PUBLICATIONS
Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2015JD023830
Key Points:
• Hot extremes in summer and soil
moisture deficit are in negative
correlation
• Offers an evidence for different
warming patterns of regional climate
• Sheds light on physical mechanisms
behind warming climate at the
regional scale
Correspondence to:
Q. Zhang,
zhangq68@mail.sysu.edu.cn
Citation:
Zhang, Q., M. Xiao, V. P. Singh, L. Liu,
and C.-Y. Xu (2015), Observational
evidence of summer precipitation
deficit-temperature coupling in China,
J. Geophys. Res. Atmos., 120,
10,040–10,049, doi:10.1002/
2015JD023830.
Received 19 JUN 2015
Accepted 11 SEP 2015
Accepted article online 14 SEP 2015
Published online 6 OCT 2015
Observational evidence of summer precipitation
deficit-temperature coupling in China
Qiang Zhang1,2,3, Mingzhong Xiao1,2, Vijay P. Singh4, Lin Liu3, and Chong-Yu Xu5
1
Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou, China, 2Key Laboratory of Water
Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou,
China, 3Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning,
Sun Yat-sen University, Guangzhou, China, 4Department of Biological and Agricultural Engineering and Zachry Department
of Civil Engineering, Texas A&M University, College Station, Texas, USA, 5Department of Geosciences and Hydrology,
University of Oslo, Oslo, Norway
Abstract Partition of the energy between sensible heat and latent heat indicates that surface temperatures
are affected by soil moisture deficits. Since transpiration by plants is the largest contributor to the land’s
total latent heat, the coupling of temperature and soil moisture will depend on the response of vegetation to
soil moisture deficit and those are influenced by the soil moisture regimes. Utilizing daily precipitation and
temperature data from China for a period of 1961–2010, this study computes average annual climatic water
balance (AACWB) for defining soil moisture regimes and then quantitatively investigates the summer soil
moisture-temperature coupling. With precipitation deficits (indicated by standardized precipitation index
with the selected optimal timescale of 3 months) as proxy of soil moisture deficits, results indicate that the
relationship between summer precipitation deficits and hot extremes tends to be enhanced when the
negative AACWB draws closer toward zero while tends to be weakened with the increase of positive AACWB.
For the region with the negative AACWB closing zero, the enhanced relationship should be attributed to the
increase of the proportion of latent heat compared to the absorbed total energy. However, the weakened
relationship with the increase of positive AACWB may be owing to the different responses of vegetation to
precipitation deficit that the transpiration in the region with lower positive AACWB is less when responding to
precipitation deficit. However, the physiological mechanisms behind vegetation response to soil moisture
deficits still need to be further analyzed. By quantifying relevant biological and hydrological processes and
their interaction, it is expected that the uncertainties in future climate scenarios be reduced, which would then
allow the development of early warning and adaptation measures prior to the occurrence of hot extremes.
Further, the summer precipitation deficit-temperature coupling is strongest along the strip stretching from
southwest to northeast in China.
1. Introduction
Owing to the potential for improving long-term and large-scale climate prediction, interactions between soil
moisture and climate are important [e.g., Seneviratne et al., 2010]. Hence, the coupling between soil moisture
and extreme temperatures has received much attention in recent decades [Hirschi et al., 2011; Mueller and
Seneviratne, 2012; Ford and Quiring, 2014]. With the quantile regression method, it has been established
that the impact of soil moisture deficit on temperature extremes is asymmetric; i.e., the connection is
the strongest for most extreme heat events, as highlighted in southeastern Europe [e.g., Hirschi et al.,
2011] and Oklahoma Mesonet, USA [e.g., Ford and Quiring, 2014]. In a recent global study [Mueller and
Seneviratne, 2012], the soil moisture-temperature coupling has been shown to be geographically extensive,
such as most areas of North America, South America, Europe, Australia, and parts of China. In order to generalize
this soil moisture-temperature coupling, data should be collected from climatically diverse regions and this is the
major motivation of the current study.
©2015. American Geophysical Union.
All Rights Reserved.
ZHANG ET AL.
