Whereas engineering wake models can be used to efficiently provide energy production estimates for wind turbine sites, recent studies indicate the importance of a <em>global</em> blockage effect becomes manifest for larger assets. This global blockage effect is caused by site-scale interactions with the atmospheric boundary layer, and results in a wind speed deficit upstream of the asset. This paper presents an efficient and accurate parametrized global blockage model which integrates into existing engineering wake models. The central idea behind this global blockage model is to interpret the wind farm site as a parametrized porous object, subjected to an ambient flow field. We calibrate and benchmark our model through high-resolution LES model data for a representative offshore site using a calibrated wake deficit shape parameter. Results show significant improvements in turbine-level energy production prediction accuracy when compared to results obtained without any blockage model and results obtained with the local <em>self-similar</em> blockage model. The parametrized global blockage model has a significantly lower computational footprint compared to local blockage models. We conclude that not taking (global) blockage into account sufficiently can yield a tendency to overestimate the strength of the turbine wake deficit effects when calibrating wake deficit shape parameters. Finally, we note that the spatial distribution of (global) blockage and wake deficit errors can easily lead to error cancellation when aggregating over binned wind directions.