ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 32, SEPTEMBER 2015, 1197–1207
Reduced Soil Moisture Contributes to More Intense and More
Frequent Heat Waves in Northern China
ZHANG Jie∗ , LIU Zhenyuan, and CHEN Li
Key Laboratory of Meteorological Disasters of Ministry of Education,
Nanjing University of Information Science & Technology, Nanjing 210044
(Received 8 August 2014; revised 23 December 2014; accepted 23 December 2014)
ABSTRACT
Heat waves have attracted increasing attention in recent years due to their frequent occurrence. The present study investigates the heat wave intensity and duration in China using daily maximum temperature from 753 weather stations from 1960
to 2010. In addition, its relationships with soil moisture local forcing on the ten-day period and monthly scales in spring and
summer are analyzed using soil moisture data from weather stations and ERA40 reanalysis data. And finally, a mechanistic
analysis is carried out using CAM5.1 (Community Atmosphere Model, version 5.1) coupled with CLM2 (Community Land
Model, version 2). It is found that the heat wave frequency and duration show a sandwich distribution across China, with high
occurrence rates in Southeast China and Northwest China, where the maximum frequency and duration exceeded 2.1 times
and 9 days per year, respectively. The increasing trends in both duration and intensity occurred to the north of 35◦ N. The
relationships between heat wave frequency in northern China in July (having peak distribution) and soil moisture in the earlier
stage (from March to June) and corresponding period (July) are further analyzed, revealing a strong negative correlation in
March, June and July, and thus showing that soil moisture in spring and early summer could be an important contributor to
heat waves in July via positive subtropical high anomalies. However, the time scales of influence were relatively short in the
semi-humid and humid regions, and longer in the arid region. The contribution in the corresponding period took place via
positive subtropical high anomalies and positive surface skin temperature and sensible heat flux anomalies.
Key words: heat wave, soil moisture, multiple time scales, heat wave frequency, heat wave duration
Citation: Zhang, J., Z. Y. Liu, and L. Chen, 2015: Reduced soil moisture contributes to more intense and more frequent heat
waves in northern China. Adv. Atmos Sci., 32(9), 1197–1207, doi: 10.1007/s00376-014-4175-3.
1. Introduction
Heat waves are natural disasters that impact upon human mortality, regional economies, and ecosystems (WMO,
2003; Retalis et al., 2010; Kotroni et al., 2011), and therefore
have attracted increasing attention in the past two decades
due to their frequent occurrence (Meehl and Tebaldi, 2004).
In 2003, a heat wave process covering the whole of Europe
increased the summer mortality rate in France by up to 54%,
and caused EUR 12.3 m of agricultural losses and EUR 1.6
m of forest losses in central, western and southern Europe
(Heck et al., 2001; Hémon and Jougla, 2004). Elsewhere, a
heat wave event in the U.S in 2012 resulted in 42 casualties;
and in Eastern China, the most serious heat wave on record
occurred in 2013, which lasted for 38 days—more than double the average duration in Zhejiang province over the past
65 years (http://zj.weather.com.cn/tqyw/01/2048435.shtml).
Due to global warming, heat waves are becoming more
frequent and more intense in the U.S. (Meehl et al., 2001;
∗
Corresponding author: ZHANG Jie
Email: [email protected]
Meehl and Tebaldi, 2004). Moreover, model simulations suggest that heat waves will increase most in the western, upper mid-western, northeastern, and southern U.S. in the future (Dai et al., 2001; Ebi and Meehl, 2007). Therefore, frequent heat waves have already influenced society. However,
at present, there is no universal definition of a heat wave. It
is well known that heat waves are associated with particularly hot and sustained temperatures, and on these grounds,
a heat wave has been commonly defined. However, different
standards exist in different regions, such as: a period of at
least three consecutive days above 32.2◦C in the U.S. (Tamrazian et al., 2008); at least five consecutive days above 25◦ C,
with three of these above 30◦ C, in Europe (Baldi et al., 2006);
and a period of at least three consecutive days above 35◦ C in
China (Deng et al., 2009; Zhang et al., 2011). The World
Meteorological Organization (WMO) suggests a period of at
least five consecutive days when the daily maximum temperature (Tmax ) exceeds its climatology (Tmax,clim ) by 5◦ C (Frich
et al., 2002).
Heat waves are influenced by many factors, including
greenhouse gases, remote forcing such as sea surface skin
temperature (SST) and the El Niño–Southern Oscillation
© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag Berlin Heidelberg 2015
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DRYING SOIL CONTRIBUTES TO FREQUENT HEAT WAVES
(ENSO), Arctic ice cover, and local responses (Dai et al.,
2001; Meehl and Tebaldi, 2004; Zhang et al., 2013, 2014).
SST is an important forcing of heat waves, and west coast
cities in the U.S. in mid-summer are influenced by high Pacific SSTs, aided by the high-pressure systems in the subtropical Pacific, which tend to stall over the western U.S.
(Meehl and Tebaldi, 2004; Ziv et al., 2004). The Atlantic
Multi-decadal Oscillation (AMO), related to Atlantic SST,
is associated with heat waves in South America (Sutton and
Hodson, 2005). SST anomalies in the Indian and Mediterranean oceans resulted in heat waves in Europe and Africa
in 2003, with the Mediterranean SST being attributed to the
early stages of the heat wave and Indian Ocean SST contributing to the extension of the heat wave into August 2003
(Ziv et al., 2004; Sutton and Hodson, 2005). All these findings demonstrate the remote forcing of SST on heat waves
over continents. As a forcing from higher latitude, arctic
ice loss also results in a positive geopotential height anomaly
over Okhotsk, Lake Baikal, and most of Europe, which promotes extreme drought and heat wave conditions in Europe
and northern China (Zhang et al., 2014). From the atmospheric circulation viewpoint, an anticyclone in the middle
troposphere is the main factor. A simulation by Meehl and
Tebaldi (2004) showed that the heat waves in France in 2003
and 1995 were directly influenced by an anticyclone at the
500 hPa level, while Gong et al. (2004) showed that a largescale, anticyclone enhancement is an important factor influencing heat waves in East Asia.
