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Instance and temperature-dependent power variation has a direct impact on quality of sensing for battery-powered long-running sensing applications. We measure and characterize the active and leakage power for an ARM Cortex M3 processor and show that, across a temperature range of 20 -60, there is a 10% variation in active power, and a variation in leakage power. We introduce variability-aware duty cycling methods and a duty cycle (DC) abstraction for TinyOS which allows applications to explicitly specify the lifetime and minimum DC requirements for individual tasks, and dynamically adjusts the DC rates so that the overall quality of service is maximized in the presence of power variability. We show that variability-aware duty cycling yields a improvement in total active time over schedules based on worst case estimations of power, with an average improvement of across a wide variety of deployment scenarios based on the collected temperature traces. Conversely, datasheet power specifications fail to meet required lifetimes by 7%-15%, with an average 37 days short of the required lifetime of 1 year. Finally, we show that a target localization application using variability-aware DC yields a 50% improvement in quality of results over one based on worst case estimations of power consumption.