Abstract:Record-breaking hot and cold extremes have occurred in China in recent years and, therefore, it is compelling to investigate the long-term trend in temperature extremes at individual stations to see whether they have become more frequent. Many previous studies on the linear trend analysis of temperaure extremes in China have used oridinary least squares (OLS) regression, without consideration of non-Gaussian and/or serially dependent characteristics, or nonparametric methods, again not considering the latter, … Show more
“…The difference between them is statistically significant according to a two-sample Kolmogorov-Smirnov (K-S) test (p < 0.05). The K-S test is a non-parametric test, suitable for extreme indices which generally show non-Gaussian distribution characteristics 35,36 .Figure 1The spatial pattern of annual-mean temperature ( a – c ) over China under stabilized 1.5 °C and 2 °C global warming relative to 2006–2015 (Units: °C). Dotted areas are statistically significant according to a two-sample Kolmogorov-Smirnov (K-S) test (p < 0.05).…”
The 1.5 °C global warming target proposed by the Paris Agreement has raised worldwide attention and inspired numerous studies to assess corresponding climate changes for different regions of the world. But CMIP5 models based on Representative Concentration Pathways (RCP) are ‘transient simulations’ and cannot reflect the response of climate warming stabilized at 1.5 °C. The current work presents an assessment of extreme temperature changes in China with simulations from ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) project specially conceived for global warming levels stabilized at 1.5 °C and 2.0 °C. When global warming stabilizes at 1.5 °C/2.0 °C, the areal-mean temperature for whole China increases by about 0.94 °C/1.59 °C (relative to present period, taken from 2006–2015). Notable increase regions are mainly found in Northwest and Northeast-North China, but warm spell duration increases mostly in Southeast China. The effect of the additional 0.5 °C warming is particularly investigated and compared between the transient and stabilized simulations. Changes of mean and extreme temperature are larger in transient simulations than in stabilized simulations. The uncertainty range is also narrower in stabilized simulations. Under stabilized global warming scenario, extreme hot event with return period of 100 years in the present climate becomes event occurring every 4.79 (1.5 °C warming level) and 1.56 years (2.0 °C warming level), extreme cold event with return period of 10 years becomes event occurring every 67 years under 1.5 °C warming and is unlikely to occur under 2.0 °C warming. For geographic distribution, the occurrence probabilities of extreme (hot and cold) events mainly change in the Tibetan Plateau, and the extreme cold events also change in Northeast and Southeast China.
“…The difference between them is statistically significant according to a two-sample Kolmogorov-Smirnov (K-S) test (p < 0.05). The K-S test is a non-parametric test, suitable for extreme indices which generally show non-Gaussian distribution characteristics 35,36 .Figure 1The spatial pattern of annual-mean temperature ( a – c ) over China under stabilized 1.5 °C and 2 °C global warming relative to 2006–2015 (Units: °C). Dotted areas are statistically significant according to a two-sample Kolmogorov-Smirnov (K-S) test (p < 0.05).…”
The 1.5 °C global warming target proposed by the Paris Agreement has raised worldwide attention and inspired numerous studies to assess corresponding climate changes for different regions of the world. But CMIP5 models based on Representative Concentration Pathways (RCP) are ‘transient simulations’ and cannot reflect the response of climate warming stabilized at 1.5 °C. The current work presents an assessment of extreme temperature changes in China with simulations from ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) project specially conceived for global warming levels stabilized at 1.5 °C and 2.0 °C. When global warming stabilizes at 1.5 °C/2.0 °C, the areal-mean temperature for whole China increases by about 0.94 °C/1.59 °C (relative to present period, taken from 2006–2015). Notable increase regions are mainly found in Northwest and Northeast-North China, but warm spell duration increases mostly in Southeast China. The effect of the additional 0.5 °C warming is particularly investigated and compared between the transient and stabilized simulations. Changes of mean and extreme temperature are larger in transient simulations than in stabilized simulations. The uncertainty range is also narrower in stabilized simulations. Under stabilized global warming scenario, extreme hot event with return period of 100 years in the present climate becomes event occurring every 4.79 (1.5 °C warming level) and 1.56 years (2.0 °C warming level), extreme cold event with return period of 10 years becomes event occurring every 67 years under 1.5 °C warming and is unlikely to occur under 2.0 °C warming. For geographic distribution, the occurrence probabilities of extreme (hot and cold) events mainly change in the Tibetan Plateau, and the extreme cold events also change in Northeast and Southeast China.
