Phenological events are integrative and sensitive indicators of ecosystem processes that respond to climate, water and nutrient availability, disturbance, and environmental change. The seasonality of ecosystem processes, including biogeochemical fluxes, can similarly be decomposed to identify key transition points and phase durations, which can be determined with high accuracy, and are specific to the processes of interest. As the seasonality of different processes differ, it can be argued that the interannual trends and responses to environmental forcings can be better described through the fluxes’ own temporal characteristics than through correlation to traditional phenological events like bud break or leaf coloration. Here we present a global dataset of seasonality or phenological metrics calculated for gross primary productivity (GPP), ecosystem respiration (RE), latent heat (LE), and sensible heat (H) calculated for the FLUXNET2015 Dataset of about 200 sites and 1500 site years of data. The database includes metrics (i) on an absolute flux scale for comparisons with flux magnitudes and (ii) on a normalized scale for comparisons of change rates across different fluxes. Flux seasonality was characterized by fitting a single-pass double-logistic model to daily flux integrals, and the derivatives of the fitted time series were used to extract the phenological metrics marking key turning points, season lengths, and rates of change. Seasonal transition points could be determined with a 90 % confidence interval of 6–11 d for GPP, 8–14 d for RE, 10–15 d for LE, and 15–23 d for H. The phenology metrics derived from different partitioning methods diverged, at times significantly.
This Flux Seasonality Metrics Database (FSMD) can be accessed at the US Department of Energy’s (DOE) Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE, https://doi.org/10.15485/1602532; Yang and Noormets, 2020). We hope that it will facilitate new lines of research, including (1) validating and benchmarking ecosystem process models, (2) parameterizing satellite remote sensing phenology and PhenoCam products, (3) optimizing phenological models, and (4) generally expanding the toolset for interpreting ecosystems responses to changing climate.