Soil moisture is a major source of water for evapotranspiration from land areas, including transpiration and
bare soil evaporation. As the energy required for evaporation is large, the soil moisture amount will affect
how energy absorbed by the land surface is returned to the atmosphere [Alexander, 2011]. Whenever soil
moisture limits the total energy used by the latent heat flux (manifested as the evaporation of liquid water),
more energy is available for sensible heating (directly heats air or land), which induces an increase of the
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near-surface air temperature [Seneviratne et al., 2010]. Thus, surface temperature is affected by soil moisture
anomalies through the partition of energy between sensible heat and latent heat [Seneviratne et al., 2010;
Alexander, 2011].
Besides, transpiration by plants is the largest contributor to the total land evapotranspiration (the main
manifestation of latent heat); the relationship between the surface temperature and soil moisture deficits will
depend on the response of vegetation to soil moisture deficit, and those are expected to be influenced by the
soil moisture regimes. It has been found that Hirschi et al. [2011] and Meng and Shen [2013] classified the soil
moisture regimes into soil moisture-limited evapotranspiration regime and energy-limited evapotranspiration
regime when analyzing the influence of soil moisture regime on hot extremes. However, above mentioned
classification was not quantified. Besides, previous studies [Stephenson, 1990; Vicente-Serrano et al., 2013] have
shown that climatic water balance (the difference between annual precipitation and potential evapotranspiration)
is one of the main climate drivers behind the geographical distribution of vegetation types. Further, as a
function of average annual climatic water balance (AACWB), different responses of the land biomes to
drought have been found, when considering separately negative and positive AACWB [Vicente-Serrano
et al., 2013]. Therefore, in this study AACWB has been utilized to define soil moisture regimes to quantitatively
analyze the relationship between soil moisture deficits and hot extremes.
Like other regions of the world, China has also been identified as one of the regions with strong soil moisturetemperature coupling in the Global Land and Atmosphere Coupling Experiments [Koster et al., 2006], and a
significant relationship between soil moisture deficits and extreme temperatures has been found in east
China by Meng and Shen [2013]. In that study, the 6 month standardized precipitation index (SPI) has been
used to represent antecedent soil moisture deficits in order to avoid uncertainties associated with modeled
soil moisture. However, no reasons have been given concerning why not to select the SPI with other
alternative timescales. So in this current study, the 3 month, 6 month, and 9 month SPI have been calculated
as a measure of soil moisture conditions, respectively, and then the optimal timescale of SPI is selected when
the relationship between the SPI and hot extremes is the best. Furthermore, China has a huge land area, and
its climate is diverse with transition from arid northwest to humid southeast, resulting in different vegetation
types. It is expected that the coupling of soil moisture and temperature would vary with climate regions.
However, previous studies [Meng and Shen, 2013] did not quantitatively identify the soil moisture-extreme
temperature coupling in different regions with different climates, even though this coupling is valuable for
the development of early warning and adaptation measures before the occurrence of hot extremes and
reduction of uncertainties in future climate scenarios in particular with regard to changes in climate variability
and extreme events [Seneviratne et al., 2010]. Thus, this study will serve as a reference for other regions of the
world. The objectives of this study therefore are to (1) investigate the relationship between the summer
monthly SPI and hot days in China using the quantile regression with the selected optimal timescale of SPI
and (2) quantitatively evaluate the variation of the relationship between the summer monthly SPI and hot
extremes corresponding to the different soil moisture regimes which are represented by the AACWB.
2. Study Region and Data
Located in East Asia (Figure 1), China (18°–54°N, 73°–135°E) is climatologically characterized by winter
and summer monsoons. The annual total precipitation in China generally ranges from less than 25 mm in
northwest to more than 2000 mm in southeast and is mainly concentrated in summer [Zhai et al., 2005].
Owning to the complex topographic landscape and various underlying features, climate across China is
complicated and diverse. China is subdivided into eight climatic regions [Xiao et al., 2013]: western arid (semiarid)
zone, Qinghai-Tibetan Plateau, east arid zone, southwest China, northeast China, north China, central China, and
south China (Figure 1).
