Large uncertainties exist for the indirect radiative forcing produced by aerosol-cloud interactions, and these uncertainties cast some doubt on the reliability of future climate models. Within these models there are a large number of parameters which cannot be related directly to observed quantities, and these parameters are often chosen in an ad hoc manner to provide the best match of historical climate simulations with satellite observations. There are two approaches I hope to take in order to constrain these parameters. The first approach to help narrow down these uncertainties would be to create a hierarchy of parameters based on the direct impact of each parameter on the radiative forcing output of a climate model. This can be done through a sensitivity analysis, relating the response of key cloud-based observables (such as liquid water path, cloud optical depth, and cloud droplet number concentration), to changes in various model parameters. However, when performed as a blanket analysis on a global scale, this may produce spurious results. This is because responses may differ between cloud regimes, owing to different atmospheric conditions both surrounding and within the cloud. Therefore, it is proposed to begin with a regime-based study, where model output is processed to classify clouds into various regimes. This can be done in the standard way, by binning clouds by cloud-top-pressure and cloud optical depth (with resultant regimes roughly analogous to standard cloud classifications). Alternatively, there may exist other classification methods (novel or otherwise) that may provide significant findings. Afterwards, a sensitivity analysis can then be performed on a regime-by-regime basis. This analysis would hopefully reveal which model parameters provide the strongest feedbacks to net-radiative forcing, allowing focus to be directed towards these variables in question. On top of this, said parameters may also have strong effects on other processes such as precipitation efficiency, which can in turn have net effects on inbound radiation. The second approach that could be taken would be to reverse-engineer parameter constraints using available satellite data. The climate model that will be used is the MET office HadGEM3 model, which comes with the CFMIP Observational Simulator Package (COSP). This package provides simulated model diagnostics under the simulated climatic conditions in the model, as would be retrieved from various satellites such as MODIS. By comparing the output from COSP with available satellite data from the last 20-30 years, it may be possible to identify any large disparities and work backwards to find the root cause of these differences. Although this process requires an in-depth knowledge of the algorithms that COSP is comprised of, I believe it would be possible to develop this process under the time constraints. By understanding the main cause of the disparities, the model can be specifically tuned to match the satellite observations, by placing emphasis on the parameters deemed most important by the reverse-engineering, rather than using a trial and error approach.