951 to 2014, greater warming phenomenon in spring and
winter occurred through most of the grid boxes. Conversely,
in the GWH period, almost all of the warming grid boxes in
Fig. 5 Season trends of
Northeast China Tmean change in
specific periods (19512014 and
19982014). The details above
each plot indicate the different
periods and the trends of regional
mean. Figure on the left (a, c, e, g)
shows the season trends for 1951-
2014. Figure on the right (b, d, f,
h) shows the season trends for
A remarkable climate warming hiatus 585
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the whole period showed cooling trends in spring and winter.
The largest decrease of spring mean temperature occurred in
central and southern parts of the study region. In summer and
autumn of the GWH period, the trends increased from slight
cooling to considerable warming with increased latitude. In
autumn, the warming trends in the GWH period were consid-
erably larger than in the past 64 years, especially in the areas
north of 46° N.
3.3 Monthly temperature changes
Linear trends of monthly Tmax (a), Tmin (b), and Tmean (c)
in the whole period and the GWH period are shown in Fig. 6.
From 1951 to 2014, warming trends were observed in all
months, and the warming rates during January to June were
larger than those during July to December. February showed
the largest warming trend and the lowest change usually oc-
curred in December. During the GWH period, there were
7 months that consistently showed cooling trends, which is
more than the warming months. The larger cooling trends
mainly occurred from February to April, and the largest
warming month was November. Moreover, all of the trends
in the GWH period were associated with a high error level.
The spatial patterns of Tmean trends for February and
November in the whole period (a, c) and the GWH period
(b, d) are shown in Fig. 7. In the GWH period, the spatial
distribution of November (d) and February (b) show opposite
patterns. In February, all the grid boxes showed cooling trends
with a cooling rate lower than 0.8 °C/decade. However, in
November, all the grid boxes show greater warming trends
especially north of 46° N where the warming rates even
reached 2.0 °C/decade.
3.4 Comparison of the trends based on different trend
We used Sens slope and least squares estimator to assess the
linear trends of the full study period (19512014) and the
GWH period (19982014), and to compare the results based
on the two methods. Table 1shows the differences between
Sens slope trends and least squares trends. For the period
19512014, there are almost no differences on the trends ob-
tained using the two estimation methods. The trends for hiatus
period by using the Sens slope method are slightly lower than
that by using the least squares method, but all the results show
consistent hiatus phenomenon over Northeast China (Table 1).
For statistical significance, both of the two estimation methods
show significant long-term trends for the entire period, and
non-significant trends of the hiatus period. Therefore, the
two methods have little impact on the estimated results of
linear temperature trends and their significance over
Northeast China, but the magnitudes of the trends do have
slight differences, and this may call for a caution in estimating
the temperature trends using the least squares method.
3.5 Comparison of Northeast China, mainland China,
and global analyses
Tab le 2gives the annual mean surface temperature trends in
Northeast China (Section 3.13.3), mainland China (Li et al.
2015), and the global averages (Karl et al. 2015;Kosakaand
Xie 2013). Because these studies used different datasets and
base periods, it only shows an approximate comparison of the
temperature trends.
Global analysis results suggested that the annual mean tem-
perature increase was significant during 1998 to 2014 over the
Fig. 6 Monthly trends (°C/decade, red and blue bar)ofmeanTmax(a),
Tmin (b), and Tmean (c). Statistically significant (P< 0.05) trends are
marked with asterisks.Error bars indicate two times the standard
586 X. Sun et al.
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global land surface (Karl et al. 2015). In Northeast China, how-
ever, the annual mean surface air temperature showed a cooling
trend over the GWH period. This illustrates that there remains a
remarkable hiatus phenomenon at the regional scale, in areas
such as Northeast China. For the seasonal hiatus, the compari-
son between Northeast China and the globe (Kosaka and Xie
2013) reveals that both consistently showed cooling trends dur-
ing DJF and MAM, and the season with the largest cooling
trends was DJF. During JJA, global and Northeast China con-
sistently showed warming trends. However, during SON, the
global result showed a slight cooling trend, but Northeast China
exhibited a greater warming trend.
Comparison of the temperature changes between mainland
China (Li et al. 2015) and the study region for annual mean
Tmax and Tmin indicated a better agreement in the GWH
period, with both showing remarkable cooling trends. For
seasonal analysis, the negative trends in Northeast China were
larger than those in mainland China during MAM. During
SON, the most significant difference between mainland
China and Northeast China was in Tmax, and there was clear
warming in Northeast China but slight cooling over mainland
China. In general, this analysis illustrates that the hiatus phe-
nomenon was more evident in Northeast China, and the
cooling trends were clearer in the cold season.
4 Circulation anomaly during the GWH
Sections 3.2 and 3.3 indicated that the decreasing trend of
annual mean temperature in Northeast China during the
GWH period is mainly controlled by the cooling of winter
and spring, especially the winter cooling. In this section, we
further analyzed the relationship between the warming hiatus
of winter over Northeast China with the atmospheric circula-
tion anomaly in larger spatial scale.
EOF method was used to analyze the winter temperature of
Northeast China during the period 19512014. The results
showed that the first mode contribution was 80%, and the
temperature in Northeast China region shows a spatially con-
sistent change. This result is similar with the previous research
by Fang et al. (2013). It shows that the winter temperature in
Northeast China shows the characteristics of synchronous
We used SVD method to analyze the correlations of the
winter mean temperature in Northeast China with the SLP
field, the 500 hPa geopotential height field, and the 500 hPa
zonal wind field. The variance contribution of SVD for SLP
field, 500 hPa geopotential height field, and 500 hPa zonal
wind field are 91, 90, and 85% in the first mode components,
respectively, with the correlation coefficients of the
Fig. 7 Tmean trends of
February (a, b) and November (c,
d) temperature over Northeast
China in specific periods (1951
2014 (a, c) and 19982014 (b, d).