Data on daily precipitation and daily 2 m air temperature (Tmax and Tmin) covering a period of 1961–2010 from
554 weather stations in China were obtained from the National Meteorological Information Center of the China
Meteorological Administration. For daily precipitation, there are 70 weather stations containing missing days with
the largest missing rate of 0.36%, and most of them had less than 0.1% of total missing values. For daily temperatures, there are 220 and 299 weather stations containing missing days, respectively, for Tmax and Tmin with the
largest missing rate of 2%. However, most of them had less than 0.1% of total missing values. It should be noted
here that the 554 stations were extracted from 754 stations in China with the guidelines that first, the stations
ZHANG ET AL.
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Figure 1. Study region and locations of weather stations.
with time series less than the period of 1961–2010 were deleted, and then the stations with missing data of
consecutive months were also excluded from the analysis, as it is hard to fill a vacancy for the stations with missing data over a consecutive month. The missing values of specific days were replaced by the long-term average
of the same days of other years, and a similar gap fill method was used by Zhang et al. [2011].
The percentage of hot days (% HD) and the maximum heat wave duration (HWDmax) were used to represent
the summer (June, July, and August) hot extremes. According to Hirschi et al. [2011], % HD was defined as the
percentage of hot days in each month with daily Tmax exceeding the local 90th percentile temperature in the
reference period (1961–1990) and HWDmax was defined as the maximum consecutive days in each month with
daily Tmax exceeding the local 90th percentile of the same reference period. Similar to Mueller and Seneviratne
[2012], a time window of 5 days centered on each day of the reference period was considered to calculate the
local 90th percentile temperature. In addition, the subsoil (30–100 cm) pH data for China have also been
extracted from the Regridded Harmonized World Soil Database v1.2 provided by the Oak Ridge National
Laboratory Distributed Active Archive Center [Fischer et al., 2008; Wieder et al., 2014], and the spatial resolution
of the data set is 0.05° longitude × 0.05° latitude. In this study, the spatial resolution of the station-based grid
weather data is 1° longitude × 1° latitude; to match the spatial resolution of the grid weather data, the spatial
resolution of the subsoil pH data, i.e., 0.05° longitude × 0.05° latitude, was resampled to the spatial resolution
of 1° longitude × 1° latitude, which was done with the average of the original subsoil pH data. If the resampled
grids contain more than half of the original grids with missing values, then the resampled grids were set as the
missing values.
3. Methodology
3.1. Gridding of Station-Based Data
Since the observation stations are not evenly distributed across China, it is difficult to obtain regional
averages without bias. To reduce the bias, a method to grid the station-based data onto a regular latitudelongitude grid was used, as suggested by Alexander et al. [2006]. Compared to several other methods such
as thin-plate splines and Delaunay triangulation, New et al. [2000] have found that the angular distance
weighting method is the most appropriate method for gridding irregularly spaced data, and then this
method has been used in this study. For gridding, the angular distance weighting method was used by
weighting each station according to its distance and angle from the center of a search radius while the search
radius was determined based on the spatial correlation structure of station data, and this method has also
been used by Caesar et al. [2006] and Alexander et al. [2006]. In this study, the resolution of gridding was
1° latitude × 1° longitude, and details of the calculation of the angular distance weighting method can be
referred to Alexander et al. [2006] and Caesar et al. [2006].
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3.2. Quantile Regression
The ordinary least squares (OLS) regression has been commonly used to estimate the change in the mean of
a response variable. However, the OLS is not suitable for regression models with heterogeneous variance as
rate of change is not constant [Hirschi et al., 2011; Meng and Shen, 2013]. Since it has been well documented
that the influence of soil moisture on temperature extremes is asymmetric [Hirschi et al., 2011; Mueller and
Seneviratne, 2012; Ford and Quiring, 2014], the quantile regression was used in this study to analyze the
relationship between soil moisture and temperature, as it estimates multiple rates of change from the minimum
to the maximum quantile, and these have also been done by Hirschi et al. [2011] and Mueller and Seneviratne
[2012]. Details of the quantile regression can be found in Koenker [2005] and Hirschi et al. [2011].
The quantile regression was analyzed using R package “quantreg” [Koenker, 2013]. To assess the significance
of regression relation, confidence intervals for the regression slope were also computed, and the rank test
method, assuming errors to be independent and identically distributed, was used, as it is the default option
in the R package quantreg.
