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Extended Spring Indices
WHAT ARE THE EXTENDED SPRING INDICES?
The Extended Spring Indices (SI-x) are models that scientists have developed to predict the “start of spring” at a particular location. Using historical observations of the timing of first leaf and first bloom in cloned lilacs and honeysuckles, as well as daily observations from nearby weather stations, scientists have been able to determine the weather conditions that precede general spring leaf-out for a wide range of plants. Like many other deciduous plants in temperate systems, these plants put on their leaves as temperatures warm in late winter and early spring. Recent work extended the original Spring Indices (SI-o; Schwartz 1997) from high latitude regions to subtropical environments by removing a chilling requirement (Schwartz et al 2013, Ault et al 2015).
Using the Extended Spring Index models, scientists can look at how much the start of spring has varied from one year to the next at a particular location, and whether recent years are dramatically different from the past or not. The models can also be used to forecast when selected plants might bloom or put on leaves in future years.
HOW ARE SCIENTISTS AND DECISION MAKERS USING THE SPRING INDICES?
The spring indices are now being used at the national level to understand the impacts of climate change:
- In the National Climate Assessment, primarily in the section on Seasonal Patterns in the Biodiversity Chapter.
- In the Global Change Information System, an ongoing information portal for key indicators in the National Climate Assessment.
- In the EPA's 2016 Climate Change Indicators Report.
- To document trends in National Park units
Learn more: Daily Accumulated Growing Degree Day and Spring Index Maps information sheet
Access Phenology Map Products
The provisional Spring Index First Leaf and First Bloom maps can be explored in the USA-NPN Visualization Tool.
Download map images (.png, .gif, .pdf, etc) or raster data files from the Geoserver Request Builder page.
Raster data files can also be accessed via the USA-NPN Geoserver instance. Available products include:
- Current day and 6-day forecast maps of Spring Index First Leaf and First Bloom, updated nightly - available for 2016 and 2017. Based on NOAA National Center for Environmental Prediction Real-Time Mesoscale Analysis and National Digital Forecast Database temperature products; 2.5km resolution
- Current year anomaly maps of First Leaf and First Bloom, generated by comparing current year maps to 30-year (1981-2010) averages. Based on PRISM temperature products; 4km resolution.
- First Leaf and First Bloom for each year, 1981-2015. Based on PRISM temperature products; 4km resolution
- First Leaf and First Bloom for each year, 1880-2013. Based on Berkeley Earth temperature products; 1 degree resolution
- 30-year (1981-2010) average maps for First Leaf and First Bloom dates
Map Products Documentation: Crimmins, T.M., R.L. Marsh, J. Switzer, M.A. Crimmins, K.L. Gerst, A.H. Rosemartin, and J.F. Weltzin. In press. USA National Phenology Network gridded products documentation. U.S. Geological Survey Open-File Report 2017–1003. DOI: 10.3133/ofr20171003.
Historical (1949-2010, based on Maurer temperature products) and modeled (1950-2100, based on BCCA) Spring Index layers are also available from the Silvis Lab at the University of Wisconsin.
How to Cite Spring Index Map Products
USA National Phenology Network. Year of dataset access. Name of data product, USA-NPN, Tucson, Arizona, USA. Data set accessed YYYY-MM-DD at http://dx.doi.org/10.5066/F7XD0ZRK
Ault, T. R., M. D. Schwartz, R. Zurita-Milla, J. F. Weltzin, and J. L. Betancourt (2015): Trends and natural variability of North American spring onset as evaluated by a new gridded dataset of spring indices. Journal of Climate 28: 8363-8378.
Schwartz, M. D. 1997. Spring index models: an approach to connecting satellite and surface phenology. Phenology in seasonal climates I, 23-38.
Schwartz, M. D., T. R. Ault, and J. L. Betancourt, 2013: Spring onset variations and trends in the continental United States: past and regional assessment using temperature-based indices. International Journal of Climatology, 33, 2917–2922, 10.1002/joc.3625.