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In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956–2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1, …, 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.
1. Introduction
Climate change and global warming, especially changes in climate extremes, are of concern because of their severe societal and ecological impacts (Kharin et al. 2007; Hansen et al. 2012; Fyfe et al. 2013; Zhang and Zwiers 2013; Ji et al. 2014; Robeson et al. 2014; Lin and Franzke 2015; Franzke 2015, 2017). Climate extremes have already been extensively studied at global, regional, and local scales. Specifically, changes in temperature extremes have been identified in many parts of the world (Brown et al. 2008; You et al. 2011; Mika 2013; Monier and Gao 2015). At regional and local scales, unlike the warming trend of global mean temperature, a wide variety of changes in temperature extremes is possible (Alexander et al. 2006; Brown et al. 2008). Local changes in temperature extremes not only influence individuals' perceptions but also affect practical planning and ultimately drive mitigation policy (Chapman et al. 2013). It is therefore important to detect the changes in temperature extremes at regional and local scales and identify their connections to global climate change (Capparelli et al. 2013). In this study, we focus on the spatiotemporal variations of temperature extremes in China.
Trend identification and significance tests in climate time series are important issues in climate research (e.g., IPCC 2007; Franzke 2012). The traditional methodology for trend identification and significance test is by using linear regression or the nonparametric Mann–Kendall (M-K) test (Mann 1945; Kendall 1975). Long-range dependence (LRD; also called long memory or long-term persistence) refers to a serial correlation between distant observations of a time series that decays according to a power law. LRD may induce trendlike behaviors; therefore, the statistical uncertainty of the trend identification will significantly increase (Yue et al. 2002; Fatichi et al. 2009; Lennartz and Bunde 2009; Franzke 2010, 2012, 2013; Bunde et al. 2014; Ludescher et al. 2016). In previous studies, LRD has already been detected in multidecadal daily or monthly temperature time series (Caballero et al. 2002; Blender and Fraedrich 2003; Király et al. 2006; Vyushin and Kushner 2009; Franzke 2010, 2012) and even in millennial yearly temperature time series (Østvand et al. 2014, Nilsen et al. 2016). The first objective of this study is to examine the temperature series at different time scales for evidence of LRD.
Quantile regression, initiated by Koenker and Bassett (1978), offers an alternative trend-detection approach for identifying changes over time of any percentile values of climate variables (Barbosa 2008; Donner et al. 2012). Koenker and Schorfheide (1994) used quantile regression to reanalyze the global surface air temperature. Barbosa et al. (2011) examined the changes in daily mean air temperature over central Europe using quantile regression and found considerable spatial diversity. Shiau and Huang (2015) and Tan and Shao (2017) also proposed to detect the distributional changes of precipitation indices by estimating the regression coefficients of linear quantile regression at dense quantiles. For serially correlated temperature time series, Franzke (2013) proposed a new method to test the statistical significance of trends of extreme values in daily temperature time series with LRD based on quantile regression and surrogate data. The 5th and 95th percentiles of daily temperature were defined as the cold and warm temperature extremes, respectively. The second objective of this study is to test the statistical significance of trends of temperature extremes using quantile regression as in Franzke (2013), if there is evidence of LRD in temperature time series.
A few methods have been applied to investigate the spatial variation quantile trends at multiple percentiles. Reich (2012) developed a spatiotemporal quantile regression model by assuming that each quantile level changes linearly in time. Unlike Reich's model-dependent method, Chapman et al. (2013) and Stainforth et al. (2013) derived a local trend parameter of temperature change; thus, the relative changes of temperature between different quantiles and between different geographical locations for the same quantiles could be evaluated. An alternative approach to detect spatial variation patterns of climate extremes is by combining quantile regression and cluster analysis (Barbosa et al. 2011; Shiau and Lin 2016). In previous studies, different distance metrics have been proposed to better measure the similarity of climate change patterns (Scotto et al. 2010, 2011). The spatial clustering was implemented using an agglomerative hierarchical method (Scotto et al. 2010, 2011; Barbosa et al. 2011; Shiau and Lin 2016), where spatial proximity between different sites was not included. When spatial variations of quantile trends of temperature are studied, it is more reasonable to apply spatial clustering algorithms that consider both geometrical properties and attributes simultaneously rather than agglomerative hierarchical methods. Our third objective is to investigate the spatial pattern of temperature changes in China by combining quantile regression and spatial clustering to consider both geometrical properties and attributes simultaneously.
Quantile regression can also be used for detecting the relation between climate extremes and large-scale climate patterns (Tareghian and Rasmussen 2013; Fan and Xiong 2015; Tan and Shao 2017). In China El Niño–Southern Oscillation (ENSO) is considered as an important factor for summer climate anomalies (Wang et al. 2008; Wu et al. 2010). Additionally, it has been found that winter temperature extremes in eastern China are also affected by ENSO (Chen et al. 2012). The Arctic Oscillation (AO) is one of the most dominant patterns of Northern Hemispheric climate variability (e.g., Feldstein and Franzke 2017), and it is most prevalent in winter and in the middle and high latitudes (Ramos et al. 2010; You et al. 2013). Recent studies have illustrated a strong positive correlation between winter temperature extremes and the AO in northern China (Gong and Wang 2003; You et al. 2013). The above teleconnections between ENSO/AO and temperature extremes in China are usually investigated by correlation analysis (Gong and Wang 2003; Wu et al. 2010; You et al. 2013); however, to our best knowledge, quantile regression has not been applied. Using quantile regression to explore the statistical connections of temperature extremes in China with ENSO/AO is the fourth objective of this study.
The paper is organized as follows. In section 2, we present the daily temperature data, the deseasonalized daily temperature, and the aggregated monthly anomaly temperature time series. The monthly time series of climate indices representing ENSO and AO are also described. Section 3 shows all the methods used in this study. Results and discussion are respectively presented in sections 4 and 5, and finally conclusions are given in section 6.
2. Data
The time series of daily mean, maximum, and minimum temperatures are provided by the Climate Data Center (CDC) of the China Meteorological Administration (CMA). (The datasets are available at http://data.cma.cn/en/?r=data/weatherBk.) The RHTest software, developed at the Climate Research Branch of Meteorological Service of Canada (available at http://etccdi.pacificclimate.org/software.shtml), is applied to assess data homogeneity. After homogeneity assessment, there are a total of 352 meteorological stations having good quality and continuous daily records of surface air temperature in the period 1956–2013. These 352 meteorological stations are not evenly distributed and most of them are located in central and eastern China (Fig. 1).
As in Franzke (2013), quantile regression could be directly used for trend detection in the original daily mean temperature time series. However, the daily mean temperature time series are obviously affected by seasonality, resulting in quasi-periodic variations of statistics such as the sample mean and variance (Fatichi et al. 2009). Here we use the classical approach by subtracting the mean annual cycle resulting in the deseasonalized daily temperature time series (Fatichi et al. 2009; Franzke 2010, 2012). By averaging the deseasonalized daily temperature time series, we can obtain the monthly anomaly temperature time series (Franzke 2010, 2012). The two Expert Team on Climate Change Detection and Indices (ETCCDI) variables, monthly minimum value of daily minimum temperature (TNn) and monthly maximum value of daily maximum temperature (TXx), are also computed using the dataset of daily maximum and minimum temperature, respectively. Specifically, annual temperature series of TNn in January and TXx in July are developed, because in China's mainland warm extremes usually occur in July and cold extremes occur in January. Trends in annual extreme temperature series, TXx (July) and TNn (January), are also analyzed.
Moreover, TXx and TNn are also used as the response variable in quantile regression to detect the relationships between temperature extremes and large-scale climate patterns. The Niño-3.4 (5°S–5°N, 170°–120°W) SST is used as an index for ENSO (Wu et al. 2010; Chen et al. 2013), and the monthly Niño-3.4 index can be accessed from https://www.esrl.noaa.gov/psd/data. The monthly time series of the AO index, which is extracted from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC; http://www.cpc.ncep.noaa.gov/products), is used to represent the AO (You et al. 2013). Before applying quantile regression, monthly time series of Niño-3.4 and AO indices are standardized by subtracting the mean and dividing by the standard deviation over the entire study period.
To understand the changing patterns of the large-scale atmospheric circulation, the reanalysis data of monthly mean geopotential height and wind fields at 850 hPa are downloaded from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) and analyzed. The NCEP–NCAR Reanalysis-1 project data assimilation system is using past data from 1948 to the present. The spatial resolution of the global dataset is 2.5° × 2.5° (Kalnay et al. 1996).
3. Methods
a. Detection of long-range dependence and serial correlation