3.3. SPI
The standardized precipitation index (SPI) was used to represent the antecedent precipitation deficit (positive
values indicate wet conditions while negative values drought). The SPI was developed by McKee et al. [1993]
to evaluate drought conditions and is based on the statistical probability of precipitation. The SPI can be
calculated by first fitting a gamma distribution to precipitation data and then transform the data to an inverse
normal function [Meng and Shen, 2013; Zhang et al., 2013]. Detailed calculation of SPI can be found in Zhang
et al. [2013, and references therein].
Considering precipitation of the previous months, the SPI can be used to represent drought at various
timescales (i.e., 1, 2, 3, 6, 9, 12, and 24 months). The 6 month SPI has been used by Meng and Shen [2013]
as a measure of soil moisture conditions in east China. However, so far, no confirmative evidence is available
as to the decision for timescales for SPI analysis. In this study, 3, 6, and 9 month SPIs were calculated as
measures of soil moisture conditions, and then the optimal timescale of SPI was selected when the relation
between summer monthly SPI and hot extremes was satisfactory.
3.4. Climatic Water Balance
To quantitatively analyze the influence of the soil moisture regimes on the relationship between precipitation
deficits and hot extremes, AACWB was employed to define soil moisture regimes. The AACWB is the average of
annual difference between precipitation and potential evapotranspiration (PET). The soil moisture regimes have
also been qualitatively classified into soil moisture-limited evapotranspiration regime and energy-limited
evapotranspiration regime by Hirschi et al. [2011] and Meng and Shen [2013], and the soil moisture-limited evapotranspiration regime nearly corresponds to the region with the negative AACWB, while energy-limited
evapotranspiration regime nearly corresponds to the region with the positive AACWB. This classification used
in the study is not exactly the same but similar to the soil moisture regimes introduced in Seneviratne
et al. [2010].
Besides, PET is the amount of evaporation and transpiration that would occur if sufficient water were
available. In this study, a modified form of the Hargreaves equation [Droogers and Allen, 2002; Hargreaves,
1994] was used to compute the monthly PET using the Hargreaves program in the R package of “SPEI”
[Beguería and Vicente-Serrano, 2013].
4. Results
4.1. Selection of Timescale of SPI
To select the optimal timescale for SPI as a proxy for precipitation deficit, the quantile regression slopes of
0.05–0.95 quantiles of summer monthly % HD in relation to 3, 6, and 9 month SPI for each subregion in
China were analyzed (Figure 2). The larger the slope, the stronger the relationship is between summer
monthly precipitation deficits and hot extremes; then it can be seen from Figure 2 that the relationship
between summer monthly %HD and precipitation deficit is stronger when the precipitation deficit is
represented by the 3 month SPI. Hence, the 3 month timescale was chosen for SPI for all the subregions. It
should be noted that in some subregions, the 3 month SPI and 6 month SPI perform nearly the same;
ZHANG ET AL.
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Figure 2. Quantile regression slopes of 0.05–0.95 quantiles of summer monthly % HD in relation to SPI with different
timescales for each subregion in China, (a) for western arid (semiarid) zone, (b) for Qinghai-Tibetan Plateau, (c) for east
arid zone, (d) for southwest China, (e) for northeast China, (f) for north China, (g) for central China, and (h) for south China.
The 90% confidence intervals of the estimated slopes are also shown as shading.
however, to maintain consistency, only the 3 month SPI has been selected as the index with the optimal
timescale for precipitation deficit. The same procedure was used to calculate the summer monthly
HWDmax, and the selected timescales for SPI were the same as those for % HD, as shown in Table 1.
Besides, it should be noted here that in these analyses regionally averaged hot extremes and SPI were used,
and the regional average was based on gridding as discussed in section 3.1.