In addition to the remote forcing and direct association
with atmospheric circulation anomalies, the local forcing
of land surface characteristics are another set of factors affecting heat waves. In Weather Research and Forecasting
(WRF) model simulations, land–atmosphere coupling suggests that local forcing contributes 30%–70% of the atmospheric temperature and heat wave intensity (Zhang and Wu,
2011). The surface changes affecting heat waves include vegetation degradation, urbanization, and decreasing soil moisture. The urban heat island effect due to urbanization was
found to contribute to an increasing of the intensity of heat
waves in Shanghai (Tai et al., 2008). In addition, soil moisture is a key surface factor influencing water-heat transfer in
land–atmosphere processes. The feedback of soil moisture to
the atmosphere has “memory” (Koster and Suarez, 2001). Its
perturbation may markedly modify the atmospheric pressure
field, wind circulation system, and water vapor distribution,
thus affecting atmospheric stability, and such effects may last
for one season or longer (Khodayar et al., 2014). Soil moisture in northern China and the Tibetan Plateau (TP) has decreased in recent years (Zhang and Zuo, 2011), and soil moisture on the TP could modify precipitation intensity and temperature not only over the TP itself, but also over East Asia
as a whole through changing the intensity of monsoon circulation (Chow et al., 2008). Soil moisture simulations using
CAM3.1 (Community Atmosphere Model, version 3.1) and
CLM (Community Land Model) have shown that the extent
of heat wave regions decreases obviously when the annual
anomalies of soil moisture are not considered in the model
VOLUME 32
(Chen and Zhou, 2013). Simulations of the 2003 heat wave
have shown that spring soil moisture contributed to 40% of
the probability of the heat wave events (Fischer et al., 2007).
Under global warming, the soil moisture in spring and summer in the Northern Hemisphere is changing (Chen and Zhou,
2013), which leads us to ask how moisture changes might
affect the distribution of heat waves, their intensity, and frequency. This question remains open.
China lies in the humid, semi-humid, semi-arid, and arid
climates of the Northern Hemisphere; and due to the common
influence of monsoon and the westerly climate, soil moisture
in China is sensitive to climate change (Koster et al., 2004;
Wang et al., 2011), such that its spatiotemporal distribution
could affect the distribution of heat waves, their intensity,
and frequency. To assess the frequency and intensity of heat
waves and the associated mechanisms, the numbers of heat
wave events, their durations and trends are analyzed in this
paper. The study also analyzes the soil moisture contributions to heat wave frequency. Data comprising daily temperature maxima and soil moisture from 58 weather stations
are described in section 2. European Center for MediumRange Weather Forecasts (ECMWF) soil moisture data are
used for analyzing the spatial distribution of correlation coefficients between soil moisture and heat waves, and these
data are also described in section 2. The spatiotemporal variability of heat wave factors and their relationships with soil
moisture are described in section 3. To quantify the effects
of soil moisture on heat waves in northern China, the results
of simulation experiments using the NCAR (National Center
for Atmospheric Research) Community Atmosphere Model,
version 5.1 (CAM5.1) are presented in section 3. Finally, a
summary and conclusions are provided in section 4.
2. Data and methods
2.1. Datasets
Three datasets are used in the study: daily maximum temperature from 753 weather stations from 1960 to 2010; 10 cm
soil moisture from 58 weather stations from 1981 to 2001;
and soil moisture data from the ECMWF (ERA40) dataset
from 1960 to 2002. According to the definition applied by the
China Meteorological Administration (CMA), a heat wave
event in China is defined as a period of at least three consecutive days with temperatures above 35◦ C (Deng et al., 2009).
In order to further study the heat wave intensity, the durations
of heat waves were calculated, where the duration is the number of heat wave days for a heat wave event. The heat wave
frequency is the total number of heat wave events in a year.
The heat wave duration and frequency were calculated using
the daily maximum temperature data. To analyze the relationships between soil moisture and heat wave frequency and intensity, the 10 cm soil moisture data from the ERA40 dataset
were used for investigating the climate zones and the spatial
differences of the correlation coefficients between soil moisture and heat waves. The soil moisture from the ECMWF
analysis was validated using observed soil moisture from 58
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ZHANG ET AL.
weather stations before it is used in the study. These stations
were regarded as representative stations. The data were first
used for validating the soil moisture data from ERA40, and
secondly for calculating the correlation coefficients between
the heat wave frequency in July and the 10-day soil moisture content from March to July, in different typical climate
zones. The climate zones were divided by empirical orthogonal functions (EOFs) and rotated empirical orthogonal functions (REOFs) of soil moisture from ERA40. The ERA40
grid and the distribution of weather stations are shown in
Fig. 1.
2.2. Methodology
The soil moisture data from ERA40 were in the form of
volumetric water content (units: cm3 cm−3 ); however, the
soil moisture data from the 58 weather stations were in the
form of the weight of the water content (units: %). Conversion of the soil moisture from weight of water content to
volumetric water content was performed using the formula
θv = θm ρ ,
(1)
where θm is the weight of the water content (units: %), θv
is the volumetric water content (units: cm3 cm−3 ), and ρ is
the dry density (g cm−3 ). The changes of ρ were very small
compared with θm ; therefore, it was regarded as a constant
for, and obtained from, individual stations.
