<p>Climate model biases in the representation of albedo variations between land cover types contribute to uncertainties on the climate impact of land cover changes since pre-industrial times, and especially on the associated Radiative Forcing. The recent publications of new observation-based datasets offer opportunities to investigate these biases and their impact on historical albedo changes in simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Conducting such an assessment is however complicated by the non-availability of albedo values for specific land cover types, as well as the limited number of simulations isolating the land use forcing in CMIP. In this study, we demonstrate the suitability of a new methodology to extract the albedo of trees and crops/grasses in standard climate model simulations. We then apply it to historical runs from 13 CMIP5 models and compare the obtained results to satellite-derived reference data. This allows us to identify substantial biases in the representation of the albedo of trees, crops/grasses, and the albedo change due to the transition between these two land cover types in the analysed models. Additionally, we reconstruct the local albedo changes induced by historical conversions between trees and crops/grasses for 15 CMIP5 models. This allows us to derive estimates of the Radiative Forcing from land cover changes since pre-industrial times ranging between 0 and −0.22 W/m<sup>2</sup>, with a mean value of −0.07 W/m<sup>2</sup>. Constraining the albedo response to transitions between trees and crops/grasses from the models with satellite-derived data leads to an increase in this range, however after excluding two models with unrealistic conversion rates from trees to crops/grasses we obtain a revised model mean estimate of −0.11 W/m<sup>2</sup> (with individual model results between −0.04 and −0.16 W/m<sup>2</sup>). These numbers are at the lower end of the range provided by the IPCC AR5 (−0.15 ± 0.10 W/m<sup>2</sup>). The approach described in this study can be applied on other model simulations, such as those from CMIP6, especially as a diagnostic enabling the reproduction of the model evaluation part has been included in the ESMValTool v2.0.</p>