Theoretically, an LRD process can be expressed either in the time or in the frequency domain. In the time domain, LRD is characterized by a hyperbolically decaying autocorrelation function (Hosking 1981), while in the frequency domain LRD manifests itself as an unbounded spectral density at zero frequency (Hurst 1951; Franzke et al. 2012). To describe the serial correlation in a time series, a number of models have been proposed. The fractional integrated autoregressive and moving average model, ARFIMA(p, d, q), is one of the most flexible models (Granger and Joyeux 1980; Hosking 1981). Here d is denoted as the long-range parameter or the fractional differencing parameter, which is a measure of the strength of the temporal dependence of a time series.
Many methods are available to detect the existence of LRD and to estimate the long-range parameter d including graphical methods, parametric methods, and semiparametric methods (Beran 1994). In this study, the Geweke and Porter-Hudak (GPH) semiparametric estimator (Geweke and Porter-Hudak 1983; Franzke 2010, 2012) is used to estimate the long-range parameter d as well as the corresponding 95% confidence interval. If the 95% confidence interval does not contain 0, d is considered to be significantly different from 0 at the 5% significance level. The estimation of the long range parameter d is implemented using the R package "fracdiff." Evidence of LRD and trends will be detected for daily and monthly temperature series.
LRD is not detected for annual temperature series, TNn in January and TXx in July, because the lengths of the series are too short. Alternatively, lag-1 and lag-2 autocorrelation coefficients are computed to measure the serial correlations (Rao and Bhattacharya 1999). If the estimated lag-1 or lag-2 autocorrelation coefficient falls inside the 95% confidence interval, serial correlation at lag 1 or lag 2 is considered to be not significant at the 5% significance level. Autocorrelation coefficients are also computed using R.
b. Quantile regression
Quantile regression is proposed as an extension to the traditional linear regression, which approximates quantiles of the response variable (Koenker and Bassett 1978). The widely used linear quantile regression minimizes the functional
where ρτ(μ) = τμI(0,∞)(μ) − (1 − τ)μI(−∞,0)(μ) is the check function; IA(μ) is the indictor function that equals to 1, if μ ∈ A; n is the length of the observed time series y; Xi = (Xi1, …, XI)T (m is the number of covariates minus one), where Xi1 = 1 for the intercept and Xi is a vector of the time points but can also contain external factors such as climate indices; β denotes the parameter vector to be estimated; and τ is the quantile level [see Yu et al.(2003) and Koenker (2005) for more details].
In this study, quantile regression is used for different percentiles; therefore, the estimated quantile curves might cross, leading to an invalid distribution for the response (Wu and Liu 2009; Bondell et al. 2010). To solve the crossing problem in quantile regression, a few methods have been proposed. We adopt the method of Bondell et al. (2010), which is also applicable to multivariate quantile regression. The estimated coefficients are considered to be significantly different from 0 at the 5% significance level, only 0 is outside of the 95% confidence interval. In this study, all quantile regressions are carried out using R.
c. Trend identification and significance test
Following Franzke (2013), linear quantile regression trends for the 5th (cold extremes), 50th (median), and 95th percentiles (warm extremes) of daily mean temperatures are estimated, respectively. The change rate is measured in degrees Celsius per decade. Analogously, the statistical significance of the quantile trends is also tested using the surrogate data generating method proposed by Schreiber and Schmitz (1996). The readers are referred to Schreiber and Schmitz (1996) and Franzke (2013) for further details. For each daily temperature time series, we generate 1000 surrogate time series with the same autocorrelation function and the same probability density function. Linear quantile regression trends for the 0.05, 0.5, and 0.95 quantiles of each surrogate time series have also been estimated, respectively. If the linear quantile regression trend of the observed daily temperature time series is outside the 2.5% or 97.5% percentiles of the quantile trends computed from the ensemble of surrogate data, we claim that the observed trend is statistically significant (Franzke 2013).
For comparison purposes, we also apply two traditional methods, robust linear regression and nonparametric M-K test, to identify the temperature trends. The robust regression is used to detect the linear trends of deseasonalized daily temperature time series, monthly anomaly temperature time series, and annual temperature time series of January TNn and July TXx, respectively. Robust regression is a form of weighted least squares regression and is done iteratively (Holland and Welsch 1977). In the first iteration, equal weights are assigned to all points and the ordinary least squares method is used to estimate the model coefficients. At subsequent iterations, weights are recomputed so that points farther from model predictions in the previous iteration are given lower weight. Model coefficients are then recomputed using weighted least squares. The process continues until the values of the coefficient estimates converge within a specified tolerance. Here, we use the bisquare weighting (Holland and Welsch 1977) and the weighting function w is given by
where r is given by
and R represents the residuals from the previous iteration, h represents the leverage values from a least squares fit, s is an estimate of the standard deviation of the error term, and T = 4.685 is the default tuning constant. The robust regression (RR) is computed using the Matlab Statistics Toolbox.
When the M-K test is used for trend identification, the presence of serial correlation in time series will affect the ability of the test to correctly assess the significance of trend (von Storch and Navarra 1995; Douglas et al. 2000). Because M-K test is not designed for trend analysis of extremes, it is not used to detect the trends of daily temperature series and the annual temperature series of January TNn and July TXx in this study. Instead, a modified M-K test is only used to detect the trend of monthly anomaly temperature time series and test its statistical significance (Yue and Wang 2004). This method uses a nonparametric method to account for the presence of serial correlations. Additionally, Sen's slope is estimated to quantify the trend (Sen 1968). An identified trend is considered to be statistically significant only if it is significant at the 5% level. The modified M-K test is completed using the R package "fume."
d. Spatial cluster analysis
In this study, spatial cluster analysis of quantile trends of temperatures is realized using a newly proposed density-based spatial clustering (DBSC) algorithm (Liu et al. 2012), which considers spatial proximity and attributes similarity simultaneously. For each daily temperature time series, linear quantile regression trends are estimated first at 19 quantile levels (0.05, 0.1, …, 0.95). A vector consisting of 19 quantile trend values is considered as the attribute of the station, and the attribute similarity is measured as the Euclidean distance (Shiau and Lin 2016). The spatial proximity between stations is measured by the geometrical distances. Here, we introduce this algorithm briefly; the reader is referred to the original references for further details (Liu et al. 2012). Delaunay triangulation with edge length constraints is first employed for modeling the spatial proximity relationships among stations. A modified density-based clustering strategy is then designed and used to identify spatial clusters. By combining quantile regression and spatial cluster analysis, both temporal and spatial changes in temperatures in China can be summarized. In this study, the computer code to implement DBSC is written in Matlab.
4. Results
a. Evidence of long-range dependence
First the deseasonalized daily temperature time series and monthly anomaly temperature time series at all 352 stations are examined for evidence of LRD by using the GPH method. The test results are shown in Figs. 2a,b. At the daily time scale, all d values are positive ranging from 0.075 to 0.47 with a median value of 0.29 (Table 1). At 350 stations, the d values are significantly different from zero confirming the universality of LRD in temperature time series (Fig. 2a). At the remaining two stations, the d values are not significantly different from zero. Since all d values are smaller than 0.5, all these daily temperature time series are considered to be persistent. Figure 2b shows that the intensity of the dependence decreases: LRD at the monthly time scale is lower than that at the daily time scale. The range of d values is from −0.035 to 0.397, and the median value is 0.226. Figures 2a and 2b also show that stations with stronger serial correlations are mainly located in northern and southwestern China as well as the southeastern coastal area. Thus, LRD in daily and monthly temperature series should be considered in the following trend analysis and significance test.
The lag-1 and lag-2 autocorrelation coefficients of all annual time series of January TNn and July TXx are presented in Figs. 2c–f and Table 2. For annual time series of January TNn, significant serial correlations at lag 1 and lag 2 are detected at 91 and 92 stations, respectively. For annual time series of July TXx, there are only 48 stations showing significant autocorrelation at lag 1 and lag 2. From Fig. 2, we conclude that the serial correlation decreases as the time scale increases at most stations in China.
b. Trend analysis and statistical significance
The trends in monthly anomaly temperature time series at all 352 stations are first identified using two traditional methods, robust linear regression and the nonparametric M-K test. Figures 3a and 3b show the spatial distribution patterns of the temperature trends at the 352 stations, and Table 1 provides the descriptive statistics of all trends. The results of the trend identification using the robust linear regression and nonparametric M-K test are very similar (Figs. 3a,b and Table 1), suggesting that they are robust. Positive trends are detected at most stations, whereas negative trends are detected at only a few stations. At the monthly time scale, both robust linear regression and nonparametric M-K test have detected significant warming trends over China.
