Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost-vegetation-climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near-surface air temperature in summer (June-August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude- and biome-dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (similar to 30% of climatic signal) on SSO with varying elevation- and canopy height-dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections. Climate change has caused substantial vegetation growth that was detected by satellite observations (greening) over northern permafrost regions. However, the consequences or feedbacks of vegetation greening remain largely unknown, hindering the understanding of near-surface thermal dynamics and bringing considerable uncertainty in model projections. Here we aimed to decipher the biophysical impact of permafrost greening on the summer surface offset (SSO), which is an indicator of permafrost degradation. We found latitude- and biome-dependent patterns of SSO changes and divergent responses of SSO to greening. Increases in satellite-observed leaf area index lead to higher SSO in relatively cold and wet areas but lower SSO in warm-dry regions. Biophysical mechanisms associated with surface albedo, evapotranspiration, and SM can help explain various effects of greening on SSO. Our results highlight greening feedbacks on the thermal dynamics of permafrost with climate warming, calling for the improvement of current projections. Vegetation greening impacts the thermal dynamics of permafrost surface Biophysical effects of greening on surface offset could be related to surface albedo, evapotranspiration, and soil moisture

Study region: The Tibetan Plateau Study focus: Evapotranspiration (ET) plays a critical role in the water balance, energy budget, and carbon cycle. However, the variations, trends, and controls of ET on the Tibetan Plateau (TP) are poorly understood because of uncertainties in ET estimates and sparse observations. In this study, the variations in ET and its components and their drivers and controls in the TP were analyzed at seasonal and annual scales during 1982-2015. New hydrological insights for the region: Spatially, the multiyear mean annual ET decreased from the southeastern to northwestern TP. Canopy transpiration (Ec) was the main component of ET (52.7%), followed by soil evaporation (Es) (34.4%) and interception (Ei) (10.7%). Regionally, the averaged ET and its components increased significantly at the seasonal and annual scales. Spatially, the controlling factor for ET changed from water to energy as the climatic zones transferred from aridity to humidity. The annual ET was controlled by soil moisture (SM) in arid and semi-arid zones, whereas Ta was the dominant factor in the other regions. The increased annual Es and Ei were primarily caused by SM, while the annual Ec was determined by Ta. In addition, NDVI played a certain role in regulating the annual Ec and Ei variations. This study improves our understanding of hydrological processes and water resource management under global climate change.

The Qinghai-Tibet Plateau (QTP) has a fragile ecosystem that is sensitive to climate change. Due to the amplifying effect of climate change, the QTP has experienced rapid warming and shifting precipitation in recent decades, profoundly impacting the local ecosystem. However, the specific details of how vegetation responds to these changes were unclear, and the corresponding contributions were poorly quantified. Here, we employed an elastic net regression model to investigate the sensitivity of vegetation to climate factors across multiple time scales and various seasons. The vegetation activity was represented by the enhanced vegetation index (EVI), while climate change was represented by temperature, precipitation, photosynthetically active radiation (PAR), and soil moisture fraction (SMF). During 2000-2020, approximately 50 % of the QTP area showed greening, mainly concentrated in the northern region. Climate change explained approximately 70 % of the variation in vegetation during the growing season, 39 % in spring and 66 % in autumn. Grasslands exhibited the highest sensitive to climate change, with a relative contribution of 83 %, followed by mixed forests (70 %), forests (53 %) and deserts (52 %). Both temperature and precipitation significantly affected vegetation, with relative contributions of 29 % and 22 %, respectively, during the growing season. PAR and SMF had less impact on vegetation, with relative contributions of 8 % and 12 %, respectively. In the greening region, precipitation (26 %) was more important for vegetation growth compared to temperature (25 %). These findings emphasize the importance of precipitation on vegetation on the QTP, providing valuable insights for improving regional ecosystem assessment model and promoting the restoration of fragile ecosystems.

