Friday, 1 June 2012: 8:45 AM
Press Room (Omni Parker House)
Vegetation phenology plays significant roles especially in surface energy, moisture, and CO2 fluxes. However, the determination of the phenological variations is one of the critical challenges in dynamic vegetation modeling due to lack of the theoretical backgrounds. Here we investigate dominant meteorological factors controlling inter-annual and inter-seasonal variability of 25-year (1982-2006) leaf area indices (LAI) from AVHRR. Our research domains include four natural vegetation regions and two cultivated vegetation regions in the mid-latitudes of Northern Hemisphere. Regardless of regions, an employment of EOF analysis finds two leading independent modes accounting for 60-90% of the inter-annual variability of LAI; the first mode (EOF1) is associated with the amplitude of LAI seasonal variation, and the second mode (EOF2) with the skewness of LAI seasonal variation between late spring and early fall. Correlation analysis of the principle components of EOF1 and EOF2 with preceding meteorological variables such as temperature (Ts) and dewpoint temperature (Td) at surface, precipitation (P) and their combinations highlights three results: 1) EOF1 is negatively correlated with dewpoint depression (=Ts-Td) over all of the study regions, while it is correlated with both springtime precipitation (P) (positively) and temperature (Ts) (negatively) over only the natural vegetation regions, 2) the dependency of EOF2 on meteorological variables also exist, but more complicated depending on regions, and 3) different characteristics in the responses of vegetation to meteorological conditions are observed in between the natural vegetation regions and cultivated vegetation regions, which is likely due to well-watered condition through advanced irrigation system over crop lands. We discuss possible underlying mechanisms behind the LAI-precursory meteorological relations that may depend on environments, vegetation types and the degree of human intervention. The present analytic results with space-viewed LAI will greatly improve our understanding of the interactive role of the biosphere with the atmosphere in future climate change situation.
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