Energy balance models (EBMs) comprise a simplified physical approach to climate modeling relative to more advanced general circulation model (GCM) counterparts. In particular, EBMs simplify numerous radiative features of Earth's surface-atmosphere system through a singular parameterization of albedo which defines the proportion of reflected to total incoming solar radiation. The literature provides some basic albedo models. However, these models are known to be limited in their real-world application. This paper takes a <em>model identification</em> approach to predicting the rate of change in top-of-atmosphere (TOA) albedo given real climate data. First, we design and implement several regression models for the prediction of the rate of change in albedo. Namely, we consider a variety of polynomial and kernel ridge regression (KRR) solutions, comparing these predictors to benchmark functions from the EBM literature. Second, we evaluate these predictors through a series of case studies in which we justify the selection of simulated temporal resolutions. Third, we quantify the performance of individual albedo predictors and test the impact of coupled temperature and albedo updates. Finally, this work finds that KRR with a radial basis function (RBF) kernel can provide a robust predictor of albedo, but requires careful consideration of tuning, spatial resolution, and input feature selection. In short, the paper delivers a thorough assessment of the limitations of albedo model identification and prediction.