diurnal temperature range trend over North Carolina and the associated mechanisms.pdf

Diurnal temperature range trend over North Carolina and the
associated mechanisms
Mohammad Sayemuzzaman a,
⁎, Ademe Mekonnen a
, Manoj K. Jha b
a
Energy and Environmental System Department, North Carolina A&T State University, Greensboro, NC, USA
b
Department of Civil, Architectural and Environmental Engineering, North Carolina A&T State University, Greensboro, NC, USA
a b s t r a c t
a r t i c l e i n f o
Article history:
Received 20 September 2014
Received in revised form 4 March 2015
Accepted 6 March 2015
Available online 7 April 2015
Keywords:
Diurnal temperature range
Trends
Total cloud cover
Precipitation
Associative mechanism
This study seeks to investigate the variability and presence of trend in the diurnal surface air temperature range
(DTR) over North Carolina (NC) for the period 1950–2009. The significance trend test and the magnitude of
trends were determined using the non-parametric Mann–Kendall test and the Theil–Sen approach, respectively.
Statewide significant trends (p b 0.05) of decreasing DTR were found in all seasons and annually during the anal-
ysis period. Highest (lowest) temporal DTR trends of magnitude −0.19 (−0.031) °C/decade were found in sum-
mer (winter). Potential mechanisms for the presence/absence of trend in DTR have been highlighted. Historical
data sets of the three main moisture components (precipitation, total cloud cover (TCC), and soil moisture) and
the two major atmospheric circulation modes (North Atlantic Oscillation and Southern Oscillation) were used for
correlation analysis. The DTRs were found to be negatively correlated with the precipitation, TCC and soil mois-
ture across the state for all the seasons and annual basis. It appears that the moisture components related better
to the DTR than to the atmospheric circulation modes.
© 2015 Elsevier B.V. All rights reserved.
1. Introduction
Several studies have shown an increase in surface average air tem-
peratures over many places of the globe in recent decades (e.g. Jones
et al., 1999; Trenberth et al., 2007, 235–336). Warming takes place
more at night than during the daylight hours (e.g., Karl et al., 1993;
Easterling et al., 1997). This suggests that increasing trends in daily min-
imum temperatures (Tmin) are larger than maximum temperatures
(Tmax) leading to a significant reduction in diurnal temperature range
(DTR; DTR = Tmax − Tmin). Given the observed wide spread increasing
trends in Tmin and subsequently decreasing trends in DTR over the
globe, many scientists have proposed various causal mechanisms re-
sponsible for these trends. A number of researches indicate that the
downward trends in DTR are related to upward trends in cloud cover,
precipitation and soil moisture over the globe (Karl et al., 1993; Dai
et al., 1997, 1999; Zhou et al., 2009). Dai et al. (1999) in their global
study concluded that the reduction of DTR can be attributed primarily
to the increases of cloud cover and secondarily to the precipitation
and soil moisture. Clouds can increase Tmin by enhancing long wave ra-
diation back to the earth surface at night and the reduction of Tmax in the
day time for decreasing short wave radiation via reflection or scattering
(Dai et al., 1999). Dai et al. (1999) estimated that cloudy days can reduce
the DTR by 25–50%, compared to clear sky days. Karl et al. (1993) found
annual and seasonal DTRs are strongly correlated with cloud cover with
the highest correlation in autumn in the contiguous United States.
Lauritsen and Rogers (2012) reported that post-1950 DTRs began de-
clining at various times ranging from around 1910 to the 1950s in the
United States. They found cloud cover alone accounts for up to 63.2%
of regional annual DTR variability across the United States. Lauritsen
and Rogers (2012) also suggested that cloud cover, precipitation, soil
moisture, and atmospheric/oceanic teleconnection indices account for
up to 80.0% of regional variances over 1901–2002. However they also
indicated that atmospheric/oceanic teleconnection accounts for small
portions of this variability.
Temperature extremes (Tmin and Tmax) and DTRs may also be associ-
ated with both regional and large scale precipitation (Trenberth and Shea,
2005; Trenberth et al., 2007). Strong negative correlations found between
temperature and precipitation are mostly in the warm season, as dry
conditions are associated with more sunshine and less evaporative
cooling while wet summers often have cool temperatures (Trenberth
and Shea, 2005).
