Spatial and Temporal Variations of Heat Waves in China from 1961 to 2010

Ye, Dianxiu; Yin, Jifu; Chen, Zhenghong; Zheng, Yu; Wu, Rongrong

doi:10.3724/sp.j.1248.2014.066

Cited by 42 publications

(9 citation statements)

References 4 publications

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“…Firstly, many previous studies on the estimation of the linear trend in temperature extremes in China used OLS regression to estimate the spatial pattern of linear trends at individual stations (e.g., Qian and Lin 2004;Ding et al 2010;Huang et al 2010;Jiang et al 2012;Ye et al 2014, among many others). Caution should be applied in that, for a single station in China, there are quite a few stations other than Shanghai whose temperature extreme indices are nonGaussian, which will be reported in detail in another paper.…”

Section: Discussionmentioning

confidence: 99%

On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai

Qian

2015

Clim Dyn

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Section: Discussionmentioning

confidence: 99%

On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai

Qian

2015

Clim Dyn

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“…Correspondence to: S. Sugimoto, shiorisug@jamstec.go.jp The Sichuan basin has an area of~190,000 km 2 (Figure 1) and is a major paddy cultivation area in China (Liu et al, 2005). The basin frequently experiences extremely hot summers (Wang et al, 2016;Ye et al, 2014), and mesoscale convective systems generate and develop during the summer (Sugimoto & Ueno, 2012;Ueno et al, 2011), causing flood events on the Yangtze River (Xie & Ueno, 2011). Thus, the Sichuan basin is a remarkable region with respect to the occurrence of extreme summer events, and more accurate simulations would improve our understanding of the physical mechanisms associated with the regional-scale climate over the basin during the summer.…”

Section: 1029/2018jd029434mentioning

confidence: 99%

Modification of Near‐Surface Temperature Over East Asia Associated With Local‐Scale Paddy Irrigation

2019

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This study used two regional climate model experiments to assess the impact of paddy irrigation in the Sichuan Basin, China, on the near‐surface temperature across East Asia: a control experiment (CTL run), which was used to calculate soil moisture using an unmodified land surface scheme and a sensitivity experiment (SEN run) that incorporated an increase in soil moisture over the basin at the beginning of the rice‐growing season. Although the near‐surface temperature in the Sichuan Basin was higher in the CTL run than in the observations, it improved in the SEN run because of a change in the Bowen ratio associated with the increase in soil moisture. In the SEN run, the modification of local‐scale land surface wetness in the basin causes the decrease in the near‐surface temperature over northeastern China and its increase over southern China relative to that in the CTL run because of a change in the cloud convection associated with precipitation, which, in turn, modifies the solar and net radiation at the surface and the partitioning into the sensible and latent heat fluxes. The greenhouse effect associated with an increase in precipitable water in the SEN run also influences near‐surface warming over southeastern China and the ocean north of southwestern Japan, where the impacts of radiation processes and surface heat fluxes on the near‐surface temperature are small. Consequently, these results suggest that local‐scale modifications to land surface wetness in the Sichuan Basin result in changes in the spatial distribution of near‐surface temperatures over a part of East Asia.

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“…Ordinary least squares (OLS) regression is the most commonly used linear trend estimator (IPCC 2013). Many previous studies on the estimation of the linear trend in temperature extremes in China have used OLS regression to estimate the spatial pattern of linear trends at individual stations, and used the Student's t-test or F-test to assess the corresponding statistical significances (e.g., Ding et al 2010;Huang et al 2010Huang et al , 2015Wang et al 2012Wang et al , 2018Du et al 2013;Zhao et al 2013;Ye et al 2014;Ding and Ke 2015;Zhou et al 2016;Liu et al 2018). Some studies have even used OLS regression and the Student's t-test to estimate the spatial pattern of precipitation extreme indices in high-resolution grids (e.g., Zhou et al 2016) or at individual stations (e.g., Du et al 2013;Zhao et al 2013;Liu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Linear trends in temperature extremes in China, with an emphasis on non-Gaussian and serially dependent characteristics

Qian

Zhang

2018

Clim Dyn

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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, thus leaving some uncertainty in the significance testing. The present study examines in detail these characteristics in eight commonly used extreme temperature indices, on the basis of both station data and gridded data across China. The results show that 71-100% of the stations or grids cannot directly use standard OLS regression to analyze the statistical significance of the linear trend, because of either non-Gaussian or Gaussian but serially dependent characteristics in the regression residuals. Also, more than 43% of the stations and more than 54% of the grid boxes for annual indies cannot directly use the original Sen's slope estimator and Mann-Kendall test because of serial dependence. Based on a nonparamtric method that takes into account serial dependence, the spatial patterns of the linear trend on an annual basis, as well as in hot and cold extremes, are examined for the period 1960-2017. The results show that hot extremes at most stations have increased, more than 57% of which are statistically significant; whereas, cold extremes at almost all stations have decreased, more than 32% (85%) of which are statistically significant during daytime (at night).Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Spatial and Temporal Variations of Heat Waves in China from 1961 to 2010

Cited by 42 publications

References 4 publications

On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai

On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai

Modification of Near‐Surface Temperature Over East Asia Associated With Local‐Scale Paddy Irrigation

Linear trends in temperature extremes in China, with an emphasis on non-Gaussian and serially dependent characteristics

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