Asymmetric seasonal daytime and nighttime warming and its effects on vegetation in the Loess Plateau

Over the period 1982–2015, temperatures have exhibited an asymmetric warming pattern diurnally, as well as seasonally across the Loess Plateau. However, very limited research has studied the implications and effects of such seasonally heterogeneous warming across the Loess Plateau. In this study, we also analyzed the time series trends and seasonal spatial patterns of the maximum (T(max)) and minimum (T(min)) temperatures and evaluated how different vegetation responded to daytime and nighttime warming in the Loess Plateau from 1982 to 2015 based on the NDVI and meteorological parameters (precipitation or temperature). We found that T(max) and T(min) significantly increased throughout the years except for T(max) in autumn, and the diurnal asymmetric warming showed some striking seasonal differences. For example, the increasing rates of T(min) in spring, summer, autumn, and winter were 0.75, 1.20, 1.88, and 1.10 times larger than that of T(max), respectively. NDVI showed significantly positive correlation with T(max) and T(min) in spring and winter, while NDVI presented significantly positive correlation with T(min) in summer and T(max) in autumn across entire Loess Plateau. Furthermore, we also discovered diverse seasonal responses in terms of vegetation types to daytime and nighttime warming. For instance, Spring NDVI showed significantly positive partial correlations with T(max) and T(min). In summer, grasslands and wetlands merely displayed significantly positive partial correlations with T(min). Cultivated land presented significantly positive partial correlation between the NDVI and T(max) (T(min)) in autumn. In winter, cultivated land, forest, and grassland exhibited significantly positive partial correlation with T(max) and T(min), while only wetland showed a significantly positive partial correlation with T(max). Our results demonstrated responses of vegetation to climate extremes and enhance a better understanding of the seasonally different responses of vegetation under global climate change at different scale.