Soil moisture is one of the main land surface parameters that affect
sub seasonal to seasonal variability and predictability of the atmosphere
(e.g., Mahmood et al., 2012). Over the last several decades the role of
soil moisture in climate prediction has gained significant attention
(e.g., Entin et al., 2000; Wu and Dickinson, 2004). Zhang et al. (2009) fo-
cused on the summer season to identify the soil moisture influences on
daily Tmax, Tmin and DTR. During the summer season oceanic impacts
are smaller on the mid latitude land areas than the soil moisture
(e.g., Koster and Suarez, 1995). Zhang et al. (2009) identified, the soil
Atmospheric Research 160 (2015) 99–108
⁎ Corresponding author at: Energy and Environmental System Department, North
Carolina A&T State University, 301 Gibbs Hall, Greensboro, NC 27411, USA.
E-mail address: msayemuz@aggies.ncat.edu (M. Sayemuzzaman).
http://dx.doi.org/10.1016/j.atmosres.2015.03.009
0169-8095/© 2015 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Atmospheric Research
journal homepage: www.elsevier.com/locate/atmos

moisture shows a negative feedback on DTR over the zone from California
through the Midwest to the Southeast of the United States mainly
through its “damping effect on Tmax.” They also suggested that the soil
moisture feedback-induced variability accounts for about 10–20% of the
total DTR variances over regions where strong feedbacks are identified.
Surface-atmosphere or land-ocean fluxes may have little impact on
the globally-averaged energy budget, but can significantly affect region-
al conditions due to the surface exchanges, or fluxes within the earth's
overall energy system Lauritsen and Rogers (2012). The complicated to-
pography in North Carolina (that ranges from 46 m from the eastern
coastal area to the western mountain area of 1829 m above mean sea
level) with 3 distinct physiographic regions exhibits the complex cli-
mate behavior in the eastern United States region (Boyles and Raman,
2003; Robinson, 2005). Low DTR and/or increasing Tmin can affect for
example, cattle and hogs in NC by the heat stress. NC is the largest hog
producer in the U.S. Warmer climates and less soil moisture due to in-
creased evaporation may increase the need for irrigation. However,
these same conditions could decrease water supplies, which also may
be needed by natural ecosystems, urban populations, industry, and
other users. Warmer and drier conditions could increase the frequency
and intensity of fires, and result in increased losses to important com-
mercial timber areas. Even warmer and wetter conditions could stress
forests by increasing the winter survival of insect pests.
This paper analyzes the spatio-temporal trends of the DTR over NC
for the period 1950–2009 (see Fig. 1 for orientation). The Mann–Kendall
(MK) test and the Theil–Sen approach (TSA) non-parametric statistical
methodology were adopted to detect the DTR trend significance and
magnitude, respectively. Correlation analyses between DTR and mois-
ture parameters (precipitation, TCC (total cloud cover), and soil mois-
ture) and with the atmospheric circulation (North Atlantic Oscillation,
and Southern Oscillation) indices were analyzed.
1.1. Data
We use three different datasets: 1) daily Tmax, Tmin and precipitation
datasets from 249 meteorological stations well distributed across NC
Fig. 1. Spatial distribution of 249 ground based weather stations for Tmax, Tmin and precipitation data (upper panel), 0.25°
longitude × 0.25°
latitude 240 grids for soil moisture ERA-40 data
(middle panel), and 1°
longitude × 1°
latitude 19 grids for TCC ERA-40 data (lower panel).
100 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

(USDA-ARS, 2013) for the time period of Jan. 1, 1950 to Dec. 31, 2009.
Total cloud cover (TCC) data on monthly time scale at 1°
longitude × 1°
latitude resolution (total 19 grids) and at 0.25°
longitude × 0.25°
latitude resolution (total 240 grids) and soil
moisture from the European Centre for Medium-Range Weather Fore-
casts (ECMWF) Re-Analysis (ERA-40) gridded data (Uppala et al.,
2005); and 3) the North Atlantic Oscillation (NAO) and Southern Oscil-
lation Index (SOI) monthly data (1958–2001) from the Climate Predic-
tion Center of NOAA (CPC, 2014).
Details about daily Tmax, Tmin and precipitation datasets with regard
to completeness and quality control information is provided in a com-
panion paper (Sayemuzzaman and Jha, 2014a,b). The ERA-40 project
(Uppala et al., 2005) has produced a comprehensive global analysis for
the 45-year period covering September 1957 to August 2002. The
ERA-40 data are available four times per day (00:00, 06:00, 12:00, and
18:00 UTC). The ERA-40 data is stored as Network Common Data
Format (NetCDF).