The details above each plot
indicate the different periods and
the trends of regional mean
A remarkable climate warming hiatus 587
Author's personal copy
normalized time coefficient series between the three fields and
the winter temperature in Northeast China being 0.79, 0.7, and
0.78 (P< 0.01), respectively. These illustrate that there are
good relationships between winter temperature and SLP field,
500 hP a geopotential height field, and 500 hPa zonal wind
The distribution map of heterogeneous correlation for SVD
first mode between the winter temperature field in Northeast
China and SLP field, 500 hPa geopotential height field (Fig.
8a, b) show that there are two significant correlation regions.
The Arctic region shows a significant negative correlation,
and the middle latitude regions show significant positive cor-
relation. The two regions are remarkably related to the con-
trolling area of AO and SH. The distribution of heterogeneous
correlation between zonal wind field and the winter tempera-
ture field (Fig. 8c) shows that the two significant correlation
regions are consistent with the regions used to define the
EAWM index by Zhu et al. (2008).
Overall, all above analyses indicate that there are good
relationships between AO, SH, EAWM, and the winter tem-
perature in Northeast China. If AO is in its positive (negative)
(weaker) than normal, the winter mean temperature will be
anomalously warm (cold) over Northeast China. Besides,
EAT is also an important system related to cold wave in
Northeast China and is closely related to the EAWM (Ding
et al. 2014). Several previous analyses of the EAT indicated
that its strengthening (weakening) is always accompanied by
cold (warm) winter in Northeast China (Chen et al. 2014;
Leung and Zhou 2015a). At the same time, the mean position
of the EAT can also influence the winter temperature of east-
ern China (Leung and Zhou 2015b).
Tabl e 1 Trends of temperature in different periods over Northeast China based on different trend estimation methods
Regions Periods Elements
The red numbers represent Sens slope trends, and the blue numbers represent least squares trends (unit: °C/decade)
The trends are significant at the 5% level
588 X. Sun et al.
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These results are generally consistent with the previous
studies (Sun and Li 2012; Li et al. (2013a,b); Ding et al.
2014; Liu et al. 2013a,b;Fangetal.2013). Fig. 9shows the
decadal mean of temperature anomalies in Northeast China
(a), mean position of the EAT (I
) (b), EAT index (I
(c), Meridional Index (I
) (f), and EAWM index (I
) (g) for DJF (the arrows
represent increasing or decreasing trends). It is clear that all of
the indices trends have reversed during the 1990s. During the
1990s, I
was located in the Japan east Pacific (about
148°E), and the I
and I
were consistently in a weak state,
limiting the cold air of the polar region from entering
Northeast China, with a result of abnormal high winter
mean temperature in the study area (Fig. 2a). Decadal
mean values of 2000s showed that I
moved west-
ward to Japan (about 144° E), closer to Northeast
China. Moreover, the I
and I
changed from weak
phases in the 1990s to strong phases, AO changed to a
negative phase, and SH, EAWM, EAT, and meridional
wind are all consistently in their strong phases. The
circulation abnormality benefited the more frequent
bursts of the winter cold waves and the decrease of
seasonal mean temperature in Northeast China during
the GWH period.
Tabl e 2 Comparison of Northeast China, mainland China (Li et al. 2015), and global (Karl et al. 2015; Kosaka and Xie 2013) temperature changes for
the GWH period
Sources Regions Periods Data sets Methods Elements
Karl et
al. (2015)
Kosaka and
Xie. (2013)
Li et
al. (2015)
Data set
This paper
19982014 CHHTD-V1.0
<0.25 <<0.0 <<0.25<
The red (blue) arrows indicate increasing (decreasing) trends, and the number of arrows indicates the range of trend values (see the legend below the
ISTI Databank v1.0.0 International Surface Temperature Initiative Databank v1.0.0, HadCRUT v Hadley Centre-Climate Research Unit com-
bined land surface air temperature v, CHTD China Homogenized Temperature Data set (total about 860 stations in China), CHHTD-V1.0 China
Homogenized Historical Temperature Data set-V1.0 (total 2419 stations in China)
A remarkable climate warming hiatus 589
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Figure 10 shows a schematic diagram of the possible mech-
anism of winter warming hiatus phenomenon in Northeast
China. The hiatus phenomenon appears under the background
of negative AO and the stronger SH, EAWM, EAT, and me-
ridional wind. Under the synergistic effects of the atmospheric
circulation factors, the cold air of the polar region more easily
moved into Northeast China, and the winter temperatures cor-
respondingly experienced the cooling phenomenon.
Most observational data used in this paper had a relatively
long and high-quality record, especially after 1960.
However, the station spatial coverage was poorer during
19511960. Therefore, the temperature anomalies calculated
for earlier period (19511960) have a larger uncertainty.
Furthermore, the poorer data coverage can also impact the
long-term trend estimate (Brohan et al. 2006), leading to
uncertainties of the trend values in the 19512014 period.
However, the uncertainties of the trend estimates are relatively
small by comparing the previous analyses of both the study
region and mainland China. In addition, this work was fo-
cused on the GWH period, and the temperature trends during
the whole time period could be regarded as a background
change. In the last 50 and 20 years, the quality and spatial
coverage of the data are better than the early data, so the bias
from data could not substantially impact the final results.