The 10 cm soil moisture data from ERA40 were interpolated to weather stations using a cubic method (tested using
Fig. 1. Study region and the distributions of the ERA40 grid,
753 weather stations, and 58 soil moisture stations. The rectangle sub-region (34◦ –55◦ N, 100◦ –123◦ E) is the simulation experiment region.
1199
the 10 cm soil moisture of in-situ measurements; Fig. 2) according to climate zones: 17, 19 and 12 soil stations in Northeast China (34◦–55◦ N, 100◦ –123◦E), northern China (40◦–
55◦ N, 123◦–135◦E), and Jianghuai basin (30◦ –34◦N, 110◦–
125◦E) were used for the validations. The correlation coefficients with ERA40 in summer were 0.97, 0.95, and 0.97
for Northeast China, northern China, and Jianghuai basin,
respectively; and in spring they were 0.96, 0.88, and 0.94,
respectively. All correlations were statistically significant at
the 95% confidence level. However, there were obvious differences between them, which should be validated. The validated coefficients in linear regression were 2.73, 0.8, and 1.1
in spring, and 7.34, 0.42, and 0.74 in summer, respectively;
the biases were 0.0014, 0.0026, and −0.0005 in spring, and
−0.004, −0.002, and −0.002 in summer, respectively. All in
all, in Northeast China, when soil moisture was larger than
the average value (wet), soil moisture from ERA40 was less,
but it was larger when soil moisture was less than the average
value (dry). However, the opposite was the case in the other
regions.
Because soil moisture showed a heterogeneous distribution, climatic sub-regions of soil moisture were defined using
EOFs, REOFs, and the validated soil moisture from ERA40.
In addition, correlation coefficients and trend analysis were
used in the study. Reliability tests were also performed, using the Monte Carlo test. It has been proven that the Monte
Carlo method is better than Pearson’s correlation.
2.3. The CAM5 model and experiments
The atmospheric module of the Community Earth System Model, i.e. CAM 5.1 (Neale et al., 2011), was developed at the NCAR. This study applied the finite volume dynamic framework with a horizontal resolution of 1.9◦ (lat) and
2.5◦ (lon), with 30 vertical layers in the σ -p vertical coordinate. CAM5.1 was coupled with the CLM2 land process
model so as to simulate the feedback of soil moisture on the
atmosphere. In this study, several physical processes, including radiation processes, cloud effects, convection, boundary
layer effects and other physical processes, were represented
in the model according to the default options. A detailed description of the model is available at http://www.ccsm.ucar.
edu/models/atm-cam/. The simulation ability of CAM3.1 in
terms of the soil moisture effects on extreme temperature has
been evaluated (Zhou and Chen, 2012), and it was found that
CAM3.1 could generally reproduce the basic features of the
large-scale spatial patterns of annual-mean extreme climate
indices over East Asia, although the bias in extreme temperature is large. CAM5.1 is an improved version based on version 3. Wang (2014) used and tested CAM5.1 for soil moisture simulation in East Asia and found the model shows a
unanimously similar spatial distribution as observed. Therefore, it can be used for further soil moisture sensitivity experiments in East Asia. To quantify the effects of soil moisture on
atmospheric circulation, local temperature and heat waves in
northern China, only the soil moisture data were changed in
the sensitivity experiments when running CAM5.1. Because
soil moisture showed a heterogeneous distribution, the sensi-
1200
DRYING SOIL CONTRIBUTES TO FREQUENT HEAT WAVES
0
−0.01
r=0.97
−0.01
0
0.01
Observation
0.005
0
−0.005
−0.01
−0.016
0.02
0.06 (b)
0.08 (e)
0.03
0.04
ERA40
ERA40
ERA40
0.01
−0.02
−0.02
0
−0.03
−0.06
−0.03
ERA40
0.01 (d)
r=0.95
−0.015
0
0.015
Observation
0.03
0.02 (f)
0.01
0.01
−0.01
−0.02
−0.02
r=0.97
−0.01
0
0.01
Observation
0.02
anomaly
−0.008
−0.04
0.02 (c)
0
r=0.96
0
0.008
Observation
0.016
0
−0.08
−0.08
ERA40
ERA40
0.02 (a)
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r=0.88
−0.04
0
0.04
Observation
−0.01
0
0.01
Observation
0.08
0
−0.01
−0.02
−0.02
trendline
r=0.94
0.02
y=x line
Fig. 2. Comparison of anomalous soil moisture (volumetric water content; units: cm3 cm−3 ) from ERA40
and observation stations in (a–c) summer and (d–f) spring in the three climate regions: (a, d) Northeast China
(34◦ –55◦ N, 100◦ –123◦ E); (b, e) northern China (40◦ –55◦ N, 123◦ –135◦ E); (c, f) Jianghuai basin (30◦ –34◦ N,
110–125◦ E).
tivity region of (34◦–55◦ N, 100◦–123◦E) was ensured based
on the soil moisture sub-regions (see Fig. 1 for the simulation
region). The initial soil moisture and atmospheric data were
the mean values from 1960 to 1985, regarded as the climate
state, and three experiments were performed through decreasing soil moisture by 20% in March, June and July. The experiments were started in those three months and ended at the
end of August.
3. Results and discussion
3.1. Spatial distribution of heat waves in China
Due to the inconsistent increase of temperature associated with global warming, the intensity and frequency of
heat waves therefore show equivalently heterogeneous distributions (Kotroni et al., 2011). Figure 3 shows the spatial
distributions of heat wave frequency (Fig. 3a) and its trend
(Fig. 3b), as well as the heat wave duration (Fig. 3c) and
its trend (Fig. 3d) in China, based on daily maximum temperature from 753 weather stations collected from 1960 to
2010. The heat wave frequencies show a sandwich distribution oriented from Southeast China to Northwest China (Fig.
3a). The spatial distributions of the durations of heat waves
are the same as those of the frequencies of heat wave events
(Fig. 3c), showing high values in Southeast China and North-
west China, exceeding 2.1 times and 9 days per year, respectively; the respective minima are 0.8 times and 0.5 days in
the semi-humid region. The distributions of the frequency
and the duration of heat waves can be classified into clear
climatic sub-regions, demonstrating that heat waves are influenced by different circulation systems, external forcing, or local effects, as further indicated by the sandwich distributions.