1.2. Analysis methods
1.2.1. Trend test
Various statistical methods have been utilized over the years to
study hydro-climatological variables (Modarres and Sarhadi, 2009;
Martinez et al., 2012; Sonali and Nagesh, 2013; Chang and
Sayemuzzaman, 2014). Non-parametric and/or non-stationary time se-
ries methods have been favored over parametric methods due to their
robustness and flexibility (Sonali and Nagesh, 2013; Gorji Sefidmazgi
et al., 2014a). In this study, statistical significance of a trend in time
Fig. 2. Annual and seasonal normalized anomalies of observed DTR, Tmax, and Tmin averaged over the 249 stations are presented for the period 1950–2010. The trends (°C/10 yr) are listed
on the plots calculated using TSA, and all are statistically significant (p b 0.05). The time series of data were normalized by subtracting their mean divided by their standard deviation (for
visualization purpose only).
101
M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

series is assessed using the Mann–Kendall (MK) test. The MK test is a
rank based non-parametric test (Mann, 1945; Kendall, 1975). It has
been widely used to detect trends in hydro-meteorological time series
(Modarres and Sarhadi, 2009; Jhajharia et al., 2009, 2014; Martinez
et al., 2012; Sayemuzzaman, 2014; Sayemuzzaman et al., 2014a,b).
We have applied the MK test to detect DTR, Tmax, and Tmin trends in
seasonal and annual time series and to examine whether results are
statistically significant at the 99% and 95% confidence levels for the
period of study (1950–2009).
1.2.2. Trend magnitude
A complete analysis of climate variability analysis would also require
exploring trend magnitude besides trend significance. For this purpose,
a nonparametric method referred to as the Theil–Sen approach (TSA) is
employed (Theil, 1950; Sen, 1968). This approach provides a more ro-
bust slope estimate than the least-squares method because of its insen-
sitivity to outliers or extreme values. This method also compares well
against simple least squares even for normally distributed data in the
time series (Hirsch et al., 1982; Jianqing and Qiwei, 2003). The TSA
approach is also known as the Sen Slope estimator and has been widely
used for trend magnitude prediction in hydro-meteorological time
series (e.g., Martinez et al., 2012).
1.2.3. Independence of data
The MK test and TSA approach require the time series to be serially in-
dependent. The existence of serial correlation will affect the test's ability
to assess the trend significance (see, e.g., Von Storch, 1995; Yue and
Wang, 2002). Therefore, it is important that the significance of serial cor-
relation of a time series prior to using the MK and TSA methods be
assessed. The lag-1 serial coefficient (r1) of sample data Xi (Salas et al.,
1980) has been utilized (Tabari et al., 2011) to compute the independence
of the data series and was adopted in this study. The ‘pre-whitened’ meth-
od was applied to eliminate the serial correlation from the precipitation
and temperature monthly scale data series (Sonali and Nagesh, 2013).
Fig. 3. Spatial distribution of the 249 stations with MK trend test and the trend magnitude calculated by TSA and interpolated (°C/yr) of DTR data on annual and seasonal scale for the period
1950–2009.

2. Results
2.1. Temporal DTR, Tmax and Tmin trends
Annual and seasonal averages (over the 249 stations) of DTR, Tmax,
and Tmin for the period 1950–2010 are shown in Fig. 2. The annual
mean trends of DTR, Tmax, and Tmin are −0.12, +0.04, and +0.08 °C
per decade, respectively, and are all statistically signiﬁcant (p b 0.05).
On average, decreasing Tmax is observed during summer (−0.06 °C/
decade) and spring (−0.12 °C/decade). Increasing Tmin is found dur-
ing summer (+0.13 °C/decade) and spring (+0.014 °C/decade). Pro-
nounced decreasing DTR trend −0.19 °C/decade notices during
summer. The fall season exhibits the highest increasing Tmin trends
of about +0.14 °C/decade, which creates a steep DTR decreasing
trend −0.12 °C/decade even though Tmax trends increase by
+0.016 °C/decade. Spring shows the lowest DTR decreasing trend of
−0.014 °C/decade among the annual and seasonal trends with the
Tmax, and Tmin trends of about −0.12, and +0.014 °C/decade,
respectively.
2.2. Spatial DTR trends
Fig. 3 shows spatial patterns of observed annual and seasonal DTR
trends for the 249 stations during the period 1950–2010. It represents
both the signiﬁcance of the trends of the station's position which was
calculated using MK trend test and the interpolated trend magnitude
(°C/yr) estimated by TSA.
