We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an ‘extra’ day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Summary: Nonstructural carbohydrate reserves support tree metabolism and growth when current photosynthates are insufficient, offering resilience in times of stress. We monitored stemwood nonstructural carbohydrate (starch and sugars) concentrations of the dominant tree species at three sites in the northeastern United States. We estimated the mean age of the starch and sugars in a subset of trees using the radiocarbon (¹⁴C) bomb spike. With these data, we then tested different carbon (C) allocation schemes in a process‐based model of forest C cycling. We found that the nonstructural carbohydrates are both highly dynamic and about a decade old. Seasonal dynamics in starch (two to four times higher in the growing season, lower in the dormant season) mirrored those of sugars. Radiocarbon‐based estimates indicated that the mean age of the starch and sugars in red maple (Acer rubrum) was 7–14 yr. A two‐pool (fast and slow cycling reserves) model structure gave reasonable estimates of the size and mean residence time of the totalNSCpool, and greatly improved model predictions of interannual variability in woody biomass increment, compared with zero‐ or one‐pool structures used in the majority of existing models. This highlights the importance of nonstructural carbohydrates in the context of forest ecosystem carbon cycling.