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Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start_GPPand End_GPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO₂to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d⁻¹) than ENF (1.10% d⁻¹), and their active season length (End_GPP–Start_GPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start_GPP, End_GPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start_GPP(1.3–2.5 days °C⁻¹) or later End_GPP(1.5–1.8 days °C⁻¹, depending on forest type and month). Finally, we tested whether existing phenological models could predict Start_GPPand End_GPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start_GPP, while a chilling‐degree‐day model was best for End_GPP. The root mean square errors (RMSE) between predicted and observed Start_GPPand End_GPPwere 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days). 

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.