Statewide decreasing tendency of DTR (ranging from −0.50 °C/yr to
no-trend) was found in summer, spring and on annual time scale. Also,
decreasing tendency of DTR was observed during fall and winter season
(ranging from −0.50 to 0 °C/yr) statewide except for some areas, which
exhibit mild positive trends (ranging from 0 to +0.40 °C/yr). A recent
analysis by Sayemuzzaman et al. (2014a) showed decreasing Tmin
trends (ranges from −0.05 to −0.005 °C/yr) and increasing Tmax trends
(ranges from 0.005 to 0.05 °C/yr) over western NC and increasing DTR
trends in winter, fall and spring seasons. However southern coastal
areas of NC exhibits increasing DTR trends (Fig. 3) in all seasons,
which were created from both the decreasing Tmin and Tmax trends pre-
sented in Sayemuzzaman et al. (2014a), in this case the rate of decreas-
ing Tmin is higher than that of the Tmax.
2.3. Precipitation, cloudiness, and soil moisture time series
Fig. 4 shows the climatological annual and seasonal precipitation (in
mm/day, 1950–2010), TCC and soil moisture (in percent, 1958–2001).
As can be seen in the left panel of Fig. 4, the mountain zone in NC re-
ceives higher precipitation (ranging from 3.50 to 5.00 mm/day) in all
the seasons and on the annual time scale. Other areas of NC receive
the lowest precipitation (ranging from 2.25 to 3.50 mm/day) in winter,
spring, and fall seasons. Statewide precipitation is higher during sum-
mer (ranging from 3.50 to 5.00 mm/day) with the highest precipitation
(ranging from 5.00 to 6.25 mm/day) occurring in the southern coastal
areas.
Climatological TCC data are presented in the middle panel of Fig. 4.
The variation of TCC has not been noticed in NC on seasonal and annual
Fig. 4. Spatial patterns of climatological seasonal and annual precipitation (mm/yr), TCC (%/yr) and soil moisture (%/yr) during the period of 1950–2010 for precipitation and 1958–2001
for TCC, and soil moisture.
103

timescales, except for the mountain zone (piedmont zone). It is seen
that in winter (fall), the highest (lowest) percentage of TCC ranges
from 55 to 60 (40 to 45) %. Climatological soil moisture is presented
on the right panel in Fig. 4. No significant spatial variations are observed
in soil moisture. It is seen in Fig. 4 that some parts of the coastal zone
exhibit lower ranges (from 0 to 10 and 10 to 25%) than the other parts
of NC.
2.4. The relationship between temperature and moisture components and
oscillation indices
Analysis of correlation coefficients between climatological tempera-
tures (DTR, Tmax and Tmin) and climatological moisture components
(precipitation, TCC, and soil moisture), and atmospheric and oceanic in-
dices (NAO, ENSO) from 1958–2001 are presented in Table 1 for raw
Table 1
Correlation coefficients between climatological temperatures (DTR, Tmax and Tmin) and climatological moisture components (precipitation, TCC, and soil moisture), and oscillation indices
(NAO, ENSO) from 1958–2002. Left and right panels represent the original and the detrended data.
Original data Detrended data
Precipitation TCC Soil moisture NAO ENSO Precipitation TCC Soil moisture NAO ENSO
Winter Tmax −0.05 0.03 −0.36 0.26 −0.18 −0.07 −0.12 −0.34 0.10 −0.17
Tmin 0.31 0.40 −0.12 0.45 −0.18 0.31 0.28 −0.07 0.29 −0.17
DTR −0.65 −0.67 −0.50 −0.30 −0.03 −0.65 −0.69 −0.52 −0.30 −0.04
Spring Tmax −0.58 −0.34 −0.55 0.03 −0.03 −0.59 −0.35 −0.56 0.02 −0.03
Tmin −0.17 0.12 −0.18 0.00 0.00 −0.17 0.11 −0.17 0.00 0.00
DTR −0.61 −0.65 −0.56 0.04 −0.03 −0.64 −0.65 −0.59 0.03 −0.05
Summer Tmax −0.58 −0.48 −0.64 0.30 0.40 −0.58 −0.53 −0.64 0.29 0.41
Tmin −0.01 0.00 −0.14 0.06 0.23 −0.01 −0.08 −0.10 0.04 0.27
DTR −0.72 −0.61 −0.67 0.32 0.28 −0.73 −0.60 −0.73 0.34 0.26
Fall Tmax −0.17 −0.04 −0.26 0.01 0.06 −0.17 −0.06 −0.25 0.01 0.07
Tmin 0.27 0.48 0.15 −0.14 0.02 0.28 0.48 0.16 −0.15 0.04
DTR −0.66 −0.80 −0.59 0.23 0.06 −0.67 −0.81 −0.61 0.24 0.05
Annual Tmax −0.06 0.20 −0.18 0.24 −0.16 −0.05 0.08 −0.13 0.16 −0.13
Tmin 0.34 0.45 −0.01 0.29 −0.08 0.39 0.32 0.09 0.18 −0.02
DTR −0.63 −0.45 −0.24 −0.14 −0.10 −0.66 −0.39 −0.32 −0.05 −0.14
Bold, underline and italics represent the significance level ≪0.001.
