To better understand land-atmosphere interactions and the associated effects on the predictability of weather and climate, it is highly desirable to extend the off-line parameter estimation of land-surface models to coupled applications. Using the NCAR Single-column Community Climate Model (SCCM) and IOP data sets from the ARM-CART SGP site, this study conducted a multi-objective sensitivity analysis to investigate the influence of land-atmosphere interactions on model sensitivities. The results show that, as expected, the land surface-atmosphere interactions have significant influences on the model parameter sensitivities, thus greatly affecting parameter estimation in the locally coupled environment. For calibration in the locally coupled environment, both land-surface and atmospheric variables/parameters were involved. In brief, the results show that atmospheric parameters are of critical importance for the calibration of a coupled land surface-atmosphere model, and atmospheric forcing variables generally contain more useful information for calibration than land-surface fluxes/variables. In the coupled environment, step-wise calibration schemes, with land and atmospheric parameters optimized successively in the offline and coupled modes, respectively, appear to be superior to the single-step calibration schemes which optimize land and atmospheric parameters simultaneously in the coupled environment, in that the former require less computational resources. In addition, the results also show that better calibration effects can be achieved in the partially decoupled environment by replacing model-generated precipitation and net radiation with the corresponding observations to drive the land part of the model, indicating the dominant importance of precipitation and radiation for the two-way interactions within the coupled system.