The dataset contains historical simulations and future projections of multiple variables from the extended spring indices (SIx) and (false spring exposure indices). The SIx variables included are the first leaf index (FLI), first bloom index (FBI), damage index (DMG), and day of last freeze in the spring (lastfreeze). The false spring indices include an indicator for early or late false spring occurrence (EFS and LFS), and the Early False Spring Exposure Index (EFSEI) and Late False Spring Exposure Index (LFSEI). The definitions of all variables are provided in Wootten et al. (2024) and cited papers in that study. This data was also created using projected data from the Climate Projections Evaluation Project (C-PrEP) also produced out of the South Central CASC and NOAA GFDL. Models, downscaling techniques, training data, time periods, and scenarios are the same as in the C-PrEP dataset. The FLI, FBI, and DMG variables are four dimensional variables with dimensions of longitude, latitude, time, and plant. These SIx variables are generally calculated for three indicator plant species and the mean of the three is also provided. For each of these three values in the plant dimension, the mean is the first value in that dimension followed by the values with respect to the individual plant species. The SIx variable lastfreeze is three dimensions only (longitude, latitude, time). EFS and LFS are also three dimensions, but are values of 0 or 1, where a value of 1 indicates that an early or late false spring occurred at the particular time. The time dimension is year in all cases. Finally, EFSEI and LFSEI are two dimensional variables (longitude and latitude) indicating the percentage of years in the period that an early false spring or late false spring occurred.