Previous work has shown that the western Pacific subtropical high (WPSH) controls Changjiang-Huaihe basin after the
Mei-yu period, resulting in a summer drought period due to
frequent adiabatic heating under downward movements of the
atmosphere (Gong et al., 2004), which is conducive to heat
waves. However, heat waves in Northwest China are mainly
influenced by stronger circulation systems over land in the
mid-latitudes, such as continental high atmospheric pressure.
Meanwhile, precipitation is low in the extreme arid regions
of Northwest China, such that intense heat, a dry surface and
low vegetation cover allow the surface to emit more longwave radiation resulting in a corresponding strong heat flux
(Zhang et al., 2011); these factors contribute to the high air
temperature and high heat wave frequency. The region of infrequent heat waves in the semi-humid region is under the
combined influence of the WPSH and other circulation systems over land in the mid-latitude, because these circulation
systems directly influence the precipitation in the semi-humid
region (Zhang et al., 2014).
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ZHANG ET AL.
1201
Fig. 3. The spatial distribution of the (a, c) mean frequency and (b, d) trend rate of (a, b) heat wave events and (c, d)
heat wave durations (days) in China. The data are calculated from 753 weather stations data from 1960 to 2010. Panels
(a, c) are mean values from 1960 to 2010.
Simulations have shown that North America and most
of Europe are likely to face more intense and more frequent
heat waves in the future under a global warming background.
Similarly, we ask how the occurrence of heat waves might
change in China as a result of clear temperature increases
with global warming. To address this issue, the differences
in heat wave occurrence under normal conditions and global
warming conditions are analyzed. The temporal trend in the
number of heat wave events reflects the change in heat wave
frequency, and the durational trend of heat waves reflects the
change in heat wave intensity. Figure 3 shows the trends in
heat wave frequencies (Fig. 3b) and heat wave durations (Fig.
3d), revealing that while both trends have the same spatial
distribution, there are obvious differences between southern
China and northern China. These distributions are therefore
in contrast to the heat wave frequency (Fig. 3a) and heat wave
duration (Fig. 3b). To the south of 35◦ N, there are two high
centers and two low centers, where the maximum rates of increase in the heat wave frequency and heat wave duration exceeded 2 times and 10 days per 100 years, respectively, while
the minimum rates were less than −2 times and −10 days per
100 years, respectively. To the north of 35◦ N, except for parts
of the west, the rates of increase are obvious. The patterns of
the heat wave frequency and heat wave duration follow that
of the low rainfall regions (figure omitted).
3.2. More intense and more frequent heat waves in northern China
In order to address the heat wave intensity, frequency
and the possible control factors, the variable trends in the region with increasing occurrence (northern China) are compared with those in the regions with decreasing rates (east
of China). Considering that increasing-rate regions include
the arid region and the semi-humid region, two centers of increase are selected according to the largest rate of increase in
the arid region and semi-humid region, and a center of decrease in the humid region is also selected according to the
largest rate of decrease. The representative stations are Ejin
Banner in the western Inner Mongolia Autonomous Region
(increasing rate, west region), Yan’an (increasing rate, semihumid region) and Xuchang (decreasing rate, humid region).
Figure 4 shows the time series of annual heat wave duration
and frequency at the three stations. In the arid region, heat
wave duration and heat wave frequency have increased in the
last 50 years, with peaks in the 1960s, 1990s and 2000s when
the highest duration and frequency were 20 days and 4 times,
1202
20 (a)
4 (d)
10
2
0
0
8
Frequency of heat wave events
Duration of heat wave (day)
DRYING SOIL CONTRIBUTES TO FREQUENT HEAT WAVES
(b)
4
0
20 (c)
10
0
1960
VOLUME 32
2 (e)
1
0
4 (f)
2
1970
1980 1990
Year
2000
2010
0
1960
1970
1980 1990
Year
2000
2010
Fig. 4. Time series of (a–c) annual heat wave durations and (d–f) the frequency of heat wave events from 1960
to 2010 in three typical region (a, d) Ejin Banner, western Inner Mongolia Autonomous Region (arid region);
(b, e) Yan’an, Shaanxi Province (semi-humid region); (c, f) Xuchang, Henan Province (humid region).
respectively. Both indicators were low in the 1970s and early
1990s. The duration and frequency of heat waves both increased steadily from 1960 to 2010 in the semi-humid region,
reaching maximum values of 8 days and 2 times, respectively.
The heat wave duration and frequency show different trends
in the arid region and semi-humid region, although both have
increasing trends overall. The three regions show different
trends in heat wave changes, especially in the humid region,
indicating that heat waves could be influenced by different atmospheric circulation systems, external forcing, or other factors. Figure 4 also shows that the heat wave duration and
frequency share similar trends in the same region, indicating
that increasing frequency contributed to an increasing in the
total of heat wave durations.
To further explore the heat wave intensity, Fig. 5 shows
the heat wave durations for every event for the three representative stations in the last 50 years. The heat wave durations at
the three stations have increased. Specifically, the frequency
of events with a duration of longer than 4 days increased at
the arid station, and that the frequency of events with a duration of longer than 5 days increased at the humid station,
reaching a maximum in the 1990s and 2000s. Most of the
heat wave durations were more than 4 days at the semi-humid
station. Therefore, heat waves were found to have prolonged
durations in both northern China and East China. Figure 6
shows the probability distribution of heat wave frequency in
summer (from 152 to 242 days). In northern China, heat
waves have two peaks, one in mid-June and the other during the last 10 days of July and the first 10 days of August.