Overall, our analysis result confirms a remarkable warming
slowdown in Northeast China during the GWH period, and
the hiatus is more obvious than those reported for mainland
China and the globe. This is in a strong contrast to the fact that
Northeast China had experienced the most significant
warming in mainland China and East Asia by late 1990s
(Hansen et al. 2006; Ren et al. 2012; Jones et al. 2012). It
would be interesting to examine whether or not the other rap-
idly warming regions of the global lands also see a larger
decline of surface air temperature during the GWH period.
SLP 500 hPa geopotential heights
500 hPa zonal wind
Fig. 8 The distribution of heterogeneous correlation for SVD first mode
between the winter mean temperature field in Northeast China and SLP
field (a), 500 hPa geopotential heights field (b), and 500 hPa zonal wind
field (c). The shaded areas represent those with significant correlations,
and ±0.27 0.35) indicate that the correlations are significant at the 0.05
(0.01) level
590 X. Sun et al.
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In Section 4, the direct influential factors of the Northeast
China temperature decline during the GWH period was inves-
tigated in terms of large-scale atmospheric circulation anom-
aly. We just founded out the factors of winter temperature
change in Northeast China. In reality, however, there remains
a complex physical mechanism between EAWM and the EAT
(Leung et al. 2016), and studies have also indicated that a
range of factors affecting the EAT and EAWM. Wu and
Wang ( 2002) analyzed the winter AO, SH, and EAWM and
indicated that, compared with impacts of the winter AO, the
SH showed more direct and significant impacts on the EAWM
and EAT, and this conclusion was confirmed by the other
studies (e.g., Gong et al. 2001; Li and Wu 2012; Wu and
Wan g 2002;SunandLi2012). The negative temperature
anomalies in the southern part of the EAT could also be rea-
sonably explained by the phase of the El NiñoSouthern
Oscillation (ENSO) (e.g., Leung et al. 2016; Wang and He
2012;Zhouetal.2013). Besides, Clark and Serreze (2000)
found that snow depth and extents over land can affect the
Pacific Ocean temperature and thus affect the EAT and
EAWM. Recently, Qiao and Feng (2016) found that a signif-
icant positive correlation exists between the December NAO
Fig. 9 Decadal mean of
temperature anomalies of
Northeast China (aC),I
°E), I
(c), I
(d), I
(e), I
(f), and I
(g) indices for DJF
(the arrows represent increasing
or decreasing trends)
A remarkable climate warming hiatus 591
Author's personal copy
and the following February EAT, and much of China features
cold anomalies with significant signals observed over
northeastern China in strengthened EAT and NAO years.
Huang et al. (2013) analyzed the variations of the intensity
of EAT and indicated that the variation of EAT may be related
to the eastward propagation of Rossby wave. Above studies
illustrate that the physical mechanisms for the hiatus phenom-
enon in Northeast China are complex, and the interactions
among the atmospheric, oceanic, and land surface factors
and the fundamental mechanisms still require further
Whatever the reason for the warming slowdown, the
cooling climate in Northeast China during the past 17 years
may pose a huge challenge to the agricultural activity and
natural system. Northeast China plain is one of the most pro-
ductive regions of grains in the world. The grain yield
amounts to ~20% of Chinas total. The growth season here
mainly concentrates in MarchOctober, but the cold weather
in spring and summer frequently causes loss of grain produc-
tion in the region. Our analysis in Section 3shows that the
hiatus phenomenon is more evident in Northeast China espe-
cially in the cold season (MAM and DJF). The cooling trends
in MAM signified that the accumulated temperature and heat
conditions in the growth season may have worsen during the
last two decades. It is worth noting if the cooling trend will
continue in decades to come, with Li et al. (2013a,b), Sun
et al. (2015), and others thought that it is very likely. If it will
continue, the instability of agricultural production will in-
crease in the future in this important grain production region
of mainland China. This is especially anxious considering the
widely hold expectation that climate will continue to become
warmer in the decades to come without doubt in Northeast
It is also interesting to note that the phonological
rhythms of a few plants seem to adjust to changed cli-
mate condition in Northeast China (Zhao 2016). An
example comes from a species of willow and Salix
matsudana, which has exhibited a delayed dates of
leafing and flowering in spring since late 1990s (Zhao
2016). Obviously, further investigation is needed to ex-
amine the phonological and vegetation response of other
species to the recent change in surface air temperature
in Northeast China.
By applying observational data of monthly mean land surface
Tmax, Tmin, and Tmean of 118 national stations since 1951,
we investigated spatial and temporal pattern of temperature
change for the periods 19512014 and 19982014 in
Northeast China. Main conclusions can be drawn as follows:
(1) The increasing trends of Tmax, Tmin, and Tmean were
0.20, 0.42, and 0.34 °C/decade, respectively, over the
period 19512014 in Northeast China, with the rate of
Tmin about twice that of Tmax, and the upward trend of
Tmean was clearly higher than the mainland China and
global averages. The long-term warming occurred con-
sistently across the study region, but the largest decrease
of spring mean temperature mainly occurred in central
and southern parts of Northeast China.
(2) In the GWH period, the annual mean temperature con-
sistently exhibited a cooling trend in Northeast China,
and the region-averaged annual trends of Tmax, Tmin,
and Tmean were 0.36, 0.14, and 0.28 °C/decade,
(3) Seasonal mean temperature significantly decreased in
winter and spring during the GWH period, but the
warming was still evident in summer and autumn, indi-
cating that the annual mean temperature decrease was
mainly dependent on the remarkable winter and spring
cooling in the study area.
