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

1. 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 analysis period. Highest (lowest) temporal DTR trends of magnitude −0.19 (−0.031) °C/decade were found in summer (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 moisture 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 temperatures 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 minimum 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 radiation 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 associated 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

2. 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 regional 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 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 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 moisture parameters (precipitation, TCC (total cloud cover), and soil moisture) 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

3. (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 series 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

4. 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 correlation 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’ method 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. 102 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

5. 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 during 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 presented in Sayemuzzaman et al. (2014a), in this case the rate of decreasing 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 summer (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 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

6. 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 temperatures (DTR, Tmax and Tmin) and climatological moisture components (precipitation, TCC, and soil moisture), and atmospheric and oceanic indices (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. 104 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

7. data (left side) and detrended data (right side). Simple correlation analysis 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 reduce 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 series. Almost all of the correlation coefficients for DTR and moisture components 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 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

8. Table 1 that DTRs are not significantly correlated with the oscillation indices 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 increases in seasonal and annual timescales are observed (Fig. 5), except for the winter season, which is consistent with the previous spatial precipitation 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 increased 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) temporal 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 greenhouse 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 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). This is expected because 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. 106 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

9. 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 contiguous 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 magnitude. 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 significant 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. References Boyles, R.P., Raman, S., 2003. Analysis of climate trends in North Carolina (1949–1998). Environ. Int. 29, 263–275. Chang, S.Y., Sayemuzzaman, M., 2014. Using unscented Kalman filter in subsurface con- taminant transport models. J. Environ. Inf. 23 (1), 14–22. http://dx.doi.org/10.3808/ jei.201400253. CPC., 2014. North Atlantic Oscillation (NAO): Climate Prediction Center (CPC), National Oceanic and Atmospheric Administration (NOAA). http://www.cpc.ncep.noaa.gov/ products/precip/CWlink/pna/nao.shtml (Accessed March, 2014). Fig. 9. Time series and trends of climatological DTR (°C/yr) and average soil moisture (°C/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. 107 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

