摘要：Forecasting ecological responses to climate change represents a challenge to the
ecological community because models are often site-specific and climate data are
lacking at appropriate spatial and temporal resolutions. We use a case study
approach to demonstrate uncertainties in ecological predictions related to the driving
climatic input data. We use observational records, derived observational datasets
(e.g. interpolated observations from local weather stations and gridded data products) and
output from general circulation models (GCM) in conjunction with site based
phenology models to estimate the first flowering date (FFD) for three woody
flowering species. Using derived observations over the modern time period, we
find that cold biases and temperature trends lead to biased FFD simulations for
all three species. Observational datasets resolved at the daily time step result
in better FFD predictions compared to simulations using monthly resolution.
Simulations using output from an ensemble of GCM and regional climate models over
modern and future time periods have large intra-ensemble spreads and tend to
underestimate observed FFD trends for the modern period. These results indicate that
certain forcing datasets may be missing key features needed to generate accurate
hindcasts at the local scale (e.g. trends, temporal resolution), and that standard
modeling techniques (e.g. downscaling, ensemble mean, etc) may not necessarily
improve the prediction of the ecological response. Studies attempting to simulate
local ecological processes under modern and future climate forcing therefore need
to quantify and propagate the climate data uncertainties in their simulations.
