Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as regime leave a unique footprint in a forest's spatial biomass patterns. We trained a model on millions of computer simulations to learn this link. By applying this model to detailed satellite biomass, we could read these patterns to infer the disturbance regime globally, helping make climate projections more accurate.