非表示:
キーワード:
NET ECOSYSTEM PRODUCTIVITY; LEAF-AREA INDEX; SUB-ALPINE FOREST; DECIDUOUS FOREST; SPRING PHENOLOGY; TEMPERATE REGIONS; HIGH-ELEVATION;
SATELLITE DATA; CLIMATE-CHANGE; UNITED-STATESBiodiversity & Conservation; Environmental Sciences & Ecology; autumn senescence; carbon cycle; land surface model (LSM); leaf area
index (LAI); model error; North American Carbon Program (NACP);
phenology; seasonal dynamics; spring onset;
要旨:
Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 20002006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 145 g C m-2 yr-1 during the spring transition period and +75 +/- 130 g C m-2 yr-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphereatmosphere feedbacks and interactions in coupled global climate models.