Table 1. The Selected Timescale of SPI as a Measure of Precipitation Deficit for Each Subregion in China and Also the Estimated Slope of Summer % HD and
a
HWDmax at the 50th and 90th Percentiles (Including Their 90% Confidence Intervals) in Relation to the SPI With the Optimal Timescale
Summer %HD
Summer HWDmax
%/SPI
SPI Timescale
Western arid (semiarid) zone
Qinghai-Tibetan Plateau
East arid zone
Southwest China
Northeast China
North China
Central China
South China
a
3
3
3
3
3
3
3
3
Days/SPI
50th
0.02 [
0.06 [
0.06 [
0.07 [
0.08 [
0.05 [
0.07 [
0.04 [
0.04,0.01]
0.08, 0.00]
0.08, 0.04]
0.10, 0.04]
0.11, 0.07]
0.07, 0.02]
0.09, 0.02]
0.06, 0.01]
90th
0.06 [
0.07 [
0.12 [
0.14 [
0.13 [
0.05 [
0.14 [
0.08 [
0.09,
0.10,
0.14,
0.17,
0.14,
0.09,
0.19,
0.11,
SPI Timescale
0.00]
0.04]
0.00]
0.06]
0.06]
0.04]
0.04]
0.00]
3
3
3
3
3
3
3
3
50th
0.12 [
1.17 [
0.87 [
1.13 [
1.19 [
0.83 [
1.20 [
0.57 [
0.74, 0.07]
1.59, 0.04]
1.34, 0.55]
1.44, 0.56]
1.60 0.89]
1.14, 0.37]
1.46, 0.29]
1.05, 0.23]
90th
0.98 [
1.24 [
1.21 [
1.71 [
1.48 [
0.51 [
1.92 [
0.78 [
1.41, 1.09]
1.76, 0.68]
2.14, 0.07]
2.43, 0.81]
2.16, 0.61]
1.07, 0.10]
2.98, 0.07]
1.68, 0.02]
And the bold numbers indicate the slopes which are not significantly different from 0 at the 90% confidence intervals.
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Figure 3. Scatterplots of summer monthly % HD versus 3 month SPI for each subregion in China, (a) for western arid (semiarid)
zone, (b) for Qinghai-Tibetan Plateau, (c) for east arid zone, (d) for southwest China, (e) for northeast China, (f) for north China,
(g) for central China, and (h) for south China. The regression lines for 0.1, 0.3, 0.5 (median), 0.7, and 0.9 quantiles are also shown
in the figures, and for 90% confidence intervals of the slopes of quantile regression lines, see Figure 2.
4.2. Relationship Between Summer Monthly SPI and Hot Extremes
Figure 3 shows scatterplots of summer monthly % HD versus 3 month SPI in each subregion and also with
quantile regression lines fitted for quantiles 0.1, 0.3, 0.5, 0.7, and 0.9, corresponding to the lowest (10%
and 30%), median (50%), and highest (70% and 90%) of the sorted % HD. It can be seen from Figure 3 that
the slope of the quantile regression lines generally increases from 0.1 to 0.9 quantiles for all regions but
for the western arid (semiarid) zone and the Qinghai-Tibetan Plateau. Similar to the results of Meng and
Shen [2013], the increased negative slope toward higher % HD indicates a stronger correlation between
higher % HD quantile (hot extremes) and SPI in the east part of China. These negative slopes are statistically
different from 0 slope at the 90% level for most quantiles (Figure 2).
For the western arid (semiarid) zone and the Qinghai-Tibetan Plateau, it seems that there are no significant
relationships between summer monthly % HD and SPI. And this may be owing to the fact that these regions
are very dry with the average annual precipitation less than 100 mm, and also, most of these regions are
dominated by the desert or desert vegetation. Compared to the sensible heat, the latent heat is very small
in those regions. Any increase in precipitation will remain sufficiently small such that it will have only a limited
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Figure 4. Spatial distribution of gridded average annual climate water balance (AACWB) in China, and areas with no data
are depicted in white.
influence on the partitioning of incoming radiation. Consequently, significant relationships cannot be
expected between summer monthly % HD and SPI. In addition, a similar pattern was also found in the
quantile analysis between summer monthly HWDmax and SPI with the selected timescale (Table 1).
4.3. Influence of Soil Moisture Regime on the Relationship Between Summer Precipitation Deficits
and Hot Extremes
To quantitatively analyze the influence of soil moisture regime on the relationship between summer
precipitation deficit and hot extremes, the AACWB was used to define the soil moisture regimes. The
spatial distribution of gridded AACWB is shown in Figure 4, indicating that the AACWB is generally increasing
from northwest to southeast. As the station-based data are gridded onto a 1° latitude × 1° longitude grid, the
quantile regression analysis of summer % HD and HWDmax in relation to 3 month SPI was done for each grid.
