## 1 Introduction  
According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), global warming and greenhouse gas increases are the main characteristics of current and future global climate change [1], which affects the dynamics of terrestrial ecosystem carbon cycles. As two important indicators of vegetation productivity, gross primary production (GPP) and net primary production (NPP) represent the carbon gain abilities of ecosystems [2]. The ratio of NPP and GPP, termed the carbon use efficiency (CUE) of ecosystems [3, 4, 5], determines the carbon sink function, carbon cycle and turnover rate of vegetation [6], indicating the efficiency of the ecosystem in absorbing carbon from the atmosphere into a terrestrial biomass. CUE is considered to be an important parameter for formation of the ecosystem carbon pool [7], reflecting the ability to transfer carbon from the atmosphere to a potential carbon sink and determining the carbon distribution of NPP and respiration [8], which are two basic components of GPP. In addition, as an indispensable parameter in the ecosystem model, a slight modification of the CUE value may lead to drastic changes in the model's prediction ability [7], especially when the research scale is enlarged from a landscape to a terrestrial ecosystem. Recent CUE research has focused on the change characteristics of CUE in the past decade on a regional or global scale, and this research was mainly based on remote sensing data, flux observation data and model simulation evaluations [9; 10; 11; 12]. Some studies showed that the CUE value of terrestrial ecosystems is a relatively constant, ranging from 0.47-0.50 [13; 14; 15]. There are also some studies showing that the CUE changes with changes in the environment [16; 17]. Increasing evidence shows that CUE will be affected by environmental factors, including vegetation type, ecosystem type, climate conditions, nitrogen deposition, management conditions, site fertility and stand age, and changes dynamically at different time scales [4; 17; 18; 19; 20; 21]. For example, [PERSON], et al. [17] found that the CUE ranged from 0.23-0.80 and increased with increasing forest age. Based on MODIS data and NPP estimation data from a forest field survey, [PERSON] and [PERSON] [22] found that CUE decreases with increasing temperature and precipitation but increases with increasing latitude and altitude, which indicated that CUE is a substantial variable. [PERSON] et al. [6] found that there is a linear relationship between the average annual temperature and CUE. The increase in temperature will lead to an increase in respiratory enzyme activity and the enhancement of plant respiration [5]. In future long-term series, there are few studies on change predictions of CUE. In the context of future climate change, the response of CUE to climate and the resulting changes remain controversial. With model development being increasingly improved to be more accurate, research using model data to analyze CUE is increasing. In previous studies, there is much research on the analysis of CUE characteristics by using eddy flux data and remote sensing data. The eddy covariance method is being applied by the global change community on the increasingly longer time scales [23; 24]. The eddy flux data and remote sensing data can be processed using a variety of methods in confluence with ground and other data sources to analyze and verify the value and trend of CUE. In addition, some studies have proposed that the conclusions drawn from a single model dataset are more sensitive to systematic bias and errors, which makes it very important to use multiple data sets for research [25; 26]. Moreover, using model forecast models and averaging the results can result in more reliable results by balancing any biases in any single model [25; 26; 27; 28; 29]. The Qinghai-Tibet Plateau is very sensitive to climate change; however, there is no conclusion on the future response of CUE to climate change in this region.