Vegetation changes play a vital role in modifying local temperatures although, until now, the climate feedback effects of vegetation changes are still poorly known and large uncertainties exist, especially over Central Asia. In this study, using remote sensing and re-analysis of existing data, we evaluated the impact of vegetation changes on local temperatures. Our results indicate that vegetation changes have a significant unidirectional causality relationship with regard to local temperature changes. We found that vegetation greening over Central Asia as a whole induced a cooling effect on the local temperatures. We also found that evapotranspiration (ET) exhibits greater sensitivity to the increases of the Normalized Difference Vegetation Index (NDVI) as compared to albedo in arid/semi-arid/semi-humid regions, potentially leading to a cooling effect. However, in humid regions, albedo warming completely surpasses ET cooling, causing a pronounced warming. Our findings suggest that using appropriate strategies to protect vulnerable dryland ecosystems from degradation, should lead to future benefits related to greening ecosystems and mitigation for rising temperatures.

The knowledge about impacts of changes in precipitation regimes on terrestrial ecosystems is fundamental to improve our understanding of global environment change, particularly in the context that heavy precipitation is expected to increase according to the 5th Intergovernmental Panel on Climate Change (IPCC) assessment. Based on observed climate data and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) satellite-derived normalized difference vegetation index (NDVI), here we analyzed the spatio-temporal changes in grassland NDVI, covering 1.64 × 10⁶ km², in northern China and their linkages to changes in precipitation and temperature during the period 1982–2011. We found that mean growing season (April–October) grass NDVI is more sensitive to heavy precipitation than to moderate or light precipitation in both relatively arid areas (RAA) and relatively humid areas (RHA), whereas the sensitivities of grass NDVI to temperature are comparable to total precipitation in RHA. Heavy precipitation showed the strongest impacts in more than half of northern China (56%), whereas impacts of light precipitation on grass NDVI were stronger in some areas (21%), mainly distributed in northwestern China, a typical arid and semi-arid area. Our findings suggest that responses of grasslands are divergent with respect to changes in precipitation intensities.

As the main anthropogenic greenhouse gas that contributes most to global warming, CO₂ plays an important role in climate changes in Central Asia. Due to the lack of studies of near-surface CO₂ in this region, we first confirmed the applicability of the near-surface Greenhouse Gases Observing Satellite (GOSAT) CO₂ data in Central Asia using atmospheric CO₂ concentration data from nine ground-based station observations. We then analyzed the temporal and spatial distributions of the near-surface CO₂ concentrations in Central Asia and their controlling factors using statistical analysis methods. The results show that the near-surface CO₂ concentrations are high in the western part of this region and low in the east. From June 2009 to May 2013, the near-surface CO₂ concentrations increased gradually, with the highest value being in spring and the lowest in autumn. The temporal distribution of CO₂ concentrations is mainly affected by photosynthesis, respiration, and heating. The combined effect of terrestrial ecosystems and CO₂ diffusion by wind is responsible for the higher near-surface CO₂ concentration in the northern, western, and southwestern areas of the five Central Asian countries compared to the central, eastern, and southern areas, and energy consumption and wind are the major factors that affect the heterogeneity of the spatial distribution of the CO₂ concentrations in Xinjiang.

Terrestrial vegetation plays a crucial role in governing the land surface energy budget, which in turn drives the climate of the Earth. The climatic feedbacks of vegetation changes are different across vegetation greenness gradients and climatological conditions, which has not been studied clearly. In this study, we used remote sensing and re‐analysis datasets to investigate global climatic feedbacks of vegetation changes on the surface air temperature (SAT). The Common Land Model (CoLM) was also used to reproduce land‐atmosphere energy processes and to explain the climatic feedbacks based on the biophysical mechanisms. Our results show that the sensitivity of SAT to the increase in vegetation greenness presents a convergent relationship. The sign and magnitude of the sensitivity are determined by the background climate conditions. The convergence sensitivity is regulated by the corresponding variations of satellite‐observed evapotranspiration (ET) and albedo, and the associated partitioning of surface energy between sensible and latent heat fluxes simulated by the CoLM. The exploration of the global climatic feedbacks of vegetation changes in this work will be helpful to identify future climate change required for the development of appropriate strategies for mitigating global warming.

Estimations of above-ground biomass (AGB) at various spatial and temporal scales remain a large uncertainty when quantifying the global carbon cycle. In this work, we assessed the ability of the Enhanced Vegetation Index (EVI), obtained from the Moderate-Resolution Imaging Spectroradiometer, for estimating AGB across multiple land cover types in China. Our results indicate that, with the exception of mixed forests at the site level, annual integrated EVI (iEVI) can be used for estimating AGB. We were also able to reduce the permissible margin of error for the total AGB of mixed forests at the biome level. Estimated total AGB for China was determined to be 21.19 Pg (1 Pg = 1 10¹² Kg), with 69.8% attributed to forests and 31.2% to non-forests. Overall, we concluded that iEVI has great potential for dynamically estimating spatial AGB. Here, we also describe the correspondence between analysed parameters.