“…The present studies on extreme temperature changes in mainland China show that although the most extreme high temperatures were increasing and extreme low temperatures were decreasing, there were certain differences between regions and magnitudes. It has been reported that days of extreme temperatures at some observatories in mainland China do not conform to a normal distribution (Qian et al 2019;Shen et al 2017;Zhang et al 2020;Xing et al 2020). Therefore, this difference may be related to the methods and data.…”
Section: Frost Days Cold Days and Warm Daysmentioning
Grain yield may be affected in the future by climate change. Some studies have suggested that variations in the seasonal cycle of temperature and season onset could affect the efficiency in the use of radiation by plants, which would then affect yield. However, the study of the temporal variation in extreme climatic variables is not sufficient in China. Therefore, this article evaluates the distribution of extreme temperature seasonality trends in mainland China, describes the trends in the seasonal cycle, and detects changes in extreme temperature characterized by the number of hot days (HD), and frost days (FD) and the frequency of warm days (Tx90p), cold days (Tx10p), warm nights (Tn90p) and cold nights (Tn10p). All data are from the EAR5 reanalysis for the 1979-2020 periods.The results show a statistically significant positive trend in the annual average amplitudes (A0) of extreme temperatures. The change in Txmax was the smallest, but it also accounted for 84.5% of the total area. The annual amplitude (A1) and phase (F1) experienced less variation than A0 for extreme temperatures in mainland China. A1 of the maximum temperature decreased significantly on the Tibetan Plateau and increased significantly in the Tianshan Mountains and Jungar Basin (mainly Taxman). F1 of the maximum temperature exhibit a negative trend in approximately 30% of mainland China, and the trend appeared in some regions except in the Northeast and Southwest. Although A1 of the minimum temperature was not as large as that of the maximum temperature, its distribution was very characteristic and it was almost bounded by the 400 mm isohyet, increasing in the Northwest and decreasing in the Southeast. In terms of the number of days, there was an increase in HD, Tx90p, and Tn90p, as well as a decrease in FD, Tx10p, and Tn10p. This number of days also indicates that temperature has increased over mainland China in the past 42 years.
“…In Brazil, these tests have been applied to identify significant changes in temperature (Ávila et al, 2014;Ferreira et al, 2015;Neves et al, 2016) and in rainfall (Ely and Dubreuil, 2017;Zilli et al, 2017). In the same way, worldwide studies were conducted to confirm changes in the weather variables mentioned previously (Chen and Zhai, 2017;Shi et al, 2018;Qian et al, 2019).…”
Climate changes have been observed all over the world and are pointed out as responsible to impact the natural resources, especially the amount and the quality of the water in a hydrographic basin. Thus, the present research aimed to analyze the variability and trends of air temperature and rainfall data in the Hydrographic Basin of the Ribeirão do Lobo, São Paulo state, Brazil. The weather data from 1988 to 2017 were collected in the Center of Water Resources and Environment Studies of the University of São Paulo (CRHEA), and in five rainfall stations of the National Water Agency of Brazil (ANA) spread all over the basin. To identify climate changes were applied the simple regression, the run test, the Mann-Kendall and modified Mann-Kendall tests. The results showed significant positive trends in air temperature, especially in the annual period and during the third quarter with rising up to 0.068 °C year-1. Significant increases were verified to minimum, maximum and mean air temperature, which suggests possible climate changes during the period in the analysis. To the rainfall data, periods with positive and negative trends were found, although, most of the time the trends were non-significant. However, a significant negative trend of the rainfall was observed in Ribeirão do Feijão station with a reduction of -10.03 mm year-1.
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