Abstract
In this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high temperature prediction was constructed. Several non-parametric methods were used to analyse the trend of meteorological variables. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature and average temperature in Lanzhou all have a significant upward trend and show different performances in each season. In detail, the trend of maximum temperature in the summer is not significant, while the upward trend of minimum temperature in the winter is the most significant, which leads to more and more “warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperature and obtain the conclusion that the prediction results by the model are consistent with the actual situation.
Highlights
IntroductionThe trend analysis of temperature change is one of the main aspects of climate change research and an important means to understand global climate change
The trend analysis of temperature change is one of the main aspects of climate change research and an important means to understand global climate change.The Intergovernmental Panel on Climate Change(IPCC) assessment points out that there has been a marked trend in global temperature change since the20th century and that the frequency of extreme weather and climate events has increased
The trend analysis of temperature change plays an important role in the detection of climate change
Summary
The trend analysis of temperature change is one of the main aspects of climate change research and an important means to understand global climate change. Many scholars have done extensive research on the trend analysis of temperature variation in different regions It is the main content of research to use trend analysis to explore the trend, direction, amplitude and abrupt of hydro-climatic data change. Partal (2017); Sharma et al (2016)explored the annual and multi-annual variation characteristics of data such as temperature and rainfall by using trend analysis method and summarized the law of climate change. In the above trend analysis articles, most studies only used the non-parametric methods to study whether there is a change trend, and few articles paid attention to the effect of sequence autocorrelation on the trend Both the MK test and Sen’s slope estimation require time series to be serially independent which can be accomplished by using the pre-whitening technique Detailed descriptions of temperature and rainfall are presented
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