Bold only represents the significance level b0.01.
Fig. 5. Time series and trends of climatological DTR (°C/yr.) and precipitation (mm/yr) of each station data are graphically presented in seasonal and annual time scales over the period of
1950–2010. Values of r greater than 0.30 or less than −0.30 represent significant statistics at the 0.05 level.

data (left side) and detrended data (right side). Simple correlation anal-
ysis of the temperature and moisture/oscillation may present spurious
associations due to the presence of strong trends (sustained upward
or downward movements) in the time series (Gujarati, 1995). We dis-
criminate the original time series (by removing the linear trends) to re-
duce the possibility of such spurious associations, and then, examine a
potential relationship. Results show a slight difference in the correlation
coefficients between the original time series and the detrended time se-
ries. Almost all of the correlation coefficients for DTR and moisture com-
ponents are statistically significant (p b 0.001) in all seasons except
annual DTR and soil moisture which are found to be −0.24. DTRs are
negatively correlated with the moisture components. It appears from
Fig. 6. Correlation coefficient between climatological DTR (°C/yr) and precipitation (mm/day) of each station data with the statewide spatial interpolation in seasonal and annual time
scales over the period of 1950–2010. Values greater than 0.30 or less than −0.30 are statistically significant at the 0.05 level.
Fig. 7. Time series and trends of climatological DTR (°C/yr) and TCC of each station data are graphically presented in seasonal and annual time scales over the period of 1950–2010. Values
of r greater than 0.30 or less than −0.30 represent significant statistics at the 0.05 level.
105

Table 1 that DTRs are not significantly correlated with the oscillation in-
dices in seasonal and annual time scales. As the detrended correlations
differ by only small amounts with the original data set correlations, fur-
ther analysis was based on the original data sets.
As observed in Figs. 5, 6 and Table 1, temporal and spatial DTRs are
negatively correlated with the precipitation. Temporal precipitation in-
creases in seasonal and annual timescales are observed (Fig. 5), except
for the winter season, which is consistent with the previous spatial pre-
cipitation trend analysis of Sayemuzzaman and Jha (2014a, 2014b). In
summer (winter) season, the highest (lowest) temporal DTR trends of
−0.019 (−0.0031 °C/yr.) are shown in Fig. 5. In temporal precipitation
trends the highest (lowest) were found in fall (spring) at +0.39
(+0.12) mm/year. Statewide significant (p b 0.05) negative correlation
was found in winter, spring, summer and fall seasons except in some
portions of the coastal area. Sayemuzzaman et al. (2014b) predicted
statewide decreasing Tmax trends except in the coastal zone, which
could be the reason for less significant spatial correlation exhibited in
the coastal zone in Fig. 6.
Similarly, precipitation and DTRs are negatively correlated with TCC
temporally and spatially (Figs. 7 and 8). The highest (lowest) temporal
TCC trends were found in winter (fall) season of +0.16 (+0.10) %/yr,
which exhibited higher negative correlation of −0.67 (−0.80) in com-
parison with seasons and annually. Statewide significant (0.001 b
p b 0.05) negative spatial correlation was found in all the seasons but
not annually. Many analyses of these cloudiness records suggest in-
creased total cloud cover from 1950 to 1980 over the U.S. because
clouds block sunlight and reduce daytime maximum temperatures,
which is the dominant effect on DTR, as shown by Dai et al. (1999).
Soil moisture trends decreased in all the seasonal and annual time
scales for the period of 1958–2001 (Fig. 9). The highest (lowest) tempo-
ral soil moisture trends were found in summer (spring) season at
−0.054 (−0.033) %/yr. It is shown in Fig. 9 that DTRs and soil moisture
exhibit significant (p b 0.001) negative correlation in all the seasons but
not annually. Summer season shows the highest correlation (r =
−0.67, p = 0.000).
