Study on Trends of Extreme Temperature in China Considering Data Homogenization and Autocorrelation
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Abstract:
China frequently experiences extreme temperature events, which often have severe impacts on social production and daily life. Therefore, it is of great importance to study the long-term trends of extreme temperature changes. The homogenisation of the observation dataset is crucial for detecting temperature change trends. In the meantime, whether to consider time series autocorrelation can also affect the detection results. Failure to consider the homogenisation of the temperature dataset or the autocorrelation of the temperature time series brings about uncertainty in research conclusions. In addition, the higher the spatial coverage of observation sites, the more advantageous it is to reveal spatial differences in change characteristics. This study analyses the trends of extreme temperature changes in China during the period of 1961-2021 using a homogenised daily station temperature dataset with the most spatial coverage currently, while taking into account the impacts of time series autocorrelation. For China as a whole, the annual warm nights (days), where daily minimum (maximum) temperature is above its 90th percentile, have an increasing trend of 10.3 (5.9) d/10a, while the annual cold nights (days), where daily minimum (maximum) temperature is below its 10th percentile, have a decreasing trend of -7.8 (-3.6) d/10a on space average, respectively. The warming rates of the annual coldest night (TNn), warming night (TNx), coldest day (TXn), and warmest day (TXx) are 0.52, 0.30, 0.30, and 0.21 ℃/10a on space average, respectively. For the regional average time series of extreme temperature in China, the percentage differences between the original trend and the decorrelation trend are all less than 5%. For a single station, the impact of time series autocorrelation on the magnitude of long-term linear trend is less than 10% for most stations, but there are also some stations with impacts exceeding 50%. There are great differences between extreme temperature changes and average temperature changes. For example, although the summer warming trend is the weakest in terms of the regional average minimum and maximum temperatures in China, the increasing trend of the regional average warm nights and warm days is the strongest during summer, while the increasing trend is the weakest during winter. With higher spatial coverage of station datasets, this study reveals more details of extreme temperature changes in China. For example, TXx shows an especially pronounced warming trend in urban agglomerations such as the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Further research is needed to determine whether this is related to urbanisation.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.