(4) The monthly mean temperature series of Northeast
China showed cooling trends in most months, with the
largest decline occurring in February; however, the
November mean temperature still showed a significant
warming trend.
Fig. 10 A schematic diagram of
the mechanism of winter warming
hiatus phenomenon in Northeast
China during 19982014
592 X. Sun et al.
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(5) Compared with the global and mainland China land sur-
face temperature changes, the hiatus phenomenon in
Northeast China was more evident, and the cooling
trends were most evident in the cold season including
winter and spring.
(6) The Northeast China cooling trend occurs in the time
period of the negative phase Arctic Oscillation, and it is
also closely related to strengthening of the SH and the
EAT. The EAWM obviously becomes stronger over the
GWH period as a result of the abovementioned changes
in the circulation indices.
Acknowledgements This study is financed by the China Natural
Science Foundation (CNSF) (Fund No: 41575003) and the Ministry of
Science and Technology of China (MOST) (Fund No:
GYHY201206012). The authors also thank two anonymous reviewers
for their constructive comments.
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... Our research used homogenized data to focus on the change of GST-SAT in the warm season in China mainland in the last 60 years. A remarkable change or regional warming slowdown occurred in the period since 1998 (2000) [55]. The updated analysis of the whole China mainland using homogenized data would be also relevant to understand the new features of regional climate change. ...
... In addition to the possible influence of urbanization on the observed GST-SAT trends, the larger increase in the warm season GST-SAT over the north than the south also implies a role played by changes in climate and geographical conditions. For example, it was well known that the north including Northeast China and Northwest China widely experienced a more rapid climate warming during the last 50-60 years [35,55,69]. Our analysis result in this study indicates an asymmetric warming of GST and SAT in the north, with the GST seeing a more rapid increase than the SAT. ...
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Examining large-scale characteristics of the difference between ground surface temperature (GST) and surface air temperature (SAT) and its long-term trend will help understand land surface energy exchange and the effect of land-atmosphere interaction on climate change and variability. Based on a homogenized monthly dataset of GST and SAT from 1961 to 2018, this study analyzes the spatial distribution and long-term trend of the difference between ground surface temperature and surface air temperature (GST–SAT) in the warm season (April to October) over China mainland. The results show that the warm-season mean GST–SAT in the Qinghai-Tibet Plateau and the northwestern deserts have the largest GST–SAT. On average, the GST–SAT in China is the greatest in summer, with the maximum monthly value occurring in July. During 1961–2018, the warm-season mean GST–SAT undergoes a significant increasing trend (0.04 °C/10yr, p < 0.01), with the largest increase seen in mid-late spring (April and May), and the smallest increase in August. Spatially, the GST–SAT increases significantly in the northern region, decreases slightly in the southern region, and remains unchanged in the Qinghai-Tibet Plateau. The warm-season mean GST–SAT is significantly positively correlated with altitude and sunshine duration (R = 0.50, 0.40; p < 0.05), and significantly negatively correlated with relative humidity and precipitation (R = 0.48, −0.42; p < 0.05), in the country on a whole in the analysis period.
... The long-term change in the sea ice extent in the marginal seas of East related to the overall trend of climate warming in East Asia and North Chin ever, the time scale of the data in this study was short, and climate warming Asia slowed down during the analysis period [35], which may be the main r insignificant long-term trends of some sea ice indicators. Obtaining a longer s resolution sea ice extent data and discussing the response mechanism of t changes and variations in the sea ice extent in the marginal seas of East As warming and natural climate variability are work that needs to be strengt ...
... The long-term change in the sea ice extent in the marginal seas of East Asia may be related to the overall trend of climate warming in East Asia and North China [34]. However, the time scale of the data in this study was short, and climate warming in Northeast Asia slowed down during the analysis period [35], which may be the main reason for the insignificant long-term trends of some sea ice indicators. Obtaining a longer series of high-resolution sea ice extent data and discussing the response mechanism of the long-term changes and variations in the sea ice extent in the marginal seas of East Asia to climate warming and natural climate variability are work that needs to be strengthened in the future. ...
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Using multisource sea ice fusion data, the spatiotemporal characteristics of sea ice cover were analyzed for the marginal seas of East Asia for the period 2005–2021. The results show that there were obvious differences in the beginning and end dates of the sea ice in the different sea areas. The northern Sea of Japan had the longest ice period, and Laizhou Bay and Bohai Bay in the Bohai Sea had the shortest ice period. The time when the largest sea ice extent appeared was relatively stable and mostly concentrated in late January to mid-February. There were obvious spatial differences in the duration of the sea ice cover in the marginal seas of East Asia. The duration of the sea ice cover gradually decreased from high latitude to low latitude and from nearshore to open seas. The annual average duration of the sea ice cover was more than 100 days in most of the Sea of Japan and approximately 20 days in most of Laizhou Bay and Bohai Bay. The melting speed was significantly faster than the freezing speed in the Bohai Sea and Yellow Sea, resulting in asymmetric changes in the daily sea ice extent in the two seas. The increasing trends in the maximum sea ice extent and total sea ice extent were 0.912 × 105 km2/10 yr and 0.722 × 107 km2/10 yr, respectively, from 2005 to 2013, both of which passed the significance test at the 0.05 level.
... In the MLYRB, LDFAI and AO showed significant oscillation cycles of about 8a, 5a, and 2-5a during 1969-1998, 1980-1986, and 2010). For short-scale DFAA, PDO had significant periods of 2-4a, 9a, and 3a during 1981-1991, 1990-2006, and 2008-2011, respectively, which passed the red noise test with a confidence level of 95% ( Fig. 10(b)). ...