In the humid region, meanwhile, they occurred mostly before
the first 10 days of July and decreased after the second 10
days of July, because the monsoon rain arrived in the latter
period. Overall, the period of relatively high probability is
July: the total probability in the arid region, semi-arid region,
and humid region is 43%, 53%, and 36%, respectively, which
covers more than the monthly average of 33%. Therefore,
heat waves in July can be selected to explore the relationships
with local forcing.
3.3. Correlation between soil moisture and heat wave frequency in northern China
Both the heat wave frequency and intensity have increased in northern China, while the heat wave intensity
in the east of China has also increased. Therefore, we ask
if these increases were directly related to global warming.
To address this question, the relationships between local
forcing responding to global warming and heat waves are
analyzed, with the local forcing including vegetation degradation, urbanization, temperature increase, and soil moisture anomalies. Soil moisture is an important parameter
for describing local forcing, because of its influence on the
surface water and energy balance. Studies have found that
soil moisture feedback on the atmosphere can last for more
than 6 months (Sun et al., 2005), indicating the ability of soil
SEPTEMBER 2015
4 (a)
10 (a)
3
6
2
Probability of heat wave days (%)
8
4
2
8 (b)
Duration (day)
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ZHANG ET AL.
6
4
2
10 (c)
1
0
5 (b)
4
3
2
1
0
4 (c)
8
3
6
2
4
1
2
1960
1970
1980
1990
Year
2000
2010
0
152
167
182
197
Day
212
227
242
Fig. 5. Heat wave durations for every heat wave event during
1960–2010. Panels (a–c) represent the stations in the three typical regions detailed in Fig. 4.
Fig. 6. Probability distributions of heat wave events in summer
(June–August; 152–243 days; 1960–2010) Panels (a–c) represent the stations in the three typical regions detailed in Fig. 4.
moisture “memory” during the early stage should be considered. Because heat waves in northern China mainly occur
in July, the relationships between the heat frequency in July
and soil moisture in spring and summer are therefore analyzed. Figure 7 shows the lead–lag correlation coefficients
for every 10-day period at the three stations. The results
indicate that there was strong negative correlation between
soil moisture during the early stage and the corresponding
period and the heat wave frequency in July at the arid station. The highest correlation coefficients occurred in March
and July, exceeding the 90% confidence level; however, the
correlation was weaker from April to June due to precipitation adjustment, i.e. precipitation results in increasing soil
moisture from April to June, which decreases the feedback of
soil moisture conditions in March. Correlation coefficients
at the semi-humid station were high in May, June and July,
and those at the humid station were high in late May–early
June and July, indicating negative correlation is earlier for
two months in the humid station. But this is different from
the correlation in the arid region. Therefore, early-season soil
moisture could contribute to heat waves in July, but a notable
difference is that soil moisture at the arid station could influence summer heat waves over a long time period; however,
the influence was relatively short at the semi-humid and humid stations, showing that the “memory” ability of soil moisture content is related to soil moisture and soil properties,
because the soil moisture content is the largest difference for
the three stations corresponding to three climate belts.
Selecting the high correlation period (March, June and
July), and using 10 cm soil moisture from ERA40, the spatial
distribution of correlation coefficients was calculated (Fig.
8). ERA40 data were interpolated to 753 weather stations using a cubic method. Correlation coefficients in March were
consistent with those in May, and were negative between
35◦ N and 45◦ N, and positive to the south of 35◦ N and north
of 45◦N. Correlation coefficients in the other months were
negative, except for a few areas in the northeastern and northern regions. The spatial distributions of the correlation coefficients further demonstrate the importance of soil moisture
in contributing to heat waves, and soil moisture at the arid
station could influence summer heat waves over longer time
scales compared with the semi-humid and humid regions.
3.4. How does soil moisture contribute to heat waves?
CAM5.1 coupled with CLM2 was selected to explore the
intensity of the effect of soil moisture on heat waves and the
mechanisms involved. This was because the model has been
previously assessed to be successful in East Asia, especially
in terms of its ability to simulate the soil moisture feedback to
extreme temperature (Zhou and Chen, 2012). Due to the heterogeneous distribution of soil moisture in China, a sensitive
region of soil moisture needed to be identified for carrying
out the sensitivity experiments. Based on the soil moisture of
(27◦ –53◦N, 73◦ –132◦E) from May to July from ERA40, the
climate zones of soil moisture were analyzed using EOF and
REOF. The total of the first eight load vectors explained about
80% of the soil moisture distribution, and the study area was
divided into seven sub-regions (Fig. 9) based on these first
1204
DRYING SOIL CONTRIBUTES TO FREQUENT HEAT WAVES
0.4 (a)
0
Correlation coefficient
−0.4
−0.8
0.4 (b)
0
−0.4
−0.8
0.4 (c)
0
−0.4
−0.8
6
9
12
15
Period of ten days
18
21
Fig. 7. Time series of the correlation coefficients between the
soil moisture from 6th to 21st period of ten days and the heat
wave frequencies in July. Panels (a–c) represent the stations in
the three typical regions detailed in Fig. 4. The thick black line
indicates statistical significance at the 90% confidence level.
The soil moisture data are from weather stations during 1981–
2001.
eight load vectors. The northern region (A) was selected in
the sensitivity experiments, because heat waves have become
more intense and more frequent in this region in recent years
(Fig. 4). Considering that the west of the region is an arid
region, soil moisture is influenced by human activity in the
form of preserving irrigated oases. Moreover, region A is
mainly located in eastern China; therefore, only the eastern
region (34◦–55◦ N, 100◦–123◦E) was used for the present experiments, and this region also covered sub-region B.
