Chunlan Li, Jun Wang, Min Liu, Desalegn Yayeh Ayal, Qian Gong, Richa Hu, Shan Yin and Yuhai Bao
Extreme high temperatures are a significant feature of global climate change and have become more frequent and intense in recent years. These pose a significant threat to both…
Abstract
Purpose
Extreme high temperatures are a significant feature of global climate change and have become more frequent and intense in recent years. These pose a significant threat to both human health and economic activity, and thus are receiving increasing research attention. Understanding the hazards posed by extreme high temperatures are important for selecting intervention measures targeted at reducing socioeconomic and environmental damage.
Design/methodology/approach
In this study, detrended fluctuation analysis is used to identify extreme high-temperature events, based on homogenized daily minimum and maximum temperatures from nine meteorological stations in a major grassland region, Hulunbuir, China, over the past 56 years.
Findings
Compared with the commonly used functions, Weibull distribution has been selected to simulate extreme high-temperature scenarios. It has been found that there was an increasing trend of extreme high temperature, and in addition, the probability of its indices increased significantly, with regional differences. The extreme high temperatures in four return periods exhibited an extreme low hazard in the central region of Hulunbuir, and increased from the center to the periphery. With the increased length of the return period, the area of high hazard and extreme high hazard increased. Topography and anomalous atmospheric circulation patterns may be the main factors influencing the occurrence of extreme high temperatures.
Originality/value
These results may contribute to a better insight in the hazard of extreme high temperatures, and facilitate the development of appropriate adaptation and mitigation strategies to cope with the adverse effects.
Details
Keywords
Chunlan Li, Xinwu Xu, Hongyu Du, Debin Du, Walter Leal Filho, Jun Wang, Gang Bao, Xiaowen Ji, Shan Yin, Yuhai Bao and Hossein Azadi
The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the…
Abstract
Purpose
The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C.
Design/methodology/approach
In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau.
Findings
Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD.
Originality/value
Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.
Details
Keywords
Li Xuemei, Yun Cao, Junjie Wang, Yaoguo Dang and Yin Kedong
Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey…
Abstract
Purpose
Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.
Design/methodology/approach
This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.
Findings
The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.
Originality/value
Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.