Batteryless, energy harvesting sensing devices enable new applications and deployment scenarios with their promise of zero maintenance, long lifetime, and small size. These devices fail often and for variable lengths of time because of the unpredictability of the energy harvesting source; be it solar, thermal, RF, or kinetic, making prediction and planning difficult. This paper explores ways to make sense of energy harvesting behaviors. We take known energy harvesting datasets, and create a few of our own, then classify energy harvesting behavior into modes. Modes are periodic or repeated elements caused by systematic or fundamental attributes of the energy harvesting environment. We show the existence of these Energy Harvesting Modes using real world data and IV surfaces created with the Ekho emulator, and then discuss how this powerful abstraction could increase robustness and efficiency of design and development on intermittently powered and energy harvesting computing devices.