Corresponding to the AACWB, the slopes of 0.5, 0.7, and 0.9 quantiles for % HD and HWDmax are shown
in Figure 5.
It can be observed from Figure 5 that when the negative AACWB draws closer to zero, the magnitude of
negative slope tends to increase and reach the maximum in the region with the near-zero AACWB
then the magnitude of negative slope tends to decrease with the increasing of positive AACWB, and these
relations are more significant for higher quantiles. Based on the definition of AACWB, it can be assumed that
all of the precipitation can be used for the evapotranspiration in the region with negative AACWB. As the
potential evapotranspiration is nearly the same, the negative AACWB is just increased with the increasing
of precipitation. So for the region with the negative AACWB drawing closer toward zero, there is more
precipitation for the latent heat (manifested as the evaporation). As the absorbed energy from the radiation
are also nearly the same, the proportion of latent heat compared to the absorbed total energy is increasing,
and this indicates that the precipitation deficit will have a greater influence on the partition of radiation to the
sensible heat at this region, so it can be expected that the magnitude of negative slope will increase when
the negative AACWB is nearing to zero. Besides, the vegetation generally ranges from the desert vegetation
to the grassland and to the forest when the negative AACWB is nearing toward zero, implying that the total
transpiration of plant is increased; then this further enhances the influences of precipitation deficit on the
partition of latent heat to the sensible heat.
However, it is hard to explain why the magnitude of negative slope tends to decrease with the increase of positive
AACWB. For the region with positive AACWB, the precipitation is more than the potential evapotranspiration;
then more precipitation will not increase the evapotranspiration and also not affect the vegetation coverage,
so it can be considered that the proportion of latent heat compared to the absorbed total energy is nearly the
same. Yet as transpiration is the largest contributor to the total land evapotranspiration [Seneviratne et al.,
2010], the characteristic of decreasing negative slope magnitude with the increase of positive AACWB may
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Figure 5. Relationships between slopes of 0.5, 0.7, and 0.9 quantiles and AACWB across China for (top row) % HD and (bottom row) HWDmax. The linear fits and
coefficients are also shown in all graphs considering separately negative and positive AACWB, and dots are shown as filled when the slopes of 0.5, 0.7, or 0.9
quantiles are significant at the 90% confidence intervals.
be owing to the different responses of vegetation to precipitation deficit. The less the total energy used by
transpiration, the more energy is available for sensible heating, and that would induce a larger increase of
near-surface air temperature [Seneviratne et al., 2010]. As the relationship between summer precipitation
deficit and hot extremes is much stronger in the region with lower positive AACWB, the transpiration in that
region should be less when responding to precipitation deficit.
Besides, for the region with more annual precipitation, the more basic elements such as calcium, magnesium,
sodium, and potassium held by soil colloids are leached by the precipitation, and they are replaced by
hydrogen ions; then the soils are inherently more acidic in this region and vice versa. It can be seen from
Figure 6 that the region with near-zero AACWB corresponds to the region with neutral soil with pH about
7 (Figure 6a), and the pH generally decreases with the increase of AACWB (Figure 6b). So these further verify
that there are less basic ions in the soil at the region with the higher positive AACWB. It has already been well
known that the plants need to absorb mineral nutrients to increase the suction pressure in the root cell to
Figure 6. (a) Spatial distribution of subsoil pH in China (areas with no data are depicted in white) and (b) the relationship with the AACWB.
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absorb water from soil solution to root xylem and which are then used for the transpiration. In this case, when
suffered from precipitation deficits, due to the higher concentration of soil solution in the region with lower
positive AACWB, the plants need to absorb more mineral nutrients to keep the suction pressure in the root
cell, especially during the extreme precipitation deficits. However, it has been found that roots absorb greater
amount of ions at a greater rate in dilute solutions of soil than in a relatively high concentration solutions which is
called “dilution effect” [Davis, 2009; Jarrell and Beverly, 1981]. Then there will be more reduction of the vegetation
transpiration in the region with lower positive AACWB, especially during the extreme precipitation deficits.
However, the physiological mechanisms behind vegetation response to the precipitation deficits still need to
be further analyzed. And a similar influence of the AACWB on the biomes responding to drought has also been
identified by Vicente-Serrano et al. [2013] that arid and humid biomes respond to drought at shorter timescales,
while semiarid and subhumid biomes respond to drought at longer timescales.