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As an emerging disaster, the drought-flood abrupt alternation (DFAA) may cause unprecedented socio-economic impacts under changing environment, which has attracted extensive attention in recent decades. DFAA involves drought to flood (DTF) and flood to drought (FTD). However, thus far, little effort has been made to identify DFAA with high spatial resolution. Moreover, few studies have fully revealed the driving mechanisms of DFAA by large-scale climate factors. Here, the Yellow River Basin (YRB) was selected as the research area, which is an important agricultural base in China. The spatiotemporal characteristics of DFAA at multiple time scales during flood season were analyzed using 0.25° grid precipitation from 1961 to 2020 in the YRB. Furthermore, the Pearson correlation method and cross wavelet method were used to investigate the relationship between circulation anomaly (such as Arctic oscillation (AO), Pacific decadal oscillation (PDO), El Niño Southern Oscillation (ENSO), and sunspot) and DFAA to explore the potential causes of DFAA in this region. The results demonstrated that: (1) FTD trend in the YRB is serious, and the short period of FTD trend is June-July > July-August > August-September; (2) spatially, the high-frequency long-period DFAA was distributed in the whole YRB, while the DFAA in June-July and July-August were concentrated in the center of the YRB; (3) AO and PDO are the key factors to induce DFAA in the YRB, especially the changes of AO and PDO phase. This study helps improve our understanding of the relationship between DFAA and large-scale climate factors and provides new insights for future disaster assessment.
... Given the recently decreasing trend of mortality due to insect damage in Northeast China (Zhang et al., 2014), drought events may directly trigger mortality (Gazol and Camarero et al., 2022). This is particularly true in areas at lower latitudes where winter temperatures have shown a greater increase that in the rest of the region (Sun et al., 2018). ...
Given the significantly increasing frequency and intensity of droughts in Northeast China under climate change, it is necessary to understand drought-induced mortality in forest ecosystems. The magnitude of drought-induced forest mortality strongly depends on climate variability and forest age, which are both important driving factors of tree mortality. However, the age-related patterns and climatic driving factors of drought-induced forest mortality at large scales are poorly understood. This study identified the age-related pattern and the key climatic driving factors of drought-induced mortality for 17 dominant tree species in Northeast China using rain-use efficiency (RUE), standardized precipitation evapotranspiration index (SPEI), and tree-list information (species and diameter for every tree). Considering that climate variability and forest age are important factors of drought-induced mortality, species-specific analysis was conducted for describing age-dependent mortality patterns. The results showed that the mean annual rate of the drought-induced mortality of forests in Northeast China was 0.49%, with relatively high mortality rates in the Changbai Mountains and the eastern part of Liaoning Province. The sensitivity of age-dependent mortality patterns to climatic drivers of drought exhibited considerable variability. Resolving the contributions of precipitation deficits and heatwaves on the drought-induced mortality of trees, almost 91.60% of the forest region in Northeast China was found to be primarily affected by heatwaves (temperature anomalies), suggesting the important role of temperature extremes in forest mortality. The findings provide deeper insight into the mechanisms behind the species-specific formation of age-dependent patterns in Northeast China. This study provides a basis for the formulation of drought adaptation measures for forest species across successional stages and highlights the potential of remote sensing indices in identifying the patterns and climatic drivers of large-scale drought-induced forest mortality.
... Previous studies have shown that the warming trend from 1988 to 2012 was slower than a few decades ago [55,56]. Sun et al. [57] and Du et al. [3] also confirmed that there was a warming gap in China, and the extent of the gap was even more pronounced. The contribution of winter to the national warming gap was the largest, while the contribution of summer was the smallest, which indirectly corresponded to the maximum decreasing rate in winter and the minimum decreasing rate in summer of the YRB from 2007 to 2012. ...
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Land surface temperature (LST) is a key parameter in the study of surface energy balance and climate change from local through to global scales. Vegetation has inevitably influenced the LST by changing the surface properties. However, the thermal environment pattern in the Yangtze River Basin (YRB) still remains unclear after the implementation of large-scale ecological restoration projects. In this study, the temporal and spatial variation characteristics of LST were analyzed based on the Theil–Sen estimator, Mann–Kendall trend analysis and Hurst exponent from 2003 to 2021. The relationships between vegetation and LST were further revealed by using correlation analysis and trajectory-based analysis. The results showed that the interannual LST was in a state of fluctuation and rise, and the increasing rate at night time (0.035 °C‧yr−1) was faster than that at day time (0.007 °C‧yr−1). An obvious cooling trend could be identified from 2007 to 2012, followed by a rapid warming. Seasonally, the warming speed was the fastest in summer and the slowest in autumn. Additionally, it was found that autumn LST had a downward trend of 0.073 °C‧yr−1 after 2015. Spatially, the Yangtze River Delta, Hubei province, and central Sichuan province had a significant warming trend in all seasons, except autumn. The northern Guizhou province and Chongqing showed a remarkable cooling trend only in autumn. The Hurst exponent results indicated that the spring LST change was more consistent than the other three seasons. It was found by studying the effect of land cover types on LST changes that sparse vegetation had a more significant effect than dense vegetation. Vegetation greening contributed 0.0187 °C‧yr−1 to the increase in LST in winter, which was spatially concentrated in the central region of the YRB. For the other three seasons, vegetation greening slowed the LST increase, and the degree of the effect decreased sequentially in autumn, summer, spring and winter. These results improve the understanding of past and future variations in LST and highlight the importance of vegetation for temperature change mitigation.