Next, robust linear regression and Franzke's (2013) method are used to test the trend of the original daily temperature series (nondeseasonalized). Figure 3c and Table 1 show the results of the trend analysis using robust linear regression. The detected trends of the original daily mean temperature series are quite similar to that of monthly anomaly temperature time series (deseasonalized and aggregated). Following Franzke's (2013) method, trends are also detected using the noncrossing quantile regression technique at the three quantile levels 0.05, 0.5, and 0.95, corresponding respectively to lowest 5th, 50th, and 95th percentiles of daily temperature. The detected trends and their statistical significance are also presented in Figs. 3d–f and summarized in Table 1. For the lowest percentile, there are 135 and 214 stations showing significant and nonsignificant positive trends, respectively. Only three stations show a nonsignificant negative trend. The rate of change of cold extremes ranges from −0.156° to 1.5°C decade−1, and the median value is 0.288°C decade−1 (Table 1). Large trends (more than 0.4°C decade−1) are mainly located in northern and eastern China (Fig. 3d). For the median daily temperatures, there are 117 and 230 stations showing significant and nonsignificant positive trends, but only 5 stations showing nonsignificant negative trends. The rates of change range from −0.125° to 0.882°C decade−1 with a median value of 0.285°C decade−1 (Table 1). Stations with significant positive trends are mainly distributed in central and eastern China (Fig. 3e). For the 95th percentile, the numbers of stations with significant positive, nonsignificant positive, significant negative, and nonsignificant negative trends are 128, 196, 5, and 23 (Fig. 3f), respectively. The minimum, median, and maximum change rates are −0.34°, 0.154°, and 0.817°C decade−1 (Table 1), respectively. Significant positive trends are mainly located in northern and southern China, while negative trends are distributed in central China (Fig. 3f).
Finally, the robust linear regression and the noncrossing quantile regression methods are applied for trend analysis of the annual temperature series of January TNn and July TXx. Because the length of the annual temperature series is short (58 yr), quantile levels 0.05 and 0.95 are replaced by 0.1 and 0.9 to reduce the uncertainty of quantile regression (the estimation of quantile coefficients can be reduced to a linear programming problem, which might return null estimation if the length of the series is short and the quantile is too low or high). Figure 4 presents the quantile evolutions of the January TNn and July TXx at quantile levels τ = 0.1, 0.5, and 0.9 during 1956–2013 and their comparisons to the results derived from the robust linear regressions. The estimated change rates are also summarized in Table 2. For the January TNn temperature series, we find that the numbers of stations with significant positive trends identified using robust linear regression and quantile regression at multiple quantile levels are similar (Figs. 4a–d). However, a few of the stations located in northern China with significant negative trends are identified by using quantile regression at the quantile level of 0.1 (Fig. 4b). For July the TXx temperature series trends, detected using robust regression and quantile regression at median levels, are similar (Figs. 4e,g). Compared with Fig. 3f, significant negative trends of warm extremes are detected at more stations, which are mainly located in central and northeastern China (Fig. 4h).
c. Spatial pattern of quantile trends
To reveal the spatial pattern of quantile trends of daily temperature, the DBSC algorithm is applied to implement spatial clustering of vectors consisting of quantile trends at the following quantile levels: 0.05, 0.1, …, 0.95. As shown in Fig. 5, there are six main clusters discovered by the DBSC algorithm. The largest cluster is cluster C1, which contains 176 stations and is located over central and southern China. The second largest cluster is C2 with 121 stations, which are distributed over northern China. The other main clusters are C3 and C4. Cluster C3 is located over the Yangzi River delta and the eastern coast, while cluster C4 is located over the northern coast of China. C5 and C6 are two small clusters located in the Qinghai–Tibetan Plateau and Xingjiang, respectively. The average of quantile trends and the associated confidence intervals (mean value plus and minus standard deviations) of each cluster are shown in Fig. 6. We see that there is an obvious difference between adjacent clusters (e.g., C1 and C3 or C2 and C4). For clusters C2, C3, and C5, the quantile trends at lower quantiles (τ < 0.3) are much higher than that at higher quantiles (τ > 0.6). This result is consistent with the rapid warming trends of cold extremes in northern and the Qinghai–Tibetan Plateau presented in Fig. 3d.
d. Relationship of temperature extremes with ENSO and the AO
The potential teleconnections between temperature extremes in China and ENSO (the AO) are investigated by computing the correlations between monthly anomaly temperatures and the Niño-3.4 index (AO index). Figure 7 shows the Kendall's τ between the monthly mean temperature anomalies and standardized climate indices for 1956–2013 in China with a 0–5-month lag. Correlation analysis shows that the number of stations at which the Niño-3.4 index is significantly correlated to monthly anomaly temperature is highest when the time lag is 5 months. The AO index is highly correlated with monthly anomaly temperature at the 0-month lag at 161 stations, with coefficients as high as 0.27. The two time lags, 5-month and 0-month, are further used in the following multivariate noncrossing quantile regression. In each quantile regression model, January TNn or July TXx is used as response variable and the two normalized climate indices, the Niño-3.4 index (5 months ahead of January or July) and the AO index, are taken as predictors.
Figure 8 shows the spatial distribution of quantile slopes estimated using noncrossing quantile regression (QR) for January TNn at quantile levels τ = 0.1, 0.5, and 0.9 as well as the slopes estimated using robust regression. The results of statistical significance are also summarized in Table 3. Using robust regression, significant influence of ENSO on January TNn has only been identified at 26 stations (Fig. 8a). When the quantile regression method is applied, the number of stations with significant quantile slopes increases notably (Figs. 8b–d). From Figs. 8a–d, we find that ENSO mainly affects cold extremes at the northeastern and southeastern coastal zones in China. The relationships between January TNn and the AO revealed by robust regression and quantile regression are consistent (Figs. 8e–h). These findings indicate that cold extremes in northern and eastern China are significantly affected by the AO. Similarly, quantile regression detects more significant positive quantile slopes than robust regression.
Figure 9 presents the spatial distribution of quantile slopes for July TXx versus the Niño-3.4 index and AO index, and the descriptive statistics are shown in Table 4. Using robust regression, a significant influence of ENSO on July TXx has been identified at 11 stations (Fig. 9a). From Figs. 9b–d, we find that the quantile slopes at most stations are negative, and the positive trends are mainly distributed at eastern China. Specifically, the July TXx at the 0.9 quantile is significantly affected by the ENSO at 35 stations positively and 69 stations negatively. Similarly, the statistical connections between the AO and July TXx have also been verified at a few stations (Figs. 9f–h) using quantile regression. Taken the quantile level of 0.9 as an illustration, stations with significant negative quantile slopes are located in southern and northeastern China, while those with significant positive stations are mainly distributed in central and northwestern China (Fig. 9h). However, robust regression only detects two significant positive slopes and nine significant negative slopes (Fig. 9a). We note that the teleconnections between temperature extremes can be more effectively detected using quantile regression than robust regression.
5. Discussion
Traditionally, the analysis of changes of climate extremes is based on either climate extreme indices (Alexander et al. 2006; You et al. 2011) or extreme value theory (Kharin et al. 2007, 2013; Brown et al. 2008). The ETCCDI have defined as many as 27 indices for extreme weather and climate events including 16 indices for temperature extremes and 11 indices for precipitation extremes (Alexander et al. 2006). Then, monthly or yearly time series of these indices can be extracted from daily observations of temperature and precipitation, and a trend analysis follows (Alexander et al. 2006). When LRD is present in climate time series, the statistical uncertainty of the trend identification will significantly increase (Fatichi et al. 2009; Franzke 2010, 2012; Ludescher et al. 2016).
Detecting the existence of LRD in temperature time series is the preliminary procedure in the time series analysis of temperature extremes. For temperature time series, a number of studies have been devoted to investigate the possibility of LRD at either global or regional scales (Caballero et al. 2002; Blender and Fraedrich 2003; Franzke 2010, 2012; Mann 2011). The detection of the presence of LRD in temperature time series relies on the method that is employed. Vyushin and Kushner (2009) found that different LRD detection methods could not generate robust estimation when they were applied to empirical time series due to the problems of frequency range choices and filtering properties of the methods. In addition, the detection is also affected by the time scale of the time series (the length or interval of time series). In this study, the semiparametric GPH method (Geweke and Porter-Hudak 1983) is applied to examine the evidence for LRD in multidecadal daily and monthly temperature series, which have been deseasonalized by removing the annual cycle. In previous studies, the GPH method has already been successfully applied in detecting LRD in temperature time series (Franzke 2010, 2012). Analogously, we also find evidence of LRD at daily and monthly time scales; therefore, the statistical significance of trends in temperature extremes should be tested using a proper method. However, for short time series with few samples, there is uncertainty in LRD detection (Rao and Bhattacharya 1999). As LRD can be characterized by a slowly decaying autocorrelation, lag-1 and lag-2 autocorrelation are computed as a measure of serial correlation of the annual extreme temperature series, TNn in January and TXx in July. Our findings verify that serial correlations are not significant for most annual extreme temperature series. The detection of LRD also shows that the intensity of serial correlation decreases as the time scale is increased (daily, monthly, and annual).
