Phenology of photosynthesis in winter-dormant temperate and boreal forests: Long-term observations from flux towers and quantitative evaluation of phenology models

We found minimal evidence for long-term change in StartGPP, EndGPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier StartGPP (1.3-2.5 days degrees C-1) or later EndGPP (1.5-1.8 days degrees C-1, depending on forest type and month). Finally, we tested whether existing phenological models could predict StartGPP and EndGPP.

For ENF forests, air temperature- and daylength-based models provided best predictions for StartGPP, while a chilling-degree-day model was best for EndGPP. The root mean square errors (RMSE) between predicted and observed StartGPP and EndGPP were 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).