... Overall, the trend of Tmean in China during 1951-2020 shows significant increase in each month, while only a few areas have a trend of decrease. The distribution of the mean temperature trend in China in our study agrees with the existing literature (Dong et al., 2015;Sun et al., 2018;You et al., 2021;Cui et al., 2017). Tmax is characterised by significant increase, nonsignificant increase, and a non-significant decreasing trend (Fig. S21). ...
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An accurate spatially continuous air temperature data set is crucial for multiple applications in the environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy, and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN (Australian National University Spline). Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at (He et al., 2021a) for monthly maximum air temperature, at (He et al., 2021b) for monthly mean air temperature, and at (He et al., 2021c) for monthly minimum air temperature.
... When AO is in anti-phase, cold flow into the mid-and high-latitudes is strengthened, and meridional exchange is enhanced, resulting in a decrease in temperature in the mid-and low-latitudes. On the contrary, when the AO is in phase, cold air is restricted from spreading southward (Sun et al., 2018;Zhou et al., 2020). WTC analyses reveal that TXm, TNm and TNx across the three cold regions have a significant positive correlation with AO (P < 0.05) on inter-annual and interdecadal oscillation cycles with periodicities of 1-8 years, with the exception of the extreme temperature indices across the TPC which lag behind AO (Fig. 10). ...
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The recent hiatus in global warming has attracted significant attention, yet whether it is a widespread global and/or regional phenomenon remains controversial. Here, we investigate the response of extreme temperature changes since 1961 across China’s cold regions (CCR): Tibetan determine the spatiotemporal characteristics of extreme temperature changes across these cold regions using Mann-Kendall and wavelet transform coherence (WTC) analyses of data from 196 meteorological stations from 1961 to 2018. We further investigate the teleconnection between extreme temperatures and large-scale ocean-atmosphere circulation to determine the potential synoptic scale causes of the observed changes. The results revealed a significant warming slowdown in all extreme temperature indices across CCR from 1998 to 2018. In addition, extreme temperature indices in northwest cold region (NWC) and north cold region (NC) reveal a clear winter warming slowdown and even a significant cooling trend, yet only the cold index in Tibetan Platean cold region (TPC) shows a warming hiatus. We conclude that the warming hiatus observed across these regions is primarily driven by extreme temperature index changes in winter. We also find that phase variations in the Atlantic Multi-decadal Oscillation (AMO) and Arctic Oscillation (AO) critically impact on the observed warming hiatus, but the specific atmospheric mechanisms are elusive and warrant further analysis and investigation.
... Moreover, certain recent studies reported a rise in extreme cold events in China in the early 21st century, which is possibly a regional response to the global warming hiatus (Ding et al., 2021;Li et al., 2015). The warming hiatus in winter in China is more pronounced than that in the annual mean (Du et al., 2019;Sun et al., 2018); a weak cooling of cold extremes is still observed despite the continued warming trend of hot extremes across most regions of China during the global warming hiatus Shen et al., 2018). The obtained results indicate a significant longterm decreasing trend in cold-uncomfortable days since the 1960s. ...
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Climatic comfort, which refers to the comfort of the human body’s thermal sensations, is important for the human habitat. Although considerable efforts have been provided to examine changes in climatic comfort response to global warming from a partial perspective, the trajectory shift in past and future climatic comfort conditions in China mainland based on uniform indicators has not been revealed. The spatiotemporal pattern of climatic comfort over historical and future periods was investigated in this study, using China mainland as an example. The temperature‒humidity index was adopted on the basis of homogenised meteorological station observations and high-resolution climate model simulations to analyse the trends of comfort/discomfort days from 1960 to 2017 and project changes in climatic comfort under representative concentration pathway scenarios in the late 21st century (2071‒2100). Results show a substantial decrease in cold-uncomfortable days and a moderate increase in comfortable and warm-uncomfortable days from 1960 to 2017. In the late 21st century, the signals of increasing warm-uncomfortable and decreasing cold-uncomfortable days are projected to enhance significantly while the direction of changes in comfortable days exhibits a north‒south divergence. The uneven changes in warm- and cold-uncomfortable days and an overall decrease in comfortable days in the late 21st century dominate the future trends in climatic comfort in the densely populated southeast half of China. Effective measures taken for adapting to and mitigating global climate warming can considerably avoid the adverse impact of the projected change.
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The reliability of climate change detection and research is significantly impacted by the inhomogeneity of surface climate observation data. However, there is an ongoing debate regarding whether comprehensive homogenization has been performed in large-scale homogenized data sets. In this study, we examined the homogeneity of the original maximum and minimum temperature (T max and T min ) data for 662 meteorological stations in North China by using multiple methods and combining with metadata. The quantile matching method was employed to adjust the daily T max and T min series. In order to avoid the potential systematic bias resulting from homogenization, no reference series were introduced during the adjustment process. The adjustment results indicate that T min in North China is significantly affected by non-climatic factors, particularly station relocations and environmental changes around the stations. The application of homogenization in this study led to a notable increase in the overall temperature trends of the stations, with T min exhibiting a larger increase and the diurnal temperature range demonstrating a more significant downward trend. Based on the homogenized data, the annual and seasonal mean temperature trends in North China from 1951 to 2020 were re-evaluated. These temperature trends generally surpass those reported in previous research for the same period from 1961 to 2000. The higher estimate of temperature trends may be attributed to the recovered urbanization effect in the newly homogenized data. Thus, the obtained homogenization data still exhibit a significant urbanization bias that requires further assessment and adjustment.