In the present paper, quantile regression has been extensively applied to conduct a spatiotemporal analysis of extreme temperature changes at 352 meteorological stations over China. Primarily, quantile regression has been applied for trend identification in daily temperature time series with significant LRD, then, the surrogate data generating method proposed by Franzke (2013) is used to test the statistical significance of quantile trends. We find that linear, quadratic, and cubic quantile regression trends are similar at most stations (not shown). For simplicity, only linear quantile regression trends are estimated in the observed temperature time series and surrogate time series. Moreover, the noncrossing quantile regression method has also been used for detecting the trends of annual extreme temperature series, TNn in January and TXx in July. As we know, China has already experienced significant temperature changes during the past few decades (Ding et al. 2007). It has been estimated that the daily maximum and minimum air temperatures increased at rates of 0.13° and 0.32°C decade−1 from 1955 to 2000, respectively (Wang and Gong 2000). For mean maximum temperatures, the increasing trends have occurred in northern China, while weak decreasing or insignificant trends have occurred in southern China (Zhai and Pan 2003; You et al. 2011). A general conclusion states that winter temperature extremes is warming faster than annual mean warm extremes (Wang and Gong 2000; Zhai and Pan 2003; You et al. 2011). Results of trend identification and significance tests in this study are consistent with the above general conclusion. The robust linear regression and M-K test also show significant warming trends over China. When Franzke's (2013) method has been used to test the statistical significance of quantile trends, the numbers of stations with significant positive trends at quantiles 0.05, 0.5, and 0.95 decrease to 125, 117, and 128, respectively. Compared with the traditional robust regression and Mann–Kendall methods for trend identification, the warming extent tested using the quantile regression and surrogate data method is lower over China suggests that the shape of the temperature distribution is changing because of global warming; that is, global warming is not just a simple shift to warmer temperatures. Moreover, significant negative trends in warm extremes are also detected in central China, which is consistent with the decreasing trends shown in some previous studies (Zhai and Pan 2003; Zhang et al. 2011; Zhou and Ren 2011; You et al. 2011). In conclusion, quantile trends have provided a more complete description of temperature change in China, where temperature variability not only in the mean but also in extreme values has been investigated.
To investigate the role of large-scale atmospheric circulation change in climate change, atmospheric circulation composite analysis is usually conducted (You et al. 2011), where composite circulation maps showing the differences of wind fields and geopotential height between two periods are consequently created to represent the change in circulation. Figure 10 shows the mean difference of wind fields and geopotential height at 850 hPa in January and July between 1981–2013 and 1956–80. In January, two enhanced anticyclonic circulations developed in northern China and the Qinghai–Tibetan Plateau. This would partly explain why the cold temperature extremes have consistently decreased in northern China, which was consistent with rapid warming in these two regions in January (Figs. 3d and 4a–d). As shown in Fig. 10b, the largest differences of geopotential height (approximately 50 gpm) occurs near 45°N, 100°E in July (Fig. 8a), and enhanced anticyclonic circulation develops over the Eurasian continent, centered on Mongolia. The increased geopotential height over Mongolia is consistent with the rapid warming in northern China in July (Figs. 3f and 4h). The northeasterly wind in northern and eastern China has strengthened, and in turn weakens the northern and eastern extent of the westerly jet stream and any southwesterly flow from the ocean. This may partly explain the decreasing trend of warm extremes in July in northeastern and central China. The association between large-scale circulation patterns and temperature extremes is an important research topic in climatology. Loikith and Broccoli (2012) proposed to identify this association based on composite analysis. Identifying the association between regional atmospheric circulation patterns and temperature extremes in China is not the main objective of this study but deserves to be studied in an independent paper.
The spatiotemporal temperature variations in China have been summarized by combining quantile regression and spatial cluster analysis. Unlike Barbosa et al. (2011), both spatial proximity and quantile trends similarity have been considered in spatial clustering by using the DBSC algorithm. Spatial clustering of quantile trends of temperatures is helpful to reveal the physical mechanism of local temperature change. Two large clusters have been identified showing significant differences of quantile trends in southern and northern China. Hu et al. (2010) and Yang et al. (2011) both found that the warming trend at Yangzi River delta was attributable to urbanization. In this study, a cluster C3 located at Yangzi River delta and the eastern coast has also been identified with larger quantile slopes than that at the nearby cluster C1 showing the contribution of urbanization to local warming.
Finally, the influences of two important large-scale climate patterns, ENSO and the AO, on temperature extremes are investigated through quantile regression. Chen et al. (2013) showed that cold extremes in the southern part of eastern China were correlated to ENSO. In this study, both robust regression and quantile regression verify the above findings (Figs. 8a–d). Moreover, we also find strong correlations between ENSO and cold extremes in northeastern China. Compared to the robust regression, quantile regression identifies more significant correlations between cold extremes and Niño-3.4 index. The Arctic Oscillation, also known as the northern annular mode, strongly influences surface air temperatures over the Eurasian continent (Thompson and Wallace 1998). Specifically, the AO has significant influence on both temperature and precipitation in northern China at the interdecadal time scale (Gong and Wang 2003). Chen et al. (2013) demonstrated that interannual variation of winter temperature extremes in the northern part of eastern China was closely linked to the AO. Analogously, You et al. (2013) found that the AO could explain more than 50% of the winter temperature extreme change in China. In this study, the significant correlations between AO and cold extremes in northern and eastern China are also identified by robust regression and quantile regression (Figs. 8e–h). In addition, impacts of ENSO on the summer temperature in northeastern China have been recognized in previous studies (Wu et al. 2010). This teleconnection is also identified by quantile regression in northeastern China; however, the impact of ENSO on warm extremes is not significant at most stations in China (Figs. 9a–e). Alternatively, ENSO plays a more important role in determining the spatial distribution of summer precipitation in China (Lin and Lu 2009). Additionally, we also find evidence of the AO's significant impact on warm extremes in northeastern and southern China (Fig. 9h). Zhu and Wang (2016) found that there was interannual variation in the correlation between the AO and ENSO in boreal winter. Our research indicates that further investigation of the mechanical linkage between the AO and ENSO and their joint influence on climate extremes in China is worthwhile.
6. Conclusions
In this study, we have conducted a spatiotemporal analysis of temperature extreme changes based on quantile regression at 352 meteorological stations over China in the period 1956–2013. Before the trend identification, temperature time series at daily and monthly time scales have been examined for evidence of long-range dependence. We find evidence for long-range dependence for most temperature time series. Then, the statistical significance of linear quantile trends of temperature extremes is tested against quantile trends of 1000 surrogate time series. We find evidence for a significant positive trend in cold extremes for 135 stations, which are mainly located in northern and eastern China. For warm extremes, significant positive and negative trends have been detected for 128 and 8 stations, respectively. This spatial variability of quantile trends represents different patterns of temperature extreme change in China. More specifically, the trends of two ETCCDI variables, TNn in January and TXx in July, are also analyzed using quantile regression that has identified changes of temperatures at more quantiles. Changes of large-scale atmospheric circulation partly explain the trends of temperature extremes in China.
Spatial cluster analysis has identified a few large clusters indicating different warming patterns in different parts of China. These clusters are located in southern China, northern China, and the Yangzi River delta. Correlation analysis reveals that the time lag of influence of AO on monthly temperature anomaly is 0 months, whereas the time lag of influence of ENSO on monthly temperature anomaly is 5 months. Finally, quantile regression also reveals the teleconnection between temperature extremes and two large-scale climate patterns, ENSO and the AO. The influence of ENSO on cold extremes is significant at most stations over China, but its influence on warm extremes is not so significant. The AO affects not only cold extremes in northern and eastern China but also warm extremes in northeastern and southern China.
We sincerely acknowledge the three anonymous reviewers whose kind and valuable comments greatly improved the quality of this manuscript. MG was partly supported by the Youth Innovation Promotion Association of CAS (2016195), CAS Knowledge Innovation Project (KZCX2-EW-QN209), and National Natural Science Foundation of China (31570423). CF was supported by the German Research Foundation through the collaborative research center TRR181 "Energy transfers in atmosphere and ocean" at the University of Hamburg. The authors also appreciated Dr. Reich for sharing the R function "NoCrossQuant.R" to implement the non-crossing quantile regression.
© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).