Three sensitivity experiments were carried out to simulate the feedback between soil moisture and air temperature
in July at different time scales; and because the high correlation coefficients (exceeding 90% confidence) were in March,
late May–June and July, these months were selected for the
simulations. The first two experiments for soil moisture in
March and June represent the contributions of early-season
soil moisture to air temperature in July, while the later experiment for soil moisture in July represents the soil moisture
effects on air temperature during the same terms. The initial
soil moisture and atmospheric data were the mean values
from 1960 to 1985, regarded as the climate state, and the
three experiments were performed through decreasing soil
moisture by 20%. The model runs began from March, June
VOLUME 32
and July, respectively, and ended at the end of August; the
temporal resolution was one month. Sensitivity experiments
and control experiments were both run. When soil moisture
in March was decreased by 20%, long-term feedback between moisture in March and atmospheric circulation from
March to July was established. Figure 10 shows the anomalies of variables such as geopotential height, air temperature
at 2 m, surface skin temperature, and sensible heat flux, in
which the anomaly value is the difference between the sensitivity experiment and control experiment results. Figure
10a shows the geopotential height anomaly at 900 hPa in
July, displaying a positive anomaly at the China–Mongolia
border, and across northern China and the Tibetan Plateau,
and a negative anomaly in southern China and the Xinjiang
autonomous region in western China. Coincident with the
positive geopotential height anomaly, there was a positive air
temperature anomaly on the China–Mongolia border, northern China, and over the Tibetan Plateau (Fig. 10b), as well
as a positive surface skin temperature anomaly (Fig. 10c).
Meanwhile, the sensible heat flux showed a zonal distribution
(Fig. 10d), with a positive zone between 40◦ N and 45◦N, and
another positive zone extending along the southern Tibetan
Plateau. The increasing temperature in July shows good relations with the anticyclone anomaly, being mainly influenced
by it. This was affected by decreased early-season soil moisture, indicating that decreasing soil moisture is conducive to
height and anticyclone anomalies. An enhanced anticyclone
contributes to increasing temperature across most of northern
China because anticyclone could increase adiabatic heating
with downward movement of the atmosphere. When soil
moisture in June was decreased by 20%, the change in soil
moisture fed back to atmospheric circulation over timescales
exceeding one month, and resulted in an influence on atmospheric circulation persisting into July. Figure 10e shows
the geopotential height anomaly at 900 hPa in July, revealing a positive anomaly across northern, northeast, and parts
of western China, and a negative anomaly across southern
China, the Tibetan Plateau, and most of western China. A
positive air temperature anomaly (Fig. 10f) and a positive
surface skin temperature anomaly (Fig. 10g) were coincident
with the positive geopotential height anomaly. Meanwhile,
sensible heat flux was not consistent with geopotential height
(Fig. 10h), showing a positive center over the north of the
Tibetan Plateau and northern China. The distributions of
increase air temperature in July were the same as those of
decreased soil moisture, despite the one-month time duration. When soil moisture in July was decreased by 20%, it
still influenced the atmosphere during this time. On the one
hand, soil moisture feeds back to air temperature within the
boundary layer, while on the other hand it feeds back to the
atmospheric circulation. The atmospheric circulation then
influences the air temperature, as can be seen from the distribution of the positive geopotential height anomaly (Fig. 10i),
positive air temperature anomaly (Fig. 10j), and surface skin
temperature (Fig. 10k). At the same time, the distribution
of sensible heat flux shows some consistency with air temperature in the west of 110◦ E and south of 45◦ N. Overall, soil
SEPTEMBER 2015
54N
54N
(a)
45N
45N
36N
36N
27N
75E
54N
1205
ZHANG ET AL.
85E
95E
105E 115E 125E 135E
27N
75E
54N
(c)
45N
45N
36N
36N
27N
75E
85E
95E
105E 115E 125E 135E
−0.3
85E
95E
105E 115E 125E 135E
85E
95E
105E 115E 125E 135E
(d)
27N
75E
95% confidence
corr. coef.
−0.4
(b)
−0.2
−0.1
0
0.1
province boundary
0.2
0.3
0.4
Fig. 8. The distributions of correlation coefficients between the soil moisture in (a) March, (b) May, (c) June,
and (d) July from validated ERA40 and heat wave frequency in July from 753 stations. White regions are the
regions without heat waves or with absolute correlation coefficients of less than 0.03. The thick black line is
the 90% confidence level of the Monte Carlo test.
is not in accordance with the air temperature distribution and
the decreased soil moisture in East China and at the Chinese
and Mongolian frontier, demonstrating the relationships between soil moisture and sensible heat flux are complicated.
55N
48N
A
41N
B
34N
27N
73E
85E
97E
109E
121E
Fig. 9. The climatic sub-regions of soil moisture divided by
REOF and the 10 cm soil moisture data from ERA40. A and
B were the sub-regions selected for the simulation experiments.
moisture in the corresponding period influences heat waves
through two processes: (1) feedback to the atmosphere; (2)
decreased soil moisture, resulting in less soil heat capacity,
which is conducive to increasing soil temperature and further for increasing long wave radiance; it also favors an increase in the temperature difference between the surface and
air, and finally for increasing the sensible heat flux in the west
of 110◦ E, all which contribute to increasing air temperature
and heat waves. However, the sensible heat flux distribution
4. Summary and conclusion
The heat wave frequencies and durations in recent five
decades (1960–2010) showed a sandwich distribution with a
southeast to northwest orientation. The highest values were
found in southern and northwest China, where the maximum
heat wave frequency and duration was 2.1 times and 9 days,
respectively; the lowest values were found in the semi-humid
region, with minima of 0.8 times and 0.5 days, respectively.