3. Discussion
Increased cloud cover, precipitation, and soil moisture have been
found to be associated with the reduction of the DTR in NC. However,
the DTR can be changed through a number of mechanisms such as
land use/cover, atmospheric composition (aerosols, GHGs), water
vapor, etc. Since NC does not possess very significant land cover land
use (LCLU) change (Sayemuzzaman and Jha, 2014a,b), thus excluding
the effects of LCLU change on the DTR trend is reasonable. At regional
scales, increased clouds and precipitation are the most effective and pri-
mary factors in controlling DTR changes than the changes from green-
house gases and aerosols (Dai et al., 1999). Dai et al. (1999) concluded
that the reduction of DTR can be attributed primarily to the increases
of cloud cover and secondarily to the precipitation and soil moisture. Ac-
cording to Dai et al. (1999) cloudy days can reduce the DTR by 25–50%,
compared to clear sky days over the globe.
Gorji Sefidmazgi et al. (2014b) found that the natural variability of
climate change in NC during 1950–2009 can be explained mostly by
the Atlantic Multi-decadal Oscillation (AMO) and solar activity. Howev-
er, in this study our results show that the correlations between the DTR
and the moisture components (precipitation, TCC, and soil moisture)
are higher than that of the atmospheric circulation (NAO, SOI) with
the DTR in NC (Table 1). In this study, statewide significant (p b 0.05)
decreasing DTR trends were noticed over the period of 1950–2009,
which is consistent with the results of Sayemuzzaman et al. (2014b).
The highest reduction of DTR, found in summer season, may be associ-
ated with the combination of the higher increasing trends of TCC
(with r = −0.61) and precipitation (with r = −0.73), and the lowest
decreasing soil moisture trends (with r = −0.67). This is expected be-
cause during warm temperatures and dry ground surface which are
pronounced in NC in summer time than in any other season, surface la-
tent heat release is limited so that the daytime Tmax depends more on
the solar heating and thus clouds. Dai et al. (1999) also predicted the
DTR reduction by clouds is largest in warm and dry seasons over north-
ern mid-latitude regions (such as the U.S., southern Canada, and
Fig. 8. Correlation coefficient between climatological DTR (°C/yr) and TCC of each station data with the statewide spatial interpolation in seasonal and annual time scales over the period of
1950–2010. Values greater than 0.30 or less than −0.30 are statistically significant at the 0.05 level.

Europe), which has been found in this research for the NC region. Karl
et al. (1993) found annual and seasonal DTRs are strongly correlated
with cloud cover with the highest correlation in autumn in the contigu-
ous United States. Karl et al.'s (1993) finding is in broad agreement with
the results found in this study (r = −0.80, Fig. 6) between DTR and TCC
in fall season compared with other seasons and annually.
4. Conclusions
Overall, the goal of this study was to identify the spatio-temporal
trends of DTR variability and to obtain some estimates of the potential
causes of that variability for the period of 1950–2009 over North Caroli-
na. Non-parametric statistical methods (Mann–Kendall test, Theil
Sen approach) were adopted to find the trend significance and magni-
tude. Historical data sets of the three main moisture components
(precipitation, total cloud cover (TCC), and soil moisture) and the two
major atmospheric circulation modes (North Atlantic Oscillation and
Southern Oscillation) were used for correlation analysis. Statewide sig-
nificant trends (p b 0.05) of decreasing DTR were found in all seasons
and annually during the analysis period. The highest (lowest) temporal
DTR trends at magnitude −0.19 (−0.014) °C/decade were found in
summer (spring). Our results show that the moisture components
(precipitation, TCC and soil moisture) have higher association with
DTR than the atmospheric circulation (NAO and SOI). The highest
reduction of DTR, found in summer season, may be associated with
the combination of the higher increasing trends of TCC (with r =
−0.61) and precipitation (with r = −0.73), and the lowest decreasing
soil moisture trends (with r = −0.67). However, the observed DTR
trends may be affected by the increased concentrations of greenhouse
gases, sulfate aerosols and/or with the evapo-transpiration changes
that have not been investigated in this study, which will be in future
research interests.
Acknowledgments
The authors would like to express their special gratitude to Dr. Keith
A. Schimmel, Chair of Energy and Environmental System Department,
for his support. The authors would also like to thank the two anony-
mous reviewers for their suggestions to improve the contents of this
paper.
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