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The wetland ecosystem is particularly vulnerable to hydrological and climate changes. The Great Xing'an Mountain is such a region in China that has a large area of wetlands with rare human disturbance. The predictions of the global circulation model CGCM3 (the third-generation coupled global climate model from the Canadian Centre for Climate Modeling and Analysis) indicated that the temperature in The Great Xing'an Mountain will rise by 2-4°C over the next 100 years. This paper predicts the potential distributions of wetlands in this area under the current and warming climate conditions. This predication was performed by the Random Forests model, with 18 environmental variables, which will reflect the climate and topography conditions. The model has been proven to have a great prediction ability. The wetland distributions are primarily topography-driven in the Great Xing'an Mountains. Mean annual temperature, warmness index, and potential evapotranspiration ratio are the most important climatic factors in wetland distributions. The model predictions for three future climate scenarios show that the wetland area tends to decrease, and higher emission will also cause more drastic shrinkage of wetland distributions. About 30% of the wetland area will disappear by 2050. The area will decrease 62.47, 76.90, and 85.83%, respectively, under CGCM3-B1, CGCM3-A1B, and CGCM3-A2 by 2100. As for spatial allocation, wetlands may begin to disappear from the sides to the center and south to north under a warming climate. Under CGCM3-B1, the loss of wetlands may mainly occur in the south hills with flatter terrain, and some may occur in the north hills and intermontane plains. Under CGCM3-A1B, severe vanish of wetlands is predicted. Under CGCM3-A2, only a small area of wetlands may remain in the north of the high mountains. [ABSTRACT FROM AUTHOR]

Near-surface temperature records show that China warmed by about 0.8 °C from 1950 to 2010. However, there exists an ongoing debate about whether this warming might have been partially due to urbanization bias. In fact, homogenization approaches may be inefficient in densely populated provinces that have experienced a significant urban development since the 1940s. This paper aims to complement previous research on the topic by showing that an alternative approach based on the analysis of the divergence between the minimum (Tmin) and maximum (Tmax) near-surface temperature records since the 1940s could be useful to clarify the issue because urban heat island (UHI) effects stress the warming of nocturnal temperatures more than the diurnal ones. Then, the significance of the divergence observed in the data could be evaluated against the expectations produced by the CMIP5 general circulation model simulations. From 1945–1954 to 2005–2014, on average and over China, these models predict that Tmin had to warm 0.19 ± 0.06 °C more than Tmax. However, during the same period, the climatic records show that Tmin warmed 0.83 ± 0.15 °C more than Tmax. A similar analysis demonstrates that the effect is more pronounced during the colder months from November–April than during the warmer ones from May to October. A comparison versus China urbanization records demonstrates that the regions characterized by a large Tmin-Tmax divergence are also the most densely populated ones, such as north-east China, that have experienced a diffused and fast urbanization since the 1940s. The results are significant and may indicate the presence of a substantial uncorrected urbanization bias in the Chinese climate records. Under the hypothesis that Tmax is a better metric for studying climatic changes than Tmean or Tmin, we conclude that about 50% of the recorded warming of China since the 1940s could be due to uncorrected urbanization bias. In addition, we also find that the Tmax record from May to October over China shows the 1940s and the 2000s equally warm, in contrast to the 1 °C warming predicted by the CMIP5 models. • Mean surface temperature in China has warmed by 0.8 °C since 1950. • Atmospheric boundary layer physics predicts higher UHI warming during night. • The divergence between Tmin and Tmax is used to detect urbanization biases. • The China regions with the strongest Tmin-Tmax divergence are also the most populated. • Thus, the temperature warming in China is partially due to UHI biases. [ABSTRACT FROM AUTHOR]