On the uncertainty of phenological responses to climate change and its implication for terrestrial biosphere models
1European Commission, DG-JRC, Institute for Environment and Sustainability, Climate Change and Risk Management Unit, Ispra, VA, Italy
2Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
3Département de Géographie, Université de Montréal, Montréal, QC H2V 2B8, Canada
4Harvard Forest, Petersham, MA 01366, USA
Abstract. Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate systems through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. Land surface models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we analyzed the Harvard Forest phenology record to investigate and characterize the sources of uncertainty in phenological forecasts and the subsequent impacts on model forecasts of carbon and water cycling in the future. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species with 12 phenological models of different complexity to predict leaf bud-burst.
The evaluation of different phenological models indicated support for spring warming models with photoperiod limitations and, though to a lesser extent, to chilling models based on the alternating model structure.
We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO2 emissions vs. low CO2 emissions scenario). Parameter uncertainty was the smallest (average 95% CI: 2.4 day century−1 for scenario B1 and 4.5 day century−1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 day century−1 in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied somewhat among models (±7.7 day century−1 for A1fi, ±3.6 day century−1 for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 day °C−1 and 5.2 day °C−1 depending on model structure.
We quantified the impact of uncertainties in bud-burst forecasts on simulated carbon and water fluxes using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of the seasonality of processes, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and evapotranspiration (ET) of 9.6% and 2.9% respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET.
For phenology models, differences among future climate scenarios represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, uncertainties related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization.