Spatiotemporal aggregation of solar irradiance occurs when a spatially distributed receiver (e.g. a PV generation facility) collects variable, geographically distributed irradiance and reduces it to a single electrical generation output. Models of this phenomenon exist and are designed to take variability from a single point irradiance monitor and predict how that variability will be reduced by aggregation. We have applied these models in reverse to assess whether the same models can be used to predict the variability of a single point measurement given an aggregate irradiance time series as an input. Results for an advection-based model show that this approach leads to overprediction of the high frequency variability due to overprediction of the site-to-site correlation, even during highly correlated advection conditions. While some modifications to the cloud advection model improve its performance, the wavelet variability model better represents the site pair decorrelation and produces superior results in representing the disaggregated time series and variability metrics. Further work may be warranted to further improve upon these efforts and enable reliable, transfer function-based downscaling of irradiance data.