Probing the past 30-year phenology trend of US deciduous forests

Phenology is experiencing dramatic changes over deciduous forests in the USA. Estimates of trends in phenology on the continental scale are uncertain, however, with studies failing to agree on both the magnitude and spatial distribution of trends in spring and autumn. This is due to the sparsity of in situ records, uncertainties associated with remote sensing data, and the regional focus of many studies. It has been suggested that reported trends are a result of recent temperature changes, though multiple processes are thought to be involved and the nature of the temperature forcing remains unknown. To date, no study has directly attributed long-term phenological trends to individual forcings across the USA through integrating observations with models. Here, we construct an extensive database of ground measurements of phenological events across the USA, and use it to calibrate and evaluate a suite of phenology models. The models use variations of the accumulative temperature summation, with additional chilling requirements for spring phenology and photoperiod limitation for autumn. Including a chilling requirement or photoperiod limitation does not improve model performance, suggesting that temperature change, especially in spring and autumn, is likely the dominant driver of the observed trend during the past 3 decades. Our results show that phenological trends are not uniform over the contiguous USA, with a significant advance of 0.34 day yr(-1) for the spring budburst in the east, a delay of 0.15 day yr(-1) for the autumn dormancy onset in the northeast and west, but no evidence of change elsewhere. Relative to the 1980s, the growing season in the 2000s is extended by about 1 week (3-4 %) in the east, New England, and the upper Rocky Mountains forests. Additional sensitivity tests show that intraspecific variations may not influence the predicted phenological trends. These results help reconcile conflicting reports of phenological trends in the literature, and directly attribute observed trends to long-term changes in temperature.