th a boro
Uni te th
NC g th (p b tem han ois ircu s w e seasons and annual basis. It appears that the moisture components related better ease in be in re 35–336 t hour t incre than m and less evaporative peratures (Trenberth parameters that affect
Atmospheric Research 160 (2015) 99–108
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Atmospheric .e lsdiation 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 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 attentionet 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 raconditions are associated with more sunshine cooling while wet summers often have cool tem and Shea, 2005).
Soil moisture is one of the main land surfacetrends in Tmin and subsequently decreasing trends in DTR over the globe, many scientists have proposed various causal mechanisms responsible 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 portions of this variability.
Temperature extremes (Tmin and Tmax) and DTRs may also be associatedwith both regional and large scale precipitation (Trenberth and Shea, 2005; Trenberth et al., 2007). Strongnegative correlations foundbetween temperature and precipitation are mostly in the warm season, as drythe DTR by 25–50%, compared to clear sky day ⁎ Corresponding author at: Energy and Environmen
Carolina A&T State University, 301 Gibbs Hall, Greensboro
E-mail address:email@example.com (M. Sayem http://dx.doi.org/10.1016/j.atmosres.2015.03.009 0169-8095/© 2015 Elsevier B.V. All rights reserved.aximum temperatures urnal temperature range d wide spread increasing 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(Tmax) leading to a significant reduction in di (DTR; DTR = Tmax− Tmin). Given the observe1. Introduction
Several studies have shown an incr peratures over many places of the glo et al., 1999; Trenberth et al., 2007, 2 more at night than during the dayligh
Easterling et al., 1997). This suggests tha imum temperatures (Tmin) are larger© 2015 Elsevier B.V. All rights reserved. surface average air temcent decades (e.g. Jones ). Warming takes place s (e.g., Karl et al., 1993; asing trends in dailyminannual 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 declining 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, soilAssociative mechanism to the DTR than to the atmospheric circulation modes.Total cloud cover
Precipitation ture across the state for all thDiurnal temperature range trend over Nor associated mechanisms
Mohammad Sayemuzzaman a,⁎, Ademe Mekonnen a, M a Energy and Environmental System Department, North Carolina A&T State University, Greens b Department of Civil, Architectural and Environmental Engineering, North Carolina A&T State a b s t r a c ta r t i c l e i n f o
Received 20 September 2014
Received in revised form 4 March 2015
Accepted 6 March 2015
Available online 7 April 2015
Diurnal temperature range
This study seeks to investiga (DTR) over North Carolina ( trends were determined usin
Statewide significant trends ysis period. Highest (lowest) mer (winter). Potential mec data sets of the three main m the twomajor atmospheric c correlation analysis. The DTR j ourna l homepage: wwws. Karl et al. (1993) found tal System Department, North , NC 27411, USA. uzzaman).Carolina and the noj K. Jha b , NC, USA versity, Greensboro, NC, USA e variability and presence of trend in the diurnal surface air temperature range ) for the period 1950–2009. The significance trend test and the magnitude of e non-parametric Mann–Kendall test and the Theil–Sen approach, respectively. 0.05) of decreasing DTRwere found in all seasons and annually during the analporal DTR trends ofmagnitude−0.19 (−0.031) °C/decadewere found in sumisms for the presence/absence of trend in DTR have been highlighted. Historical ture components (precipitation, total cloud cover (TCC), and soil moisture) and lationmodes (North Atlantic Oscillation and SouthernOscillation)were used for ere found to be negatively correlated with the precipitation, TCC and soil moisResearch ev ie r .com/ locate /atmos(e.g., Entin et al., 2000; Wu and Dickinson, 2004). Zhang et al. (2009) focused 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 moisture shows anegative feedback onDTR over the zone fromCalifornia 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 regional conditions due to the surface exchanges, or fluxes within the earth's overall energy system Lauritsen and Rogers (2012). The complicated topography 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 climate 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 increased 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 commercial timber areas. Even warmer and wetter conditions could stress forests by increasing the winter survival of insect pests.