As an important component of the climate system, permafrost responds significantly to climate change, and its impact on the ecosystem cannot be ignored. In this study, we analyzed the temporal and spatial variation trends of the normalized difference vegetation index (NDVI) in Arctic permafrost regions and revealed the correlation between the active-layer thickness (ALT), soil temperature, and NDVI change. Using the partial correlation method, we assessed the ecological regulation service of permafrost to the ecosystem. The results showed that both the average annual maximum and summer NDVI values in the Arctic region followed a significant increasing trend from 1982 to 2015. The average correlation coefficient (ACC) between Arctic NDVI and ALT was 0.35, followed by the ACC (0.33) between NDVI and soil temperature at 7-28 cm depth, and had a lower ACC (0.31) at 0-7 cm ALT. When the precipitation and snow water equivalent (SWE) remained unchanged, the partial correlation between NDVI and ALT was 0.711, which was a significant positive correlation. It also showed that permafrost degradation was the dominant factor controlling Arctic NDVI increase, whereas precipitation and SWE had little effect. The study revealed the impact of permafrost on NDVI change, deepened our understanding of the importance of permafrost degradation for ecosystem services, and effectively filled the gap that tundra ecosystem services value has been ignored in the global ecological service value assessment.

As an important component of the climate system, permafrost responds significantly to climate change, and its impact on the ecosystem cannot be ignored. In this study, we analyzed the temporal and spatial variation trends of the normalized difference vegetation index (NDVI) in Arctic permafrost regions and revealed the correlation between the active-layer thickness (ALT), soil temperature, and NDVI change. Using the partial correlation method, we assessed the ecological regulation service of permafrost to the ecosystem. The results showed that both the average annual maximum and summer NDVI values in the Arctic region followed a significant increasing trend from 1982 to 2015. The average correlation coefficient (ACC) between Arctic NDVI and ALT was 0.35, followed by the ACC (0.33) between NDVI and soil temperature at 7-28 cm depth, and had a lower ACC (0.31) at 0-7 cm ALT. When the precipitation and snow water equivalent (SWE) remained unchanged, the partial correlation between NDVI and ALT was 0.711, which was a significant positive correlation. It also showed that permafrost degradation was the dominant factor controlling Arctic NDVI increase, whereas precipitation and SWE had little effect. The study revealed the impact of permafrost on NDVI change, deepened our understanding of the importance of permafrost degradation for ecosystem services, and effectively filled the gap that tundra ecosystem services value has been ignored in the global ecological service value assessment.

Over the past several decades, various trends in vegetation productivity, from increases to decreases, have been observed throughout Arctic-Boreal ecosystems. While some of this variation can be explained by recent climate warming and increased disturbance, very little is known about the impacts of permafrost thaw on productivity across diverse vegetation communities. Active layer thickness data from 135 permafrost monitoring sites along a 10 degrees latitudinal transect of the Northwest Territories, Canada, paired with a Landsat time series of normalized difference vegetation index from 1984 to 2019, were used to quantify the impacts of changing permafrost conditions on vegetation productivity. We found that active layer thickness contributed to the observed variation in vegetation productivity in recent decades in the northwestern Arctic-Boreal, with the highest rates of greening occurring at sites where the near--surface permafrost recently had thawed. However, the greening associated with permafrost thaw was not sustained after prolonged periods of thaw and appeared to diminish after the thaw front extended outside the plants' rooting zone. Highest rates of greening were found at the mid-transect sites, between 62.4 degrees N and 65.2 degrees N, suggesting that more southernly sites may have already surpassed the period of beneficial permafrost thaw, while more northern sites may have yet to reach a level of thaw that supports enhanced vegetation productivity. These results indicate that the response of vegetation productivity to permafrost thaw is highly dependent on the extent of active

Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988-2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 x 104 km2) and from alpine grassland to alpine meadow (17.43 x 104 km2) during 1988-2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influ-encing factors affecting vegetation greening on the QTP are precipitation (q -statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human develop-ment strategies.

Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent decades. Most studies suggest that wetter and warmer climatic conditions would release low temperature constraints and stimulate alpine vegetation growth. Other studies suggest that climate warming might inhibit vegetation growth by increasing soil moisture depletion in the southern QTP. Most of previous studies have relied on vegetation indices derived from satellite observations to retrieve large-scale vegetation changes, and the uncertainty of vegetation indices makes it difficult to accurately characterize the vegetation trends on the QTP. Here, we developed a deep learning algorithm in the Google Earth Engine (GEE) platform to accurately map the land use/cover change (LUCC) on the QTP, and then infer vegetation gain and loss and their drivers during the period 1988-2018. The vegetation on the QTP experienced rapid greening, which was dominated by transitions from bareland to alpine grassland (27.45 x 104 km2) and from alpine grassland to alpine meadow (17.43 x 104 km2) during 1988-2018. Furthermore, although human activities influence vegetation succession at the local scale, the dominant influ-encing factors affecting vegetation greening on the QTP are precipitation (q -statistic = 23.87 %) and temperature (q-statistic = 11.01 %). A 30-year time series analysis clarified the specific dynamics of vegetation on the QTP, thus contributing to the understanding of the response mechanisms of alpine vegetation under climate change and providing a reference for the formulating of reasonable ecological protection policies and human develop-ment strategies.

Broad-scale changes in arctic-alpine vegetation and their global effects have long been recognized and labeled one of the clearest examples of the terrestrial impacts of climate change. Arctic-alpine dwarf shrubs are a key factor in those processes, responding to accelerated warming in complex and still poorly understood ways. Here, we look closely into such responses of deciduous and evergreen species, and for the first time, we make use of high-precision dendrometers to monitor the radial growth of dwarf shrubs at unprecedented temporal resolution, bridging the gap between classical dendroecology and the underlying growth physiology of a species. Using statistical methods on a five-year dataset, including a relative importance analysis based on partial least squares regression, linear mixed modeling, and correlation analysis, we identified distinct growth mechanisms for both evergreen (Empetrum nigrum ssp. hermaphroditum) and deciduous (Betula nana) species. We found those mechanisms in accordance with the species respective physiological requirements and the exclusive micro-environmental conditions, suggesting high phenotypical plasticity in both focal species. Additionally, growth in both species was negatively affected by unusually warm conditions during summer and both responded to low winter temperatures with radial stem shrinking, which we interpreted as an active mechanism of frost protection related to changes in water availability. However, our analysis revealed contrasting and inter-annually nuanced response patterns. While B. nana benefited from winter warming and a prolonged growing season, E. hermaphroditum showed high negative sensitivity to spring cold spells after an earlier growth start, relying on additional photosynthetic opportunities during snow-free winter periods. Thus, we conclude that climate-growth responses of dwarf shrubs in arctic-alpine environments are highly seasonal and heterogenic, and that deciduous species are overall likely to show a positive growth response to predicted future climate change, possibly dominating over evergreen competitors at the same sites, contributing to the ongoing greening trend.

Rapid climate warming has widely been considered as the main driver of recent increases in Arctic tundra productivity. Field observations and remote sensing both show that tundra greening has been widespread, but heterogeneity in regional and landscape-scale trends suggest that additional controls are mediating the response of tundra vegetation to warming. In this study, we examined the relationship between changes in vegetation productivity in the western Canadian Arctic and biophysical variables by analyzing trends in the Enhanced Vegetation Index (EVI) obtained from nonparametric regression of annual Landsat surface reflectance composites. We used Random Forests classification and regression tree modelling to predict the trajectory and magnitude of greening from 1984 to 2016 and identify biophysical controls. More than two-thirds of our study area showed statistically significant increases in vegetation productivity, but observed changes were heterogeneous, occurring most rapidly within areas of the Southern Arctic that were: (1) dominated by dwarf and upright shrub cover types, (2) moderately sloping, and (3) located at lower elevation. These findings suggest that the response of tundra vegetation to warming is mediated by regional- and landscape-scale variation in microclimate, topography and soil moisture, and physiological differences among plant functional groups. Our work highlights the potential of the joint analysis of annual remotely sensed vegetation indices and broad-scale biophysical data to understand spatial variation in tundra vegetation change.