The heat wave frequency and duration distributions displayed
different trends and obvious climatic sub-regions, as further
demonstrated by the sandwich distributions, indicating that
heat waves are influenced by many factors such as different
circulation systems, external forcing, or local effects.
The trends in heat wave frequency and duration shared
the same spatial distribution, indicating that the frequency
and duration are affected by the same mechanisms. However,
there were obvious spatial differences between both southern and northern China. To the north of 35◦ N, except for
1206
DRYING SOIL CONTRIBUTES TO FREQUENT HEAT WAVES
(a)
(e)
VOLUME 32
(i)
3 ms
55
−1
5
45
0
35
−5
25
(b)
(f)
(j)
2
55
45
0
Latitude ( °N)
35
25
(c)
(g)
(k)
−2
2
55
45
0
35
25
(d)
(h)
(l)
−2
20
55
45
0
35
25
74
84
94 104 114 124 134
74
84
94 104 114 124 134
Longitude ( °E)
74
84
94 104 114 124 134
−20
Fig. 10. CAM5.1 simulation of (a, e, i) anomalous geopotential height and wind vectors at 900 hPa (units: gpm), (b,
f, j) anomalous air temperature at 2 m (units: ◦ C); (c, g, k) anomalous surface skin temperature (units: ◦ C), and (d,
h, l) anomalous sensible heat flux (units: W m−2 ). The results are from sensitivity experiments in July, when the soil
moisture was decreased by 20% in (a–d) March, (e–h) June, and (i–l) July. The anomaly value is the difference between
the sensitivity experiment result and the control experiment result.
parts of western China, rates in heat wave events and duration matched the rainfall distribution. The trends of heat
wave duration and frequency displayed differences between
the arid region and semi-humid region. The heat wave frequency increased in northern China and decreased in eastern
China, and the increased duration indicates a prolonging of
heat waves in northern China.
Because heat waves in northern China mainly occurred in
July, with probabilities of 43%, 53%, and 36% in the arid region, semi-humid region and humid region, the heat waves in
July were selected to analyze the correlation with soil moisture during early stages and the corresponding periods. There
was obvious negative correlation between soil moisture and
heat wave frequency in July at the arid station, while the
correlation coefficient at the semi-humid station was high in
May, June and July, and the correlation coefficient at the humid station was high in late May, early June, and July. Therefore, both early-season and July soil moisture contributed to
heat waves in July; however, the difference is that soil moisture in the arid region influenced summer heat waves over a
longer time scale, and only over relatively shorter time scales
at the semi-humid and the humid stations. The spatial correlation results also showed obviously negative correlation.
The mechanisms involved were elucidated using CAM5.1based sensitivity experiments. The decreased soil moisture
in March, June and July resulted in an anticyclonic anomaly
and positive anomalies in geopotential height and air temperature. Meanwhile, soil moisture decreasing in July not only
resulted in positive geopotential height and air temperature
anomalies, but also contributed to a high surface skin temperature and sensible heat flux in the arid and semi-arid regions,
which promoted high air temperatures and heat waves. However, sensible heat flux was not in accordance with the temperature distribution in the humid region in eastern China. In
addition, from Fig. 10, we can conclude that the feedback of
early-season soil moisture on heat waves is complex, because
positive temperature anomalies do not match well with decreased soil moisture. A possible reason is that soil moisture
could influence temperature in remote regions through atmospheric circulation, while another reason might be the uncertainty and limited simulation ability of CAM5.1. Of course,
there are likely to be many other factors involved, and thus
further research in the future in this regard is necessary.
Acknowledgements. This research was jointly supported by
the National Natural Science Foundation of China (Grant Nos.
41375155 and 91437107) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We
thank the two anonymous reviewers for their helpful comments and
suggestions, which greatly improved the manuscript.
SEPTEMBER 2015
ZHANG ET AL.
REFERENCES
Baldi, M., G. Dalu, G. Maracchi, M. Pasqui, and F. Cesarone,
2006: Heat waves in the Mediterranean: A local feature or
a larger-scale effect? Int. J. Climatol., 26, 1477–1487.
Chen, H. S., and J. Zhou, 2013: Impact of interannual soil moisture
anomaly on simulation of extreme climate events in China.
Part II: Sensitivity experiment analysis. Chinese J. Atmos.
Sci., 37(1), 1–13. (in Chinese)
Chow, K. C., J. C. L. Chan, X. Shi, Y. M. Liu, and Y. H. Ding,
2008: Time-lagged effects of spring Tibetan Plateau soil
moisture on the monsoon over China in early summer. Int.
J. of Climatol., 28, 55–67.
Dai, A., T. M. L. Wigley, G. A. Meehl, and W. M. Washington,
2001: Effects of stabilizing atmospheric CO2 on global climate in the next two centuries. Geophys. Res. Lett., 28, 4511–
4514.
Deng, Z. Y., Q. Zhang, J. F. Xu, M. Chen, S. J. Qin, and S. Y.
Zhang, 2009: Comparative studies of the harm characteristic
of hot-day wind and high temperature heat waves. Advance in
Earth Science, 24(8), 865–873. (in Chinese)
Ebi, K. L., and G. A. Meehl, 2007: The heat is on: Climate change
& heat waves in the Midwest. Regional Impacts of Climate
Change: Four Case Studies in the United States, K. L. Ebi,
G.A. Meehl, D. Bachelet, R.R. Twilley, and D. Boesch, Eds.,
Pew Center on Global Climate Change. Arlington, 14 pp.
