## 1 Introduction
For an ecosystem, the water use efficiency (WUE) is defined by the ratio between carbon sequestration and water consumption, and it can be used to describe the tight link between carbon and water cycles within the ecosystem. Compared to NPP and ET, WUE is more sensitive to the ambient changes such as changes in precipitation, temperature, and CO\({}_{2}\) concentration. So it has been widely used to detect the responses of the ecosystems to historical changes in climate and CO\({}_{2}\) concentration. Under the global climate change, the annual WUE shows an increasing trend over middle and high northern latitudes [6; 29]. Previous studies showed that the increasing CO\({}_{2}\) contributes to the increase of WUE by influencing the stomatal closure [30]. In addition, the climate change can enhance the effects of CO\({}_{2}\) on WUE [29]. Moreover, the WUEs of different ecosystems respond differently in climatechange [31; 32]. However, previous studies mainly focused on the WUE responses to historical changes in climate and CO\({}_{2}\). So it is very urgent for us to know about the WUE dynamics of the ecosystems in the arid area where the ecosystems are sensitive to the ambient changes. The ecosystem process-based modelling is one of the best choices for predicting the carbon and water dynamics in global carbon and water cycles [33; 34]. The arid ecosystem model (AEM) is selected to explore the dynamics of the NPP and ET in the Tianshan North Slope under multiple RCP scenarios at a horizontal resolution of 25 km. Compared to the popular ecosystem models, the AEM model can represent the unique root and canopy structure of the xeric vegetation and ecophysiological processes [35]. Additionally, it has been well validated and successfully applied to study the impacts of the climate change and CO\({}_{2}\) fertilization effect on the carbon and water cycles of the arid ecosystems in Central Asia, including the Tianshan North Slope area [3; 4; 14; 17; 36; 37]. The AEM model is forced by dynamically downscaled data from the RCP2.6, RCP4.5, and RCP8.5 at 25 km resolution from 2006 to 2055 during which significant climate change has been detected [38]. These three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5), which were predicted to lead to radiative forcing levels of 2.6, 4.5, and 8.5 W/m\({}^{2}\) by the end of the 21 st century, present the low, medium, and high greenhouse gas emissions, respectively [39]. The CO\({}_{2}\) concentration reaches the maximum around 2052 and keeps at a high level from 2053 to 2055 under RCP2.6, while it increases continuously from 2006 to 2055 under RCP4.5 and RCP8.5. These three RCP scenarios are widely used to study future climate change and its ecological effects [40; 41; 42]. Previous studies mainly focused on the historical effects of changes in climate and/or CO\({}_{2}\) concentration on one or two elements (NPP, ET, and WUE) at a large scale [12; 17; 18; 25; 36]. How the fragile ecosystems in the arid area will respond to the future changes in climate and CO\({}_{2}\) concentration remains unclear. In this study, we take the arid area in Tianshan North Slope as our study area to explore the dynamics of NPP, ET, and WUE for different arid ecosystems under multiple RCP scenarios (RCP2.6, RCP4.5, and RCP8.5) in the near future. The Tianshan North Slope, one of the economic centers in the northwest China, is an important region in the Silk Road. It is an ideal region to explore the climate change and CO\({}_{2}\) fertilization effects for it contains various vegetation types, changing from the desert to the mountains, and is sensitive to the climate change [43; 44]. Therefore, it is of particular importance to study the response of the arid ecosystem in Tianshan North Slope to the near future changes in climate and CO\({}_{2}\) concentration. In this study, we first analyzed the temporal and spatial patterns of the climate change in the Tianshan North Slope region under different RCP scenarios. Then, the spatial and temporal dynamics in NPP and ET of the region are detected.