Body weight affects many components of demography and therefore has consequences for management and conservation. Growth is typically affected by environmental variables such as population density and weather. Terrestrial demographers often model growth in body size using time-series of weights, whereas fish demographers more often use length-at-age models such as the Von Bertalanffy or Gompertz growth models. However, typical length-at-age models are less flexible for quantifying transient variability in growth. To use length-at-age models more flexibly, we converted weights to lengths and modeled growth increments from Von Bertalanffy and Gompertz growth models. We compared these methods with regression models of weight-at-age time-series. Regression methods we considered included glmmTMB, glmmLasso, and a state-space model that controls for observation error. Then we evaluated the ability of these models to forecast because our goal is to project future fish weights under different climate change scenarios.