10. Dai, A., Del Genio, A.D., Fung, I.Y., 1997. Clouds, precipitation and temperature range. Nature 386, 665–666. Dai, A., Trenberth, K.E., Karl, T.R., 1999. Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. J. Clim. 12, 2451–2473. Easterling, D.R., Horton, B., Jones, P.D., Peterson, T.C., Karl, T.R., Parker, D.E., Salinger, M.J., Razuvayev, V., Plummer, N., Jamason, P., Folland, C.K., 1997. Maximum and minimum temperature trends for the globe. Science 277, 364–367. Entin, J.K., Robock, A., Vinnikov, K.Y., Hollinger, S.E., Liu, S., Namkhai, A., 2000. Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res. 105 (11865e11877). Gorji Sefidmazgi, M., Sayemuzzaman, M., Homaifar, A., 2014a. “Non-stationary Time Series Clustering with Application to Climate Systems,” in Advance Trends in Soft Computing. In: Jamshidi, M., Kreinovich, V., Kacprzyk, J. (Eds.), 10.1007/978-3-319- 03674-8_6 312. Springer International Publishing, pp. 55–63. Gorji Sefidmazgi, M., Sayemuzzaman, M., Homaifar, A., Jha, M.K., Liess, S., 2014b. Trend analysis using non-stationary time series clustering based on the finite element method. Nonlinear Processes Geophys. 2 (3), 605–615. http://dx.doi.org/10.5194/ npg-21-605-2014. Gujarati, D.N., 1995. Basic econometrics. 3rd edn. McGraw-Hill, New York 0-07-025214-9. Hirsch, R.M., Slack, J.R., Smith, R.A., 1982. Techniques of trend analysis for monthly water quality data. Water Resour. Res. 18, 107–121. Jhajharia, D., Shrivastava, S.K., Sarkar, D., Sarkar, S., 2009. Temporal characteristics of pan evaporation trends under the humid conditions of northeast India. Agric. For. Meteorol. 149 (5), 763–770. Jhajharia, D., Dinpashoh, Y., Kahya, E., Choudhary, R.R., Singh, V.P., 2014. Trends in temperature over Godavari River basin in Southern Peninsular India. Int. J. Climatol. 34 (5), 1369–1384. Jianqing, F., Qiwei, Y., 2003. Nonlinear time series: nonparametric and parametric methods, springer series in statistics. Jones, P.D., New, M., Parker, D.E., Martin, S., Rigor, I.G., 1999. Surface air temperature and its changes over the past 150 years. Rev. Geophys. 37, 173-19. Karl, T.R., Jones, P.D., Knight, R.W., Kukla, G., Plummer, N., Razuvayev, V., Gallo, K.P., Lindseay, J., Charlson, R.J., Peterson, T.C., 1993. Asymmetric trends of daily maximum and minimum temperature. Bull. Am. Meteorol. Soc. 74, 1007–1023. Kendall, M.G., 1975. Rank Correlation Measures. Charles Griffin, London. Koster, R.D., Suarez, M.J., 1995. Relative contributions of land and ocean processes to precipitation variability. J. Geophys. Res. 100 (13,775– 13,790). Lauritsen, R.G., Rogers, J.C., 2012. U.S. diurnal temperature range variability and regional causal mechanisms, 1901–2002. J. Clim. 25 (20), 7216–7231. Mahmood, R., Littell, A., Hubbard, K.G., You, J., 2012. Observed data based assessment of relationships among soil moisture at various depths, precipitation, and temperature. Appl. Geogr. 34, 255–264. Mann, H.B., 1945. Non-parametric tests against trend. Econometrica 13, 245–259. Martinez, J.C., Maleski, J.J., Miller, F.M., 2012. Trends in precipitation and temperature in Florida, USA. J. Hydrol. 452–453, 259–281. Modarres, R., Sarhadi, A., 2009. Rainfall trends analysis of Iran in the last half of the twen- tieth century. J. Geophys. Res. 114, D03101. http://dx.doi.org/10.1029/2008JD010707. Robinson, P., 2005. North Carolina Weather and Climate. University of North Carolina Press in Association with the State Climate Office of North Carolina (Ryan Boyles, graphics). Salas, J.D., Delleur, J.W., Yevjevich, V., Lane, W.L., 1980. Applied Modelling of Hydrologic Time Series. Water Resources Publications, Littleton, Colorado. Sayemuzzaman, M., 2014. Spatio-temporal trends of climate variability in North Carolina. Ph.D dissertation. Energy and Environmental System Department. North Carolina A&T State University. Sayemuzzaman, M., Jha, M.K., 2014a. Modeling of future land cover land use change in North Carolina using Markov chain and cellular automata model. Am. J. Eng. Appl. Sci. 7, 295–306. http://dx.doi.org/10.3844/ajeassp.2014.295.306. Sayemuzzaman, M., Jha, M.K., 2014b. Seasonal and annual precipitation time series trend analysis in North Carolina, United States. Atmos. Res. 137, 183–194. http://dx.doi.org/ 10.1016/j.atmosres.2013.10.012. Sayemuzzaman, M., Jha, M.K., Mekonnen, A., Schimmel, K., 2014a. Subseasonal climate variability for North Carolina, United States. Atmos. Res. 145, 69–79. http://dx.doi. org/10.1016/j.atmosres.2014.03.032. Sayemuzzaman, M., Jha, M.K., Mekonnen, A., 2014b. Spatio-temporal long-term (1950–2009) temperature trend analysis in North Carolina, United States. Theor. Appl. Climatol. 116 (3), 1–13. http://dx.doi.org/10.1007/s00704-014-1147-6. Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall's tau. J. Am. Stat. Assoc. 63 (324), 1379–1389. Sonali, P., Nagesh, K.D., 2013. Review of trend detection methods and their application to detect temperature changes in India. J. Hydrol. 476, 212–227. Tabari, H., Shifteh, S.B., Rezaeian, Z.M., 2011. Testing for long-term trends in climatic variables in Iran. Atmos. Res. 100 (1), 132–140. Theil, H., 1950. A rank-invariant method of linear and polynomial regression analysis. Proc. K. Ned. Akad. Wet. A53, 386–392. Trenberth, K.E., Shea, D.J., 2005. Relationships between precipitation and surface temperature. Geophys. Res. Lett. 32, L14703. http://dx.doi.org/10.1029/2005GL022760. Trenberth, K.E., et al., 2007. Observations: surface and atmospheric climate change. Climate change 2007: the physical science basis. In: Solomon, S., co-authors (Eds.), Contribution of Working Group I to the Fourth Assessment Report of the Intergovern- mental Panel on Climate Change. Cambridge University Press, UK, pp. 235–336. Uppala, S.M., et al., 2005. The ERA-40 re-analysis. Q. J. R. Meteorolog. Soc. 131, 2961–3012. http://dx.doi.org/10.1256/qj.04.176. USDA-ARS, 2013. Agricultural Research Service, United States Department of Agriculture. http://www.ars.usda.gov/Research/docs.htm?docid=19440 (accessed November, 2013). von Storch, H., 1995. Misuses of statistical analysis in climate research. In: Storch, H.V., Navarra, A. (Eds.), Analysis of Climate Variability: Applications of Statistical Tech- niques. Springer, Berlin, pp. 11–26. Wu, W., Dickinson, R.E., 2004. Time scales of layered soil moisture memory in the context of land–atmosphere interaction. J. Clim. 17, 2752–2764. Yue, S., Wang, C.Y., 2002. Regional streamflow trend detection with consideration of both temporal and spatial correlation. Int. J. Climatol. 22, 933–946. Zhang, J., Wang, W.-C., Wu, L., 2009. Land–atmosphere coupling and diurnal temperature range over the contiguous United States. Geophys. Res. Lett. 36, L06706. http://dx.doi. org/10.1029/ 2009GL037505. Zhou, L., Dai, A., Dai, Y., Vose, R.S., Zou, C.-Z., Tian, Y., Chen, H., 2009. Spatial patterns of diurnal temperature range trends on precipitation from 1950 to 2004. Clim. Dyn. http://dx.doi.org/10.1007/s00382-008-0387-5. 108 M. Sayemuzzaman et al. / Atmospheric Research 160 (2015) 99–108

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

You are reading a preview.

Check these out next

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

More Related Content

Similar to diurnal temperature range trend over North Carolina and the associated mechanisms.pdf (20)

Recently uploaded (20)