Fischer, E. M., S. I. Senviratne, P. L. Vidale, D. Lüthi, and C. Schär,
2007: Soil moisture–atmosphere interactions during the 2003
European Summer Heat Wave. J. Climate, 20, 5081–5099.
Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. K. Tank, and T. Peterson, 2002: Observed coherent
changes in climatic extremes during the second half of the
twentieth century. Climate Research, 19, 193–212.
Gong, D. Y., Y. Z. Pan, and J. A. Wang, 2004: Changes in extreme
daily mean temperatures in summer in eastern China during
1961–2000. Theor. Appl. Climatol., 77, 25–37.
Heck, P., D. Lüthi, H. Wernli, and C. Schär, 2001: Climate impacts
of European-scale anthropogenic vegetation changes: A study
with a regional climate model. J. Geophys. Res., 106, 7817–
7835.
Hémon, D., and E. Jougla, 2004: Surmortalité li ée à la canicule
daoût 2003. L ’Institut National de la Santé et de la recherché
Médicale (INSERM), Paris, France, 76 pp. (in French)
Khodayar, S., A. Sehlinger, H. Feldmann, and C. Kottmeier, 2014:
Sensitivity of soil moisture initialization for decadal predictions under different regional climatic conditions in Europe.
Int. J. Climatol., doi: 10.1002/joc.4096.
Koster, R. D., and M. J. Suarez, 2001: Soil moisture memory in
climate models. J. Hydrometeorol., 2, 558–570.
Koster, R. D., and Coauthors, 2004: Regions of strong coupling
between soil moisture and precipitation. Science, 305, 1138–
1140.
Kotroni, V., K. Lagouvardos, and A. Retalis, 2011: The heat wave
of Jun. 2007 in Athens, Greece—Part 2: Modeling study and
sensitivity experiments. Atmos. Res. 100, 1–11.
Meehl, G. A., and C. Tebaldi, 2004: More intense, more frequent,
and longer lasting heat waves in the 21st Century. Science,
305, 994–997.
Meehl, G. A., F. Zwiers, J. Evans, T. Knutson, L. Mearns, and
P. Whetton, 2001: Trends in extreme weather and climate
1207
events: Issues related to modeling extremes in projections of
future climate change. Bull. Amer. Meteor. Soc., 81, 427–436.
Neale, R. B., J. H. Richter, and A. J. Conley, 2011: Description of
the NCAR community atmosphere Model (CAM5). Technical Report NCAR/TN-486+STR, National Center for Atmospheric Research, Boulder, Colorado, 268 pp.
Retalis, A., D. Paronis, V. Kotroni, and K. Lagouvardos, 2010:
The heat wave of Jun. 2007 in Athens, Greece-Part 1: study
of satellite derived land surface temperature. Atmos. Res., 98,
458–467.
Sun, C. H., W. J. Li, Z. Q. Zhang, J. H. He, and X. Q. Zhang, 2005:
Impact of Huaihe River basin soil temperature and humidity abnormality in pre-winter and spring time on the anomalous summer rainfall and its application. Acta Meteorologica
Sinica, 63(1), 115–122. (in Chinese)
Sutton, R. T., and D. L. R. Hodson, 2005: Atlantic Ocean forcing
of North American and European summer climate. Science,
309, 115–118.
Tai, J. G., Y. F. Zhen, L. Peng, S. Q. Gu, and J. Shi, 2008: Effect
of urban heat island on heat waves in summer of Shanghai.
Plateau Meteorology, 27(S1), 144–149. (in Chinese)
Tamrazian, A., S. L. Dochy, J. Willis, and W. C. Patzert, 2008:
Heat waves in southern California: Are they becoming more
frequent and longer lasting? Yearbook of the Association of
Pacific Coast Geographers, 70, 59–69.
Wang, A. H., D. P. Lettenmaier, and J. Sheffield, 2011: Soil moisture in China 1950–2006. J. Climate, 24, 3257–3271.
Wang, L., 2014: The relationship between heat waves and soil
moisture in north China. M.S. thesis, Nanjing University of
Information Science & Technology, 77 pp. (in Chinese)
WMO, 2003: The health impacts of 2003 summer heat-waves.
Briefing note for the Delegations of the fifty-third session of
the WHO Regional Committee for Europe. 12 pp.
Zhang, R. H., and Z. Y. Zuo, 2011: Impact of spring soil moisture
on surface energy balance and summer monsoon circulation
over East Asia and precipitation in East China. J. Climate, 24,
3309–3322.
Zhang, J., D. L. Li, L. Li, and W. T. Deng, 2013: Decadal variability of droughts and floods in the Yellow River basin during the
last five centuries and relations with the North Atlantic SST.
Int. J. Climatol., 33, 3217–3228.
Zhang, J., L. Li, D. L. Li, and W. T. Deng, 2014: Summer drought
around the mid-latitude river in the northern China associated
with the recent Arctic ice loss in summertime. Int. J. Climatol., doi: 10.1002/joc.4177.
Zhang, J. Y., and L. Y. Wu, 2011: Land-atmosphere coupling amplifies hot extremes over China. Chinese Science Bulletin, 56,
3328–3332.
Zhang, Q., J. Zhang, J. Qiao, and S. Wang, 2011: Relationship of
atmospheric boundary layer depth with thermodynamic processes at the land surface in arid regions of China. Science
China Earth Sciences, 54(10), 1586–1594.
Ziv, B., H. Saaroni, and P. Alpert, 2004: The factors governing the
summer regime of the Eastern Mediterranean. Int. J. Climatol., 24, 1859–1871.
Zhou, J., and H. S. Chen, 2012: Impact of interannual soil moisture
anomaly on simulation of extreme climate events in China.
Part I: Model evaluation based on CAM3.1. Chinese J. Atmos. Sci., 36(6), 1077–1092. (in Chinese)