Current vegetation models hold constant the dynamics of seasonal biospheric changes, neglecting annual variability. One of these features, the spring “green wave” (onset of leafing or “grean-up”) in midiatitudes, is an example of the complex feedback loop between the atmosphere and plant communities. The green wave responds to the atmosphere, but, in turn, leafing causes rapid changes in the surface-layer energy budget. Annual variability tends to obscure the magnitude of the green wave's effects on conventional climatic records. When meteorological data are “normalized” to the green wave (averaged relative to first-leaf phonology data rather than to calendar date), an actual break in the spring maximum temperature curve can be detected. This project examined many surface-level meteorological variables (at standard shelter height, representing surface-layer conditions) during the time of spring leafing in order to detect other changes and to further analyze the cause, extent, and persistence of these annual variations. The results show that the green wave occurs in conjunction with discontinuities (signals) in lower-atmospheric lapse rate, surface vapor pressure, relative humidity, visibility, and V wind component. These signals are consistent with the start of plant photosynthetic activity, seasonal shifts in atmospheric circulation patterns, and physical changes in the nature of the surface layer. A parameter from simple green-wave models that identifies seasonal changes in the lower atmosphere may be useful in operational forecasting and GCM simulations that link the atmosphere and biosphere.