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Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night-time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in the response of NDVI to changes in day- versus night-time temperatures. For instance, while higher daytime temperature (T_max) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between T_max and NDVI is much larger in spring (41% of area in boreal zone – total area 12.6 × 10⁶ km²) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in T_max, increases in T_max tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night-time temperature (T_min) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day- and night-time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Lastly, understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation-climate models

Abstract The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index ( LAI ) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO ₂ concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend ( LAI _GS ) estimated by satellite datasets (average trend of 0.0070 yr ⁻¹ , ranging from 0.0035 yr ⁻¹ to 0.0127 yr ⁻¹ ), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI _GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI _GS ( P  < 0.05), and marginally significantly ( P  = 0.07) correlated with the residual of LAI _GS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO ₂ concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.