Trends in Daily Temperature and Precipitation Extremes for the Southeastern United States: 1948–2012

Abstract

Spatial and temporal trends in temperature and precipitation extremes were investigated for the period 1948–2012 across the southeastern United States using 27 previously defined indices. Results show that regionwide warming in extreme minimum temperatures and cooling in extreme maximum temperatures occurred. The disproportionate changes in extreme daytime and nighttime temperatures are narrowing diurnal temperature ranges for most locations. The intensity and magnitude of extreme precipitation events increased overall, except for more easterly locations, particularly in South Carolina. These indices further show that warming in minimum temperatures has been pronounced most in summer and least in winter. Fall has become significantly wetter, while spring and summer have become drier, on average. Principal component analysis (PCA) was used to characterize a “geography of extremes” based on temperature and precipitation extreme indices. The PCA based on temperature indices revealed two coherent western and eastern subregions that share common modes of variability in extremes. Precipitation indices resulted in a greater number of smaller, spatially coherent groups exhibiting similar modes of variability. This classification regime illustrates important variations in extremes that exist on subregional scales. These findings have relevance for established climate research institutes, local governments, resource managers, and community planners interested in the variability of extreme events throughout the region.

Spatial and Temporal Variability in the Precipitation Concentration in the Upper Reaches of the Hongshui River Basin, Southwestern China

“…Finally, a quality control evaluation and a homogeneity assessment of daily precipitation data were performed using the software packages RclimDex and RHtests V4 (http://etccdi.pacificclimate.org/software.shtml). These two programs have been widely used in the quality control, homogenization, and extreme index classification of precipitation data [12,[34][35][36][37][38]. The processes of quality control evaluation and homogeneity assessment include (1) identifying errors in the precipitation data, such as precipitation values below 0 mm; (2) searching for outliers, where we choose 3 standard deviations as the threshold for a fineresolution quality control of the data; (3) using plots of generalized data in RclimDex to visually inspect the data and further identify outliers and a variety of other problems that may cause errors or bias in analysing changes in the seasonal cycles or variance of the data; and (4) using RHtestsV3 to detect artificial shifts that could exist in a time series.…”

“…Both global [7] and regional [8,9] precipitation characteristics have changed due to the influence of climate change and intensified human activities. A significantly decreasing number of rain days and significantly increasing precipitation intensities have been identified in many places around the world, such as in China [8,10], the United States [11,12], Spain [13], Iraq [14], and Siberia [15].…”

“…Therefore, extreme climatic and weather events have garnered considerable attention from public, government, 2 Advances in Meteorology and academic communities around the world [12,[16][17][18][19]. In recent years, numerous studies have shown that the frequencies of extreme precipitation events and associated disasters have significantly increased.…”

Trend and Homogeneity Analysis of Precipitation in Iran

“…In the rainfall seasonality analysis in Iran, Talaee et al (2014) noted that rainfall showed a decreasing trend in Iran in the previous years [1,2]. Some researchers [3][4][5][6][7] recognized indirect indications of trend and long-term variability of rainfall. Analysis of rainfall seasonality is important in investigating the influence of climate variability on the regional climate and environmental conditions [5].…”

Spatial-temporal Variability of Seasonal Precipitation in Iran

“…The spatial-seasonal forecasts using least squares for precipitation in Iran with additive models are presented in section 3. The forecast model is a least squares method for seasonality estimation and precipitation variations [61]. Since the additive model of seasonality has an acceptable condition, the measuring model has an appropriate quality.…”