Abstract. Evaluating land surface models (LSMs) using available observations is important to understand the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a land surface model (LSM), four essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, and surface albedo) via simulations with IPSL LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems), particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH-Forcing-DATA-ERA-Interim: WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation using satellite- and ground-based reference data show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape/meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing/snowmelt, temperate plant phenology, irrigation, as well as contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy-limited or water-limited) and snow-albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model.