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Traditional plough day to return as funds are raised for the MAGPAS Air Ambulance and Cystic Fibrosis
PUBLISHED: 13:18 15 August 2018 | UPDATED: 13:18 15 August 2018
A popular agricultural fun day will return for the 28th year next month.
Brampton Plough Day is expected to attract hundreds of people once more for the annual event at Manor Farm in Brampton near Beccles on Sunday, September 2 from 10am.
The charities chosen to benefit this year are the MAGPAS Air Ambulance and Cystic Fibrosis, and the committee is hoping to match the 2017 total which raised £4,000 for Autism Anglia and Parkinsons UK.
In addition to the normal ploughing competition, attractions will include heavy horse demonstrations, steam threshing, classic cars, a dog show organised by the Wangford Veterinary Clinic and the Lowestoft Dog Agility Display Team.
Entertainment will be provided throughout the day by Tosh, the Lowestoft Signing Choir, the Beccles Ukulele Group and Punch and Judy shows.
Admission is £5 with entry free for children under 14.
If you value what this story gives you, please consider supporting the Lowestoft Journal. Click the link in the orange box above for details.

subset: commoncrawl


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The Pernice Bianca Fulmine is a Lightning-attribute Bird-type Box Weapon.
Abilities[edit | edit source]
Usually, it concentrates the Lightning Flames in its beak, but by covering its entire body in Lightning Flames, it can become massively fortified. It is a masterpiece that is inexpensive to copy, thus it is deployed in all units as the main aerial weapon of the Millefiore Famiglia.
- Full-Body Lightning (全身雷, Zenshin Kaminari?)
- Charge (突撃, Totsugeki?)
- Lightning Shield (雷シールド, Kaminari Shīrudo?)
- Beak Special Attack (嘴特攻, Kuchibashi Tokkō?)
- Lightning Wings (雷羽, Rai'u?)
Gallery[edit | edit source]
[edit | edit source]
Community content is available under CC-BY-SA unless otherwise noted.


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The following statement was issued today by the Spokesman for UN Secretary-General António Guterres:
The Secretary-General is deeply shocked at and strongly condemns the shooting today at the Tree of Life Congregation synagogue in Pittsburgh in the United States. He expresses his deepest condolences to the families of the victims.
The shooting in Pittsburgh is a painful reminder of continuing anti-Semitism. Jews across the world continue to be attacked for no other reason than their identity. Anti-Semitism is a menace to democratic values and peace and should have no place in the twenty-first century.
The Secretary-General calls for a united front — bringing together authorities at all levels, civil society, religious and community leaders, and the public at large — to roll back the forces of racism, anti-Semitism, Islamophobia and other forms of hatred, bigotry, discrimination and xenophobia gaining strength in many parts of the world.


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a step up from camping -- go 'glamping' august 14, guests stay in cedar cabins and safari tents grouped in villages with names like 'peace tree' and 'shaded creek' nearby bathroom and
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a hidden fastening system is used for the new deck, which features a brazilian hardwood called garapa; red cedar shingles that were cut into a wave pattern are installed at the top of the side
the conclusion of the rowley, mass., queen anne victorian farmhouse project: host bob vila tours the finished kitchen, dining room, cedar-and-stone deck and the patio, and examines the window


text: Glevum 1 Posted December 8, 2019 Autotrader is getting too big for its boots - It consistently rapes the dealers that pay for it's existence but at the same time are forcing the prices down with their "car guru's" type price indicator. Clean cars are getting harder harder and progressively more expensive and the expenses to run a retail business are huge. Why is autotrader hell bent on meddling with perceived car values to help the customer get a cheaper deal but at the same time always trying to increase the cost to the dealers that pay for it? You have to pay extra to get your car advert to the top (sponsored) and to be optimized for tablets - these should be free if they are trying to look after their customers. They now have a 49% share of Cox Automotive's Dealer Auction as of the beginning of this month and guess what..... we used to pay £60 per year to join the buying group which was great with a fixed price for buyers fee typically £60 + vat........ Now AT is in the frame it's £99 per month and who knows what the buyers fee is going to be now ??? Quote Share this post Link to post Share on other sites


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A new J.D. Power study showed that insurer digital experiences now lead in satisfaction importance. The J.D. Power 2020 U.S. Auto Insurance Study released last week revealed that auto insurance websites have become more important to the customer experience than human agents for the first time. This was the first time in the 21-year life of the study that digital came out on top. The study found that auto insurance websites had a bigger impact on customer satisfaction than human agents. This suggests that if insurers want to score the…Read More
Tag: j.d. power
Coronavirus auto insurance discounts may harm customer loyalty instead of helping
J.D. Power data shows that the refunds and credits are increasing a policyholder's odds of switching. Across the country, insurers are offering their various versions of coronavirus auto insurance discounts. These are meant to help make sure policyholders aren't paying more than what their risk level justifies as many people drive far less than they usually would. A new J.D. Power study indicates that providing this savings is making customers more prone to leaving. The study showed that the coronavirus auto insurance discounts are not improving customer loyalty. In fact,…Read More
1 in 5 Consumers accept Amazon and Google as homeowners insurance companies
A J.D. Power survey showed that shoppers see these tech giants as acceptable home insurers. One in five consumers said in a recent J.D. Power survey that they would be willing to use Amazon or Google as homeowners insurance companies. Among those who participated in the survey, 80 percent were already insured by a large national carrier. Amazon and Google have both dabbled in the insurance industry and are expected to become increasing participants in the marketplace. Earlier in August, media reports showed that Amazon was testing the waters in…Read More
Loyalty may mean consumers are paying more for auto insurance coverage
Consumers could find less expensive coverage if they switch carriers Loyalty may be costing drivers in terms of auto insurance premiums. Many consumers opt to stay with their insurance provider because they are provided with certain benefits, such as credit toward insurance payments or discounts on other services. This loyalty may not be as beneficial to consumers as they believe, however, and they could find less expensive auto insurance coverage by switching to another insurer, and this may be a greater benefit than the loyalty rewards they are receiving from…Read More
Millennials are unsatisfied with interaction with homeowners insurance companies
Insurers are having trouble interacting with Millennials Homeowners insurance companies may be failing to meet consumer expectations when it comes to interaction, according to a new study released by J.D. Power, a global marketing research and information services firm. The study highlights "Gen Y" consumers, typically referred to a Millennials, as having the lowest satisfaction with insurers and their ability to interact with consumers. This demographic typically represents those born in the early 1980s and early 2000s and these people are becoming first-time homeowners and have high expectations of the…Read More
Satisfaction with auto insurance has never been higher in the US
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Insurance news looks good for customer satisfaction this year
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Our roving reporter Phil Cook of Royston Park South Australia reviews Cowra Van Park in NSW.
"Owners are Craig & Michelle Constable. We stayed in this park for 3 days in May 2010 and also left our van on site for a further 7 days to travel to Sydney. Arrangements to do this were concluded by phone before we left home so we appreciated the assistance of the owners.
On arrival, we were ably assisted onto our site by Dick (Relieving Manager). We loved this park. It is in a delightful setting on the banks of the Macquarie River and is well maintained. It is also within walking distance of the CBD. Amenities are clean but may have some difficulty in coping during peak periods. A highly recommended park."


