B21G-0545
Spring onset variations and long-term trends from new hemispheric-scale products and remote sensing

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Xiaolu Li1, Toby Ault1,2, Raul Zurita-Milla3 and Mark D Schwartz4, (1)Cornell University, Department of Earth and Atmospheric Sciences, Ithaca, NY, United States, (2)Cornell University, Department of Earth and Atmospheric Science, Ithaca, NY, United States, (3)University of Twente, Geo-Information Science and Earth Observation, Enschede, Netherlands, (4)University of Wisconsin Milwaukee, Milwaukee, WI, United States
Abstract:
Spring onset is commonly characterized by plant phenophase changes among a variety of biophysical transitions and has important implications for natural and man-managed ecosystems. Here, we present a new integrated analysis of variability in gridded Northern Hemisphere spring onset metrics. We developed a set of hemispheric temperature-based spring indices spanning 1920-2013. As these were derived solely from meteorological data, they are used as a benchmark for isolating the climate system’s role in modulating spring “green up” estimated from the annual cycle of normalized difference vegetation index (NDVI). Spatial patterns of interannual variations, teleconnections, and long-term trends were also analyzed in all metrics. At mid-to-high latitudes, all indices exhibit larger variability at interannual to decadal time scales than at spatial scales of a few kilometers. Trends of spring onset vary across space and time. However, compared to long-term trend, interannual to decadal variability generally accounts for a larger portion of the total variance in spring onset timing. Therefore, spring onset trends identified from short existing records may be aliased by decadal climate variations due to their limited temporal depth, even when these records span the entire satellite era. Based on our findings, we also demonstrated that our indices have skill in representing ecosystem-level spring phenology and may have important implications in understanding relationships between phenology, atmosphere dynamics and climate variability.