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The magnitude of long-term surface climate warming over some regions, such as the Chinese mainland, is still uncertain due to the lack of observational data early in the 20th century. In this study, the monthly data series of the average, maximum, and minimum temperatures in the Chinese mainland during 1901-2020 were constructed based on the daily surface air temperature observations from 60 stations across the country, and the characteristics of the average, maximum, and minimum temperature, and diurnal temperature range (DTR) changes were analyzed. Results show that (1) regional average annual mean temperature in the Chinese mainland rose by 0.14°C per decade, maximum temperature rose by 0.07°C per decade, minimum temperature rose by 0.19°C per decade, and DTR decreased by 0.13°C per decade. All these trends are statistically significant (p < 0.01); (2) the largest annual mean maximum temperature increase occurred in spring, followed by winter and autumn/summer, and the largest annual mean minimum temperature increase was in winter and spring, followed by autumn and summer; (3) annual mean DTR decreased significantly at a rate of -0.08, -0.12, -0.12, and -0.13°C per decade (p < 0.01) in spring, summer, autumn, and winter, respectively; (4) the stations with drops in maximum temperature were mainly in Central China, southern North China, the southeastern coastal areas, and the middle and lower reaches of the Yangtze River, and the stations with significant increases in minimum temperature were located in North China, Northeast China, and Northwest China; (5) the areas with the fastest dropping DTR were mainly located in Northeast China and North China. The maximum and minimum temperature series for China based on climate anomalies are comparable to those based on other currently available datasets.
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In this paper, we briefly report the first analysis results of the global annual mean Land Surface Air Temperature (LSAT) changes during different periods since 1901 based on the newly developed CMA GLSAT-v1.0 data set. The results show that the upward trends of annual mean LSAT for Sothern Hemisphere, Northern Hemisphere (NH) and the globe were 0.088℃/decade, 0.115℃/decade and 0.104℃/decade, respectively. The global land surface warming trends during 1979-2014 were considerably higher than those of the entire time period (1901-2014), with particularly large trends occurring in the high latitudes of the NH. A high incoherence in global LSAT changes can be seen for the recent “warming hiatus” (1998–2014), with the abnormal warming in Arctic areas neighboring the Eurasian Continent and North Atlantic Ocean, and remarkable cooling at the low and mid- latitudes of the hemispheres, especially in the boreal cold season.
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Many studies have reported enhanced warming trend on the Tibetan Plateau (TP), even during the warming hiatus period. However, most of these studies are based on instrumental data largely collected from the eastern TP, whereas the temperature trend over the extensive northwestern TP remains uncertain due to few meteorological stations. Here we combined the stable isotopic δ(18)O record of an ice core recovered in 2012 from the Chongce glacier with the δ(18)O records of two other ice cores (i.e., Muztagata and Zangser Kangri) in the same region to establish a regional temperature series for the northwestern TP. The reconstruction shows a significant warming trend with a rate of 0.74 ± 0.12 °C/decade for the period 1970-2000, but a decreasing trend from 2001 to 2012. This is consistent with the reduction of warming rates during the recent decade observed at the only two meteorological stations on the northwestern TP, even though most stations on the eastern TP have shown persistent warming during the same period. Our results suggest a possible recent warming hiatus on the northwestern TP. This could have contributed to the relatively stable status of glaciers in this region.
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Improving observations of ocean heat content show that Earth is absorbing more energy from the sun than it is radiating to space as heat, even during the recent solar minimum. The inferred planetary energy imbalance, 0.59 ± 0.15 W m<sup>−2</sup> during the 6-year period 2005–2010, confirms the dominant role of the human-made greenhouse effect in driving global climate change. Observed surface temperature change and ocean heat gain together constrain the net climate forcing and ocean mixing rates. We conclude that most climate models mix heat too efficiently into the deep ocean and as a result underestimate the negative forcing by human-made aerosols. Aerosol climate forcing today is inferred to be −1.6 ± 0.3 W m<sup>−2</sup>, implying substantial aerosol indirect climate forcing via cloud changes. Continued failure to quantify the specific origins of this large forcing is untenable, as knowledge of changing aerosol effects is needed to understand future climate change. We conclude that recent slowdown of ocean heat uptake was caused by a delayed rebound effect from Mount Pinatubo aerosols and a deep prolonged solar minimum. Observed sea level rise during the Argo float era is readily accounted for by ice melt and ocean thermal expansion, but the ascendency of ice melt leads us to anticipate acceleration of the rate of sea level rise this decade. Humanity is potentially vulnerable to global temperature change, as discussed in the Intergovernmental Panel on Climate Change (IPCC, 2001, 2007) reports and by innumerable authors. Although climate change is driven by many climate forcing agents and the climate system also exhibits unforced (chaotic) variability, it is now widely agreed that the strong global warming trend of recent decades is caused predominantly by human-made changes of atmospheric composition (IPCC, 2007). The basic physics underlying this global warming, the greenhouse effect, is simple. An increase of gases such as CO<sub>2</sub> makes the atmosphere more opaque at infrared wavelengths. This added opacity causes the planet's heat radiation to space to arise from higher, colder levels in the atmosphere, thus reducing emission of heat energy to space. The temporary imbalance between the energy absorbed from the sun and heat emission to space, causes the planet to warm until planetary energy balance is restored. The planetary energy imbalance caused by a change of atmospheric composition defines a climate forcing. Climate sensitivity, the eventual global temperature change per unit forcing, is known with good accuracy from Earth's paleoclimate history. However, two fundamental uncertainties limit our ability to predict global temperature change on decadal time scales. First, although climate forcing by human-made greenhouse gases (GHGs) is known accurately, climate forcing caused by changing human-made aerosols is practically unmeasured. Aerosols are fine particles suspended in the air, such as dust, sulfates, and black soot (Ramanathan et al., 2001). Aerosol climate forcing is complex, because aerosols both reflect solar radiation to space (a cooling effect) and absorb solar radiation (a warming effect). In addition, atmospheric aerosols can alter cloud cover and cloud properties. Therefore, precise composition-specific measurements of aerosols and their effects on clouds are needed to assess the aerosol role in climate change. Second, the rate at which Earth's surface temperature approaches a new equilibrium in response to a climate forcing depends on how efficiently heat perturbations are mixed into the deeper ocean. Ocean mixing is complex and not necessarily simulated well by climate models. Empirical data on ocean heat uptake are improving rapidly, but still suffer limitations. We summarize current understanding of this basic physics of global warming and note observations needed to narrow uncertainties. Appropriate measurements can quantify the major factors driving climate change, reveal how much additional global warming is already in the pipeline, and help define the reduction of climate forcing needed to stabilize climate.