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This article originally appeared on Noisey in Spanish.
In the early 1980s, Thomas "Angelripper" Such was working in a coal mine. The desperation of seeing his life pass in a dark, deep underground pit, as well as the nonconformity he felt toward everything, motivated him to take his bass and start a band in an attempt to make a leap out of his precarious reality. With his rough voice, he said fuck it to everything, and in 1982 formed Sodom along with Frank Testegen. The deep, gut level rage of the rebellious working class man led to the creation of one of the most influential bands in the history of metal.
The rage, velocity, aggression, and rawness of Sodom made them a legend. The trio has fluctuated between black, death, and crossover metal, but they always maintained the thrash essence that defines them. War and its horrors have always been one of band's main themes. Sodom's rhythms make you feel like you're on a battlefield, where the machine guns never stop firing and bombs explode every second. It's music that evokes fire and destruction, but at the same time, critiques the thirst that humans have for destroying everything in their path.
Sodom was in Colombia for the first time 20 years ago, performing a historic concert in Medellín. In his heavily accented German, Angelripper recalls that the band was picked up in a van that was riddled with bullet holes. On November 25, the band will return to Colombia, this time to play at the gates of hell.
Sodom is one of the headlining acts for the third annual Festival of the Devil. The group is coming to the country to promote its most recent album, Decision Day, perfect for setting off the most profane pogos and filling Bogotá with fire.
From his home in Germany, Thomas Angeripper talked to us by phone about metal, punk, and the old school.
Noisey: After more than 30 years doing thrash metal, do you feel like the rage that's wrapped up in this music is the same, or has it changed?
Angeripper: It's distinct. I believe that times and music change. In the 80s, when we started this, it was a kind of revolution. We were opposed to everything: To cool, to teachers, our parents, the establishment. I remember my father telling me, "You shouldn't play music, you should go get a job," and I told him, no, this is what I want. Honestly, listening to metal in the 80s was something special. There were 30 kids in my class and I was the only one who listened to heavy metal. Now, things are very different. Now, you can't shock anyone with metal because the whole world knows what it is. With Sodom, we always try to maintain that spirit. I'm always trying to bring the feeling of the 80s to the stage, not as something commercial, but because of the pleasure of making music.
That idea of music as revolution reminds me a lot of punk.
Yes, in that time, it was the same revolution; it's just that the music was different. In the beginning, I liked punk a lot because a lot of [the bands] were even heavier than metal and I always tried to buy the heaviest things money could buy. I really liked Judas Priest, AC/DC, the things everyone listened to. But punk bands like The Exploited, Discharge, GBH, attracted me a lot. When I had parties at my house, I always liked to play the heaviest stuff. Obviously, we also put on better-known stuff like Motörhead, which was fantastic, but I always looked for more underground, extreme stuff, and I think that punk has always been a great influence on metal.
I know a lot of punks who started out as metalheads, but when they heard the rawness of punk, they switched over. Why didn't you ever jump over to punk?
I was really a metalhead. And later, it turned out that a lot of commercial punk bands appeared. In the beginning, punk was a kind of revolutionary protest. But later, between 1984 and 1986, the decadence began. There were a ton of kids who decided to be punk, but weren't. So I stuck with metal and when Venom came out, it was great; there was no turning back. But punk also crossed over to metal. I saw a lot of people wearing Venom and Motörhead t-shirts who had never listened to the bands. In my opinion they did it because it was trendy to say that you listened to metal. But I think that music is more than that; it's also a lifestyle, you know?
Nowadays, there are a lot of people who miss the old school metal and are always thinking about how it sounded before. What do you think about that nostalgia?
I think it's good. I've met a lot of metalheads at concerts, lots of young people, and they tell me "We like the old school [stuff] a lot. We're really young, so we don't know much about the current scene, but we have albums from before." They tell me that it's the only way to play good metal. I like that because the new generation is really interested in learning the history of the music, where bands came from. It gives me hope because I see that they're not people who listen to metal just because the whole world does, but because they have a genuine interest. That's what keeps the metal scene alive and helps you never change your taste just because somebody else likes something different. That was always the idea from the beginning: To create without anyone telling you how to do it, and without having to make it commercial. I make the music I like to listen to, and that's the secret to it all.
Why do you think that metal is a global language that people understand and share all around the world?
Because metal is the most peaceful music there is. We have fans in North Korea who ask us why we don't go there to play. Unfortunately, we can't, and it's a shame. The people in that country aren't free. Some years ago, we tried to play in Vietnam and it's prohibited to be a metalhead there. But now, with the Internet, there's the advantage of being able to listen to music anywhere. So no matter whether it's prohibited or you don't have money to buy things, if you're a metalhead, you can find a way to access the music because it's something that comes from the heart. But it's a shame that we can't play wherever we want, a shame that this isn't a free world. We've played all over, and even so, we can't do a concert in Iraq or North Korea—it's incredible. But in South America, people are free and, furthermore, they love metal. It's not like in Germany, where you can see any band, at any time. Here's it's nothing special. But in South America, the people wait, they save up for concerts, and that's something you really have to respect.
You've always said that Sodom isn't a political band, but it is a band with a message, which is anti-war. In these times, when we're on the verge of a new world war, do you think that metal bands should be committed to conveying some sort of message?
Yes, I think it's more and more important. In recent years, we've written a lot of lyrics against war or about why I feel worried that another world war is on the way. And yes, we can't change things because we're not elected officials, but we can take the stage and shout about everything we feel, and that's really important. I'd like to have more time to try to change things, but Donald Trump or Putin wouldn't listen to me. Also, I don't want to write empty words, especially when there are so many problems in the world. We have to talk about our opinion and if you read between the lines, you can find the message. We want war to end.
What's the best part of a life dedicated to metal?
I'm a metalhead, but I'm not a rock star. I'm a fan who makes music. And the best is that I can earn a living making music—that's a dream come true. Lots of young people ask me, "How can I become a millionaire with this music?". The truth is, there's no way to do it. When we started, there were only a couple bands, but I'm really proud of my groups, the musicians I've played with, and the fans who keep this music alive. Many people forget that bands exist thanks to their fans, and that's why it's always important to give something back to the people.
Follow Noisey on Twitter.


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Susan from Fairfield Age: 27. Who can keep me company today?


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Germany faces problems in forming a coalition
Lana Hetzel, Staff Writer
January 19, 2018
Germany held federal elections on Sept. 24, and a coalition government has yet to be formed. Germany's electoral system utilizes a form of proportional representation in which each voter gets two votes, one for a candidate of their choice and the other for a party. Germany's federal parliament i...


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Digi TransPort WR54 Cellular Router (Single LTE, Wi-Fi)
In stock
Code: WR.DG/WR54
Quick Overview:
Rugged and secure cellular router with dual redundant communications for mission critical industrial and transportation applications.
Key features include:
* Future built with dual-core 880 MHz MIPS processor and PCIe 2.0 internal bus
* Link failover and load balancing across dual 600 Mbps CAT11 cellular
* High speed 867 Mbps 802.11ac Wi-Fi for WAN or LAN service
* Cryptographic co-processor with secure key generation and storage
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Ideal for mission critical applications requiring continuous connectivity, the Digi WR54 Cellular Router is designed for field longevity and edge computing. Routing and gateway functions are secured across traditional and wireless communications networks, protocols and interfaces – while bridging local private wired or wireless subnets across a public network – safeguarding message privacy and integrity.
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Each cellular transceiver employs Gobi LTE Advanced technology allowing use on major networks. Dedicated PCIe 2.0 internal data buses deliver 10 GT/s aggregate bandwidth ensuring low latency high bandwidth cellular communications. Four gigabit Ethernet ports connect other IP capable systems, each configurable as an isolated secure LAN or all four can function as a layer 2 switch.
Featuring the Digi TrustFence®, a suite that ensures the integrity of Digi WR54 security functions. A cryptographic co-processor protects particularly sensitive data such as stored passwords and encryption keys.


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I am a boy mom, and incredibly proud to be one. The privilege of being tasked with raising kind, compassionate, brave little gentlemen is something I do not take lightly. Plus, I always said that sororities weren't my thing, but I'd join a fraternity if that was allowed. Now, I basically live in my own fraternity – a wish come true!
I have never been a super "girly" girl. I spend less than ten minutes fixing my hair every day, use the least amount of makeup possible to still make me look presentable, and you'll be shocked to see me with a manicure or wearing high heels. Because of this, I always knew I'd love to have boys. Give me all the #boymom attire, please!
So often I am met with the same responses when people find out I have only boys. "Oh, you have to try one more time so you can get your girl." "When are you going to try again for a girl?" "You CAN'T just have boys. You need to try again to see if you'll have a girl." I know all of these comments are said with good intentions, and I know that if I did one day have a little girl I would be so in love with her!
BUT. Let me tell you just why I do NOT think I need to try again to have a girl.
1. Girls can't pee outside.
Or into a water bottle in the back of your car when you're in a public place without a public restroom. I won't say potty training my three-year-old was easy, but the fact that he could go out onto the porch to pee whenever necessary definitely helped the process along. Also, they rarely have to touch public toilets – THANK GOODNESS!
2. You don't have to do much with their hair.
Remember when I said I barely spend any time on my hair? Thankfully I can slap a little water and hairspray on their bedhead and tame it pretty well. I don't have to mess with ponytails and hair bows and all of the other cute options out there – the choices would overwhelm me!
3. Too many clothing options.
Again, I don't like to have to put a ton of thought into what I'm wearing (yoga pants, yes please!), so I can only imagine if I had dresses and leggings and cardigans and jackets and scarves, etc. to choose from when dressing my little girl. We throw on some soft pants and a soft shirt, hope it matches, and call it a day! As long as they're comfy and can easily crawl all over the ground, they're happy.
4. Weddings are expensive.
Bridesmaids are dramatic. That's all I will say about that.
5. Nobody loves their mama quite like a little boy.
They're messy and wild and loud, but the love they have for their moms is like none other, and nothing can ever replace that.
So, no, I don't really need to try again for a girl. I'm as happy as can be with these boys of mine, living in my own little version of a frat house.
Photo Credit: Gregory Creative // Carly Gregory