5. Conclusions
The relationship between summer precipitation deficit and hot extremes in China is investigated using the
quantile regression technique. Results indicate that the 3 month SPI can be selected as a proxy of precipitation
deficit for all subregions in China. Similar to the results of Meng and Shen [2013], summer hot extremes are
negatively correlated with precipitation deficit and the relationship is stronger for the higher end of the
distribution of summer hot extremes.
Acknowledgments
This work is financially supported by
the National Science Foundation for
Distinguished Young Scholars of China
(grant 51425903), the Xinjiang Science
and Technology Planning Project (grant
201331104), and the Natural Foundation
of Anhui Province (grant 1508085MD65)
and is fully supported by a grant from the
Research Grants Council of the Hong
Kong Special Administrative Region,
China (project CUHK441313). Daily precipitation and daily temperature data in
China are obtained from the National
Meteorological Information Center of
the China Meteorological Administration
in the website http://www.cma.gov.cn/
2011qxfw/2011qsjgx/, and the subsoil
(30–100 cm) pH data are extracted from
the Regridded Harmonized World Soil
Database v1.2 provided by the Oak
Ridge National Laboratory Distributed
Active Archive Center (http://daac.ornl.
gov/cgi-bin/dsviewer.pl?ds_id=1247).
Detailed information such as data can be
obtained by writing to the corresponding author at zhangq68@mail.sysu.edu.
cn. The last but not the least, my cordial
gratitude should be extended to the
Editor, L. Ruby Leung, and three
reviewers for their careful reviewing
and their pertinent and professional
comments and suggestions which are
greatly helpful for further improvement
of the quality of this paper.
ZHANG ET AL.
Furthermore, the influence of soil moisture regimes on the relationship between precipitation deficit and
summer hot extremes is quantitatively analyzed. Results indicate that the relationship between summer
precipitation deficit and hot extremes tends to be enhanced when the negative AACWB draws toward zero,
while it tends to be weakened with the increase of positive AACWB. For the region with the negative AACWB
that draws toward zero, the enhanced relationship should be attributed to the increase of the proportion of
latent heat compared to the absorbed total energy. However, the characteristics of weakened relationship
with the increasing of positive AACWB may be owing to the different responses of vegetation to precipitation
deficit. The less the total energy used by plants’ transpiration, the more energy is available for sensible
heating, which would induce a larger increase of near-surface air temperature [Seneviratne et al., 2010].
And then it can be expected that the transpiration in the region with lower positive AACWB should be less
when responding to precipitation deficit. However, the physiological mechanisms behind vegetation
response to the precipitation deficits still need to be further analyzed. Besides, as the AACWB generally
increases from northwest to southeast, the summer precipitation deficit-temperature coupling is the strongest in the strip stretching in the direction from southwest to northeast China.
References
Alexander, L. (2011), Climate science: Extreme heat rooted in dry soils, Nat. Geosci., 4(1), 12–13.
Alexander, L., et al. (2006), Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res., 111, D05109,
doi:10.1029/2005JD006290.
Beguería, S., and S. M. Vicente-Serrano (2013), SPEI: Calculation of the Standardised Precipitation-Evapotranspiration Index, R package
version 1.6. [Available at http://CRAN.R-project.org/package=SPEI.]
Caesar, J., L. Alexander, and R. Vose (2006), Large-scale changes in observed daily maximum and minimum temperatures: Creation and
analysis of a new gridded data set, J. Geophys. Res., 111, D05101, doi:10.1029/2005JD006280.
Davis, D. R. (2009), Declining fruit and vegetable nutrient composition: What is the evidence?, HortScience, 44(1), 15–19.
Droogers, P., and R. Allen (2002), Estimating reference evapotranspiration under inaccurate data conditions, Irrig. Drain. Syst., 16(1), 33–45.
Fischer, G., F. Nachtergaele, S. Prieler, H. T. van Velthuizen, L. Verelst, and D. Wiberg (2008), Global Agro-ecological Zones Assessment for
Agriculture (GAEZ 2008), IIASA, Laxenburg, Austria and FAO, Rome.