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Haze is a serious issue in China with increasing concerns, and understanding the factors driving decadal-scale variations in haze occurrence is relevant for government policymaking. Using a comprehensive observational haze dataset, we demonstrate notable decadal fluctuations in the number of haze days (HD) during winter in central eastern China, showing a decline since the mid-1980s. The leading mode of the wintertime HD features an increasing trend for 1959–2012 in eastern China, highly correlated with China’s gross domestic product (GDP) that represents increasing trend of pollutant emissions, and to a lesser extent meteorological factors. The second mode shows decadal variations in central eastern China associated with Pacific Decadal Oscillation (PDO). Observations and numerical simulations suggest that Mongolia High and corresponding descending motion tend to be enhanced (weakened) in central eastern China during the positive (negative) phase of PDO. With PDO shifting towards a negative phase, the weakened Mongolia High and ascending anomalies make the air unstable and conduce to the spread of pollutants, leading to the decline in the wintertime HD over central eastern China since the mid-1980s. Based on above physical mechanisms, a linear model based on PDO and GDP metrics provided a good fit to the observed HD.
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This paper examined the underlying dynamic mechanisms associated with the meridional displacement of the East Asian trough (EAT), which is closely related to the temperature variability in the southern part of East Asian winter monsoon (EAWM). During the southward displacement of the EAT, the Siberian high is stronger and the Aleutian low is displaced southward. This is due mainly to the anomalous cyclonic flow associated with seasonal eddies over the midlatitude central Pacific, which enhances the horizontal advection of cold (warm) air to the southern (northern) part of the EAT in the lower troposphere. The cold (warm) advection narrows (thickens) the height thickness and results in negative (positive) temperature anomalies in the southern (northern) part of the EAT. These anomalous circulation features can be reasonably explained by the phase of the El Niño–Southern Oscillation (ENSO). The results are also verified by the numerical experiments with prescribing ENSO-like heat source anomalies over the tropical eastern and western Pacific in an anomaly atmospheric general circulation model. All of these results advance our understanding for the linkage between the ENSO and the EAWM via its modulation of the EAT.
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China has warmed rapidly over the past half century and has experienced widespread concomitant impacts on water availability, agriculture and ecosystems. Although urban areas occupy less than 1% of China’s land mass, the majority of China’s observing stations are situated in proximity to urban areas, and thus some of the recorded warming is undoubtedly the consequence of rapid urban development, particularly since the late 1970s. Here, we quantify the separate contributions of urbanization and other external forcings to the observed warming. We estimate that China’s temperature increased by 1.44 ℃ (90% confidence interval 1.22–1.66 ℃) over the period 1961–2013 and that urban warming influences account for about a third of this observed warming, 0.49 ℃ (0.12–0.86 ℃). Anthropogenic and natural external forcings combined explain most of the rest of the observed warming, contributing 0.93 ℃ (0.61–1.24 ℃). This is close to the warming of 1.09 ℃ (0.86–1.31 ℃) observed in global mean land temperatures over the period 1951–2010, which, in contrast to China’s recorded temperature change, is only weakly affected by urban warming influences. Clearly the effects of urbanization have considerably exacerbated the warming experienced by the large majority of the Chinese population in comparison with the warming that they would have experienced as a result of external forcing alone.
During winter, the December North Atlantic Oscillation (NAO) has an impact on the following February East Asian trough (EAT), and a significant positive correlation exists between them. It is shown that the circulation anomalies affected by the December NAO for December and for the following January are primarily confined to the Euro-Atlantic sector while they extend to East Asia during the following February, and this is related to anomalous wave trains originating from the southwestern Atlantic and spreading to the northeastern Atlantic, northern Europe, western Siberia, and East Asia. When the NAO is positive phase in December, the SST tripole pattern is forced by persistence positive NAO from December to the following January, contributing to pronounced positive SST anomalies in mid-latitude areas of the North Atlantic during the following February. The pronounced positive SST anomalies found during this period can generate feedback for atmospheric anomalies, and the westerly winds are enhanced (reduced) to the north (south) side of the positive SST anomalies, which result from strengthened (weakened) baroclinicity there. In addition, the Rossby wave source (RWS) over the northeastern Atlantic shows a positive anomaly, establishing a link between the positive SST anomalies in mid-latitude areas of the North Atlantic and the deepened EAT downstream.