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Pre-opening Comments for Friday April 26th
U.S. equity index futures were mixed this morning. S&P 500 futures were down 1 point in pre-opening trade.
Amazon (AMZN $1902.25) is expected to open higher after reporting higher than consensus first quarter sales and earnings.
Intel (INTC $57.61) is expected to open lower after reporting lower than consensus first quarter sales and earnings.
Starbucks (SBUX $77.11) is expected to open higher after reporting higher than consensus first quarter sales and earnings.
Ford (F $9.40) is expected to open higher after reporting higher than consensus first quarter sales and earnings.
ExxonMobil (XOM $82.22) is expected to open lower after reporting lower than consensus first quarter earnings.
EquityClock's Daily Market Comment
Following is a link:
Note seasonality chart on Durable Goods Orders
Technicals on China deteriorated. Shanghai Composite broke below its 20 day moving average, short term momentum indicators turned lower and strength relative to the S&P 500 Index turned negative. Related North American equities (Materials, Energy, Industrials) responded by moving lower.
StockTwits released yesterday @EquityClock
Technical action by S&P 500 stocks to 10:00: Quietly bearish. No intermediate breakouts. Breakdowns: $PSX $MMM $IRM
Cameco $CCO.CA $CCJ, a TSX 60 stock moved below $15.10 Cdn and $11.29 U.S. setting an intermediate downtrend
Bombardier $BBD.B.CA, a TSX 60 stock moved below $2.55 completing a double top pattern.
SNC Lavalin $SNC.CA moved below $33.21 extending an intermediate downtrend
Canfor $CFP.CA moved below $12.91 extending an intermediate downtrend.
Celestica $CLS.CA moved below $11.09 and $9.86 on less than consensus results.
Methanex $MX.CA moved below $73.88 setting an intermediate downtrend.
Trader's Corner
Equity Indices and related ETFs
Daily Seasonal/Technical Equity Trends for April 25th 2019
Green: Increase from previous day
Red: Decrease from previous day
Daily Seasonal/Technical Commodities Trends for April 25th 2019
Green: Increase from previous day
Red: Decrease from previous day
Daily Seasonal/Technical Sector Trends for April 25th 2019
Green: Increase from previous day
Red: Decrease from previous day
Disclaimer: Seasonality and technical ratings offered in this report and at
www.equityclock.com are for information only. They should not be considered as advice to purchase or to sell mentioned securities. Data offered in this report is believed to be accurate, but is not guaranteed
|Sponsored By...
21 Responses to "Tech Talk for Friday April 26th 2019"
Larry/ON Says:
April 26th, 2019 at 8:46 am
US Q1 GDP 3.2% vs est 2.5%. We need David Rosenberg to now tell us that a recession is inevitable in 2019 and the sky is falling.
Larry/ON Says:
April 26th, 2019 at 9:03 am
US 3.2% Q1 GDP is even more remarkable because there was a 35 day government shutdown during that period. The bears will try to come up with all sorts of reasons like inventory builds to say the economy wasn't that strong. March was the strongest month in Q1 and April is reported to be shaping up as a strong month so growth is not decelerating. Q2 will be a strong quarter which will confound the skeptics.
James/Sudbury Says:
April 26th, 2019 at 9:51 am
Larry/ON re: #2. Post of the year! Don't forget to add how Rosie loves to mention how he "Called" the housing crash of 2008 whilst he was at Merrill. I'm certain these stopped clocks will be right some day but think of the damage to customer portfolios that these uber bears have caused. Meredith Whitney, Eric Sprott, Nandu Narayanan all scared investors into cash and have cost them dearly. Where are they now? Thanks Larry/ON for the post and happy trading !
bruce Says:
April 26th, 2019 at 10:08 am
Rosenberg made a speech in toronto last night……no change in his thinking…..what if he s right?..
Larry/ON Says:
April 26th, 2019 at 10:41 am
Bruce – Eventually there will be a recession but not in the near future. Rosenberg is wrong and he has to push out his call for a recession further into the future. When a China trade deal happens that will enable a lot of companies to make longer term decisions about expansion and it will be positive for growth. There has been a foot on the brake on trade for some time and it will soon come off.
Anybody remember hedge fund manager Kyle Bass and his End of The World prognostication a few years ago. Psychologically people are more willing to buy into pessimistic viewpoints as somehow they appear smarter than bullish ones. Short selling fund managers use this to their advantage when they target a company with apocalyptic projections. People have been burned on their investments in the past so that when positive things happen they tend not to believe it. They miss the boat and when they finally get in it is near the top. That said we can have a correction in this market at any time. Gotta know what you are going to do about it before it finally happens.
KC Says:
April 26th, 2019 at 10:55 am
Hello Ron/BC,
Have you ever looked at Harris Corp (HRS) ? What do you think about the chart ? Ready for a pull back ?
JP/BC Says:
April 26th, 2019 at 12:25 pm
Martin Armstrong emailed this today
Bank of Japan and the bond crisis
Posted Apr 26, 2019 by Martin Armstrong
BoJ Statement 4-24-2019
The Great Financial Unknown is now upon us. After 10 years of Quantitative Easing, the European Central Bank (ECB) in Europe owns 40% of the national debts in the EU and it can neither sell them nor stop buying without creating a Panic in Interest Rates. Likewise, the Bank of Japan (BoJ) owns between 70% and 80% of the ETF bond market in Japan. The Bank of Japan confirmed it is ending free market determination of interest rates for the municipal level and that they "will not require any procedures such as auction as the method of determining lending conditions." today it may introduce a lending facility for its exchange-traded fund buying program, which would allow it to temporarily lend ETFs to market participants.
Introduction of Exchange-Traded Fund (ETF) Lending Facility:
The Bank will consider the introduction of ETF Lending Facility, which will make it possible to temporarily lend ETFs that the Bank holds to market participants.
The statement at the end of the announcement on the last page on its monetary policy has left traders in shock. This appears that the BoJ realizes that it now effectively has destroyed its bond market and realizes that there is not only the end of a free market, but there is a contagion of surrounding lack of liquidity.
We have never before in the history of human society ever witnessed such a major financial crisis. The BoJ makes it clear it will continue its policy of Quantitative Easing. It stated plainly:
The Bank will continue with "Quantitative and Qualitative Monetary Easing (QQE) with
Yield Curve Control," aiming to achieve the price stability target of 2 percent, as long as it is
necessary for maintaining that target in a stable manner.
Both the ECB and the BoJ are completely trapped. They have destroyed their respective bond markets meaning they can no longer even tolerate a free market with respect to interest rates. They cannot stop buying government debt for there is no bid at these rates. We are far beyond every economic theory ever contemplated. How we deal with this government-created financial crisis will be extremely interesting. This is were opinion will just not suffice. There is no reference in history to turn to. This is where Socrates can guide us through the absolute chaos that we face.
Ron/BC Says:
April 26th, 2019 at 12:52 pm
Not near a computer now.
bruce Says:
April 26th, 2019 at 12:55 pm
tnx JP………..interesting read……I wonder if that means Armstrong is expecting an avalanche of foreign funds into the u.s. and the u.s.$??….just a guess…..
Ana Says:
April 26th, 2019 at 1:13 pm
#2. Larry/ON
#3. James/Sudburry
#4. bruce
Do you question the GDP with the tariffs and the shutdown? I have my doubts about this number.
$VIX is not supporting a higher $SPX but could change. I am watching that closely.
Ana Says:
April 26th, 2019 at 1:53 pm
So here comes the rollout of what actually was manipulated for that glorious GDP number:
"The federal deficit hit $234 billion in February, according to new data from the Treasury Department, an apparent record for a one-month time period.
According to Bloomberg, the previous monthly record was set seven years ago, at $231.7 billion.
Five months into the fiscal year, which began in October, the deficit has already surpassed a half-trillion dollars, reaching $544 billion.
The record deficit comes as the government paid back more corporate taxes than it took in, losing $669 million. Overall, corporate taxes for the year were down $14.3 billion in comparison to the same period last year.
The GOP tax law, which went into effect last January, has been projected to cost $1.9 trillion over a decade.
Spending, in the meantime, was up by $145.6 billion in comparison to the same five-month period last year, following a bipartisan agreement to boost both defense and nondefense discretionary spending.
The Congressional Budget Office has warned that rising deficits mean that the national debt is on track to hit 93 percent of GDP in a decade, the highest level since just after World War II.
Last month, the debt surpassed $22 trillion for the first time in the nation's history."
"So despite the upside surprise on gov. spending, final domestic demand (#GDP excluding trade and #inventories) grew just 1.5% annualized, the weakest since 2015."
Ron/BC Says:
April 26th, 2019 at 5:15 pm
HRS is at a major top which is very difficult to clear unlike the average resistance levels. Notice the breakout last October and the pullback that didn't hold and then plunged. Not unusual to see this. If I had a gun to my head I'd short it at the 168 area.
Canuck2004 Says:
April 26th, 2019 at 6:43 pm
Last week, TSX broke-out into blue sky territory, this week NASDAQ did the same, today S&P500 closed within an inch of breaking out….lol.
Of course the aggressive FED rate hike reversal has everything to do with it…the market told them to STOP…and they listened for once…and as a rule, when the year starts off BULLISH, the year ends BULLISH.
I suspect that the 5-6 years of ZERO interest rates and QE in US will act as a no-man's land, a dead zone, to be ignored by the traditional market cycle….and this year is an economic soft patch on a much longer than usual secular Bull market….extra long bull. The yield curve inversion is beginning to look like a traditional mid-cycle event, I would bet…but who knows….all uncharted territory here with zero rates for several years and QE…never been done before in USA…the dominant world economy.
Rates are unlikely to go much higher for years to come as per Gov. debt loads….so with muted rates, or even an odd rate cut…markets can only go higher.
Anyway, makes no difference to me, I'm a yield investor, so as long as the cash flow remains intact, I don't really care. Nice to see the accounts perk up though….lol.
bruce Says:
April 26th, 2019 at 9:54 pm
tnx Ana
if rates ever rise the interest paid on the u.s. $ debt will be enormous……..i m not sure just how they can continue to finance $ trillion plus annual deficits without causing inflation….the buffoon boasts constantly about the strong economy but at the same time nags the Fed to cut rates…..even Kudlow and Steve Moore are asking for lower rates……this cant end well….
Ron/BC Says:
April 27th, 2019 at 1:05 am
Yes it makes you wonder. With the Fed's debt loads etc I guess it's time to keep an eye on $GOLD as it hasn't done much since 2011. Price tends to have a summer low so it might be time to watch for a significant price low over the next few months. Could be a good trade ahead. It used to be a great trading vehicle most of the time along with the precious metal stocks.
Ron/BC Says:
April 27th, 2019 at 12:09 pm
The $SPX is at a double top being 2940 and major price resistance. This is the line in the sand where short sellers and major buyers battle. Price needs to clear AND hold above 2940 to see the short sellers throw in the towel. Technically price needs to rally 3% above this level to get rid of these short sellers and confirm a valid breakout has occurred. (nose pokes don't count). Then price needs to pullback to the breakout point AND hold above this level that needs to hold and serve as support. That is where conservative traders/investors buy with a new bottom in place to rally further from to new highs. Big test here to see who wins this battle of the big players as both have very deep pockets.
bruce Says:
April 27th, 2019 at 2:00 pm
next week we get the fed statement on wednesday and the employment numbers on friday…..that should help the s&p decide which way it wants to go…..
Mary Says:
April 27th, 2019 at 6:45 pm
I read….
SPX to 3150
Each politician only cares about their term(s) to look good in history. Let the other worry about the future until….
Ron/BC Says:
April 27th, 2019 at 9:18 pm
Here is the Fear & Greed Index again. Look at the first Indicator showing the number of PUT Options. It is at the lowest level in two years meaning most everyone is bullish ahead with very few wanting to short the market. And the $SPX is at the highest level above its 125 day average in two years. This of course doesn't mean a certain selloff but is not a good sign if long. But again,the most important thing is for price to finally "clear and hold" above the double top and run up 3% and then pullback to or close to the breakout point for the next test of the breakout point that needs to hold as support. And then a rally to new highs to put all the battles behind it. The $SPX is what is watched world wide,not the Nasdaq which can soar or plunge at any time due to the nature of the Nasdaq's volatile stocks. Recall the dot com period. So the big boys will be facing each other at this price point of the $SPX. Should be a noteworthy battle…
bruce Says:
April 28th, 2019 at 2:47 pm
the aaii sentiment survey for april 25th is 33.5% bullish 46.3% neutral and 20.2% bearish……..that s an unusually high neutral number…….fence sitters?..
Ron/BC Says:
April 28th, 2019 at 3:45 pm
Yes that is odd. These sentiment readings can be deciptive as you have to question who they are asking. The general public or fund managers or futures traders,etc.
But the big boys know they both (breakout buyers & short sellers) have a battle on their hands. Doesn't matter much what retail stock holders do but their irrational exuberance does warn of trouble at extremes being so many are emotional traders. The last thing you want to see if long stocks is average stock holders talking bullish and giddy about it. "IF" the buyers can blow through that old high "and keep it above", those short sellers will have to cover which drives prices even higher. That is why the history books say you need a 3% breakout for valid breakout. Even with deep pockets the short sellers won't hold their losing positions that far up. Then the big test of the breakout point down the road. No one is paying much attention to $GOLD so that might be the next one to run. Would be nice to see a major selloff with extreme bearish sentiment to get long with.


