• Phenological controls over aerodynamic resistance ( R ah ) were investigated. • R ah exhibits significant seasonal variability across a wide range of sites. • These shifts in R ah were caused by phenology in some ecosystems. • Accounting for variation in kB −1 is important for improving predictions of H . Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux ( H ). We evaluated aerodynamic resistance and surface roughness lengths for momentum ( z 0m ) and heat ( z 0h ) using the kB −1 parameter (ln( z 0m / z 0h )). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer ( R ah ). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with R ah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in R ah were not linked to green-up. Our findings highlight that decreases in kB −1 are an important control over R ah , explaining > 50% of seasonal variation in R ah across most sites. Decreases in kB −1 during green-up are likely caused by increasing z 0h in response to higher leaf area index. Accounting for seasonal variation in kB −1 is key for predicting H as well; assuming kB −1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.

• Phenological controls over aerodynamic resistance ( R ah ) were investigated. • R ah exhibits significant seasonal variability across a wide range of sites. • These shifts in R ah were caused by phenology in some ecosystems. • Accounting for variation in kB −1 is important for improving predictions of H . Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in “big leaf” models of sensible heat flux ( H ). We evaluated aerodynamic resistance and surface roughness lengths for momentum ( z 0m ) and heat ( z 0h ) using the kB −1 parameter (ln( z 0m / z 0h )). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer ( R ah ). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with R ah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in R ah were not linked to green-up. Our findings highlight that decreases in kB −1 are an important control over R ah , explaining > 50% of seasonal variation in R ah across most sites. Decreases in kB −1 during green-up are likely caused by increasing z 0h in response to higher leaf area index. Accounting for seasonal variation in kB −1 is key for predicting H as well; assuming kB −1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales.

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.