Alpine treeline, as a prominent ecological boundary between forested mountain slopes and alpine meadow/shrub, is highly complex in altitudinal distribution and sensitive to warming climate. Great efforts have been made to explore their distribution patterns and ecological mechanisms that determine these patterns for more than 100 years, and quite a number of geographical and ecophysiological models have been developed to correlate treeline altitude with latitude or a latitude related temperature. However, on a global scale, all of these models have great difficulties to accurately predict treeline elevation due to the extreme diversity of treeline site conditions. One of the major reasons is that “mass elevation effect” (MEE) has not been quantified globally and related with global treeline elevations although it has been observed and its effect on treeline elevations in the Eurasian continent and Northern Hemisphere recognized. In this study, we collected and compiled a total of 594 treeline sites all over the world from literatures, and explored how MEE affects global treeline elevation by developing a ternary linear regression model with intra-mountain base elevation (IMBE, as a proxy of MEE), latitude and continentality as independent variables. The results indicated that IMBE, latitude and continentality together could explain 92% of global treeline elevation variability, and that IMBE contributes the most (52.2%), latitude the second (40%) and continentality the least (7.8%) to the altitudinal distribution of global treelines. In the Northern Hemisphere, the three factors’ contributions amount to 50.4%, 45.9% and 3.7% respectively; in the south hemisphere, their contributions are 38.3%, 53%, and 8.7%, respectively. This indicates that MEE, virtually the heating effect of macro-landforms, is actually the most significant factor for the altitudinal distribution of treelines across the globe, and that latitude is relatively more significant for treeline elevation in the Southern Hemisphere probably due to fewer macro-landforms there.

Land use changes affect the surface radiative budget and energy balance by changing the surface albedo, which generates radiative forcing, impacting the regional and global climate. To estimate the effect of land use changes on the surface albedo and climate change in a mountainous area with complex terrain, we obtained MODIS data, identified the spatial–temporal characteristics of the surface albedo caused by land use changes, and then calculated the radiative forcing based on solar radiative data and the surface albedo in the Qinling-Daba mountains from 2000 to 2015. The correlation between the land use changes and the radiative forcing was analyzed to explore the climate effects caused by land use changes on a kilometer-grid scale in the Qinling-Daba mountains. Our results show that the primarily land use changes were a decrease in the cultivated land area and an increase in the construction land area, as well as other conversions between six land use types from 2000 to 2015. The land use changes led to significant changes in the surface albedo. Meanwhile, the radiative forcing caused by the land use had different magnitudes, strengths, and occurrence ranges, resulting in both warming and cooling climate change effects.

Climate change exhibits great variation on different flanks and at different elevations in the same mountain range. To investigate the complexity of the geographic patterns of climate and phenology in the Qinling–Daba mountains (QDM), in the North–South transition zone of China, this study analyzed the spatiotemporal distribution characteristics of daily air temperature and precipitation data measured at 118 national weather stations (1969–2018). The principal findings were as follows. (1) Overall, a significant trend of warming was detected in all seasons over the past 50 years, with rates of increase of 0.347, 0.125, 0.200 and 0.302 °C/10a, in spring, summer, autumn and winter, respectively. Precipitation did not show significant variation at most stations in different seasons. (2) The rising rate of air temperature varied considerably between different flanks. Generally, air temperature change on northern flanks was greater than on southern flanks in all seasons. The tendency of air temperature change was greater in spring and winter than in summer and autumn on different flanks in the QDM. (3) The rate of increase in high-elevation regions was greater than in low-elevation regions in summer, autumn and winter, e.g., 0.440, 0.390 and 0.456 °C/10a at 3000–4000 m and 0.205, 0.218 and 0.303 °C/10a at 0–1000 m, respectively. However, in spring, the rate of increase in low-elevation regions were higher than in high-elevation regions, e.g., 0.369 °C/10a at 0–1000 m and 0.317 °C/10a at 3000–4000 m.

As the highest and most extensive plateau on earth, the Tibetan Plateau has strong thermo-dynamic effect, which not only affects regional climate around the plateau but also temperature and precipitation patterns of itself. However, due to scattered meteorological stations, its spatial precipitation pattern and, especially, the mechanism behind are poorly understood. The availability of spatially consistent satellite-derived precipitation data makes it possible to get accurate precipitation pattern in the plateau, which could help quantitatively explore the effect and mechanism of mass elevation effect on precipitation pattern. This paper made full use of TMPA 3B43 V7 monthly precipitation data to track the trajectory of precipitation and identified four routes (east, southeast, south, west directions) along which moisture-laden air masses move into the plateau. We made the assumption that precipitation pattern is the result interplay of these four moisture-laden air masses transportation routes against the distances from moisture sources and the topographic barriers along the routes. To do so, we developed a multivariate linear regression model with the spatial distribution of annual mean precipitation as the dependent variable and the topographical barriers to these four moisture sources as independent variables. The result shows that our model could explain about 70% of spatial variation of mean annual precipitation pattern in the plateau; the regression analysis also shows that the southeast moisture source (the Bay of Bengal) contributes the most (32.56%) to the rainfall pattern of the plateau; the east and the south sources have nearly the same contribution, 23.59% and 23.48%, respectively; while the west source contributes the least, only 20.37%. The findings of this study can greatly improve our understanding of mass elevation effect on spatial precipitation pattern.