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Beatriz Summers has enjoyed the relationships that she has built with patients since joining our dental team over 5 years ago as a RDA. She completed her study with Northern VA Community College in Radiation Safety 2009. Beatriz, her husband and daughter enjoy living in the Northern Virginia area and serving the community. She is known for her kind chairside manner. Beatriz speaks Spanish from her native Bolivia & English. Beatriz also has a bachelor's degree from college. Her knowledge and experience gained through her continuing education has been a great asset to our practice.
Beatriz says, "I am so proud to be part of the team. We are like a family, and we all want to be the best we can be. My goal is to make your experience in our office so enjoyable you'd tell a friend and invite them to be a patient as well."
Rajan was recently welcomed as a new staff member to our office. She attended NOVA and then VCU with aspirations of becoming a dentist. Rajan is X-Ray certified. She enjoys the dental environment, greeting people with a bright smile and happy demeanor. Rajan is native of India, but was raised for almost half her life in the USA. She speaks English, Urdu, Hindi and Punjabi.
Rajan says: "My goal is to provide our patients with the best care and treat them with the utmost r
Shue Jen Chuang, D.D.S.
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- Give Kids a Smile
- Speaker in dental health and career in various schools and communities
- Thomas Jefferson HS volunteer
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David W. Lucht, D.D.S.
- Worked for United way for 1 year as a dental provider
- Mom's project
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- Michigan State University:
- University of Michigan DDS degree


text:
Boris Johnson remains in hospital this evening – still suffering from persistent symptoms of the coronavirus, including a temperature and a cough. Despite that, he is still leading the government.
The Foreign Secretary Dominic Raab said Mr Johnson was in good spirits and was being regularly updated on the government's work.


text:
This morning, my client reached the six hour point of his 10 hour course in Basic Muay Thai for Martial Arts Cross Training. We spent a lot of time looking at fundamental principles of movement and positioning. My aim was to bring in knee strikes, which is a natural choice of short-range techniques to follow on from last lesson's elbow strikes. However, in order to do this I needed to feel confident my client had a strong delivery method. Thai strikes rely a lot on rhythmic and fluid body mechanics. The teep and the straight knee share many different similarities. Having looked a lot at correct posture alignment and footwork each lesson, I was happy to see my client was able to self-critique and self-correct throughout his training.
We warmed up with footwork, moving off all angles and directions, gradually building on punching and kicking combinations. We revised the teep and the round kick, building height and increasing mobility. These were then take onto the focus mitts and mixed in with the elbow strikes taught last lesson. All basic elbow strikes were revised – horizontal, slashing, uppercut, diagonal, chopping, spear, side, smash-down, backward and spinning.
The knees were then brought into play. Here I made great emphasis on the importance of negative force. Efficient and smooth techniques rely on sending a technique back from its targets down its respective powerline. Techniques often lose force from the moment of execution if the fighter is not disciplined in understanding where the technique will return. This especially seems to come out with short-range techniques. If done correctly, the fighter creates a shockwave in the target area and retains a good defensive position after striking. The negative element of training is an often neglected component. Many strength coaches make this point with understandable frustration. Negative movement greatly improves positive movement. Boxing coaches often cite the problem with not enough boxers paying attention to pulling exercises. The same applies with the execution of a technique. The negative aspect increases impact, strengthens stability and improves recovery. We covered the straight and the diagonal knee strike.
Below please see image of my old Muay Thai kru, Tony Hayes, demonstrating an excellent straight knee technique.


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Top 10 MC's
So Billboard recently dropped their greatest mC's of all time list, and it pissed off a few people to say the least. Earlier Ambrosia For Heads also dropped their seemingly never ending GOAT Tourney and it left a bad taste in some readers mouth. Now Urban Mogul Life is here to end the debate once […]
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