Using Spring Phenological Indices with Downscaled Climate Projections: Case Study with the South-Central United States

Spring phenological indices are critical to representing the timing of many relevant events for ecosystems and species including the first leaf and first bloom. In addition, false springs (when a freeze occurs following the first leaf or first bloom) can have detrimental effects on both agriculture and ecosystems. Climate model output is increasingly used for impact assessments, including assessments on potential impacts to ecosystems involving first leaf, first bloom, and false springs and projections created with statistical downscaling are frequently used for these impact assessments. This project examines the potential changes of first leaf, first bloom, and false springs to the multiple components used to create the climate projections using the extended spring indices (SI-x), using the Climate Projections Evaluation Project (C-PrEP). C-PrEP that incorporates multiple statistical downscaling techniques, training datasets, and global climate models and covers the south-central United States. This analysis uses the C-PrEP projections for representative concentration pathway (RCP) 8.5 as inputs to calculate the SI-x indices and the False Spring Exposure Index (FSEI) during historical (1981-2005) and future (2070-2099) periods. Results suggest different downscaling techniques can have an impact on projected changes for the FSEI, particularly for the LFSEI. Results also suggest that there are particular characteristics of historical observations in various locations that can lead to unique challenges for using SI-x and false spring indices for climate impact assessments. This talk will discuss the calculation of these indices from climate projections, the sensitivity to the downscaling approach, and location characteristics that present unique challenges to using projections of the SI-x and false springs. User’s assessing future changes in first leaf, first bloom, or false springs for impacts to ecosystems or agriculture should carefully consider the downscaling technique used and the historical characteristics of a location when assessing these projected changes.