Ford, T. W., and S. M. Quiring (2014), In situ soil moisture coupled with extreme temperatures: A study based on the Oklahoma Mesonet,
Geophys. Res. Lett., 41, 4727–4734, doi:10.1002/2014GL060949.
Hargreaves, G. H. (1994), Defining and using reference evapotranspiration, J. Irrig. Drain. Eng., 120(6), 1132–1139.
Hirschi, M., S. I. Seneviratne, V. Alexandrov, F. Boberg, C. Boroneant, O. B. Christensen, H. Formayer, B. Orlowsky, and P. Stepanek (2011),
Observational evidence for soil-moisture impact on hot extremes in southeastern Europe, Nat. Geosci., 4(1), 17–21.
Jarrell, W. M., and R. B. Beverly (1981), The dilution effect in plant nutrition studies, Adv. Agron., 34, 197–224.
Koenker, R. (2005), Quantile Regression, Cambridge Univ. Press, New York.
Koenker, R. (2013), Quantreg: Quantile regression, R package version 5.05. [Available at http://CRAN.R-project.org/package=quantreg.]
Koster, R. D., Y. Sud, Z. Guo, P. A. Dirmeyer, G. Bonan, K. W. Oleson, E. Chan, D. Verseghy, P. Cox, and H. Davies (2006), GLACE: The Global
Land-Atmosphere Coupling Experiment. Part I: Overview, J. Hydrometeorol., 7(4), 590–610.
McKee, T. B., N. J. Doesken, and J. Kleist (1993), The relationship of drought frequency and duration to time scales, in Proceedings of Eighth
Conference on Applied Climatology, pp. 179–184, Am. Meteorol. Soc., Anaheim, Calif.
Meng, L., and Y. Shen (2013), On the relationship of soil moisture and extreme temperatures in east China, Earth Interact., 18(1), 1–20,
doi:10.1175/2013EI000551.1.
RAINFALL DEFICIT-TEMPERATURE COUPLING
10,048
Journal of Geophysical Research: Atmospheres
10.1002/2015JD023830
Mueller, B., and S. I. Seneviratne (2012), Hot days induced by precipitation deficits at the global scale, Proc. Natl. Acad. Sci. U.S.A., 109(31),
12,398–12,403.
New, M., M. Hulme, and P. Jones (2000), Representing twentieth-century space–time climate variability. Part II: Development of 1901–96
monthly grids of terrestrial surface climate, J. Clim., 13(13), 2217–2238.
Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling (2010), Investigating soil moisture–climate
interactions in a changing climate: A review, Earth Sci. Rev., 99(3–4), 125–161.
Stephenson, N. L. (1990), Climatic control of vegetation distribution: The role of the water balance, Am. Nat., 135, 649–670.
Vicente-Serrano, S. M., et al. (2013), Response of vegetation to drought time-scales across global land biomes, Proc. Natl. Acad. Sci. U.S.A.,
110(1), 52–57.
Wieder, W. R., J. Boehnert, G. B. Bonan, and M. Langseth (2014), Regridded Harmonized World Soil Database v1.2, Data set, Oak Ridge Natl.
Lab. Distributed Active Archive Cent., Oak Ridge, Tenn., doi:10.3334/ORNLDAAC/1247.
Xiao, M. Z., Q. Zhang, V. P. Singh, and X. H. Chen (2013), Regionalization-based spatiotemporal variations of precipitation regimes across
China, Theor. Appl. Climatol., 114(1–2), 203–212.
Zhai, P., X. Zhang, H. Wan, and X. Pan (2005), Trends in total precipitation and frequency of daily precipitation extremes over China, J. Clim.,
18(7), 1096–1108.
Zhang, Q., V. P. Singh, J. Li, and X. Chen (2011), Analysis of the periods of maximum consecutive wet days in China, J. Geophys. Res., 116,
D23106, doi:10.1029/2011JD016088.
Zhang, Q., M. Xiao, V. Singh, and X. Chen (2013), Copula-based risk evaluation of droughts across the Pearl River Basin, China, Theor. Appl.
Climatol., 111(1–2), 119–131.
ZHANG ET AL.
RAINFALL DEFICIT-TEMPERATURE COUPLING
10,049