Statistically downscaled future precipitation for the Luquillo Mountains, Puerto Rico

This dataset contains climate predictions that serve as the basis for the analysis in Ramseyer et al. (2019), which projected a trend toward drier conditions in eastern Puerto Rico during the mid- and late-21st century. The analysis was informed by computing nine atmospheric variables, which had been shown by previous research to related to precipitation in Puerto Rico (Ramseyer and Mote 2016) from four GCMs. These nine variables were used to train an artificial neural network (ANN) to predict the binary occurrence of a wet (>= 5 mm of precipitation) versus dry (<5 mm) day using in-situ daily precipitation observations from El Verde Field Station in northeast Puerto Rico. The nine atmospheric variables used to train the ANN were: 1000- 850-, 700-, and 500-hPa daily specific humidity, 1000–700-hPa bulk wind shear (BWS), the Gálvez-Davison Index (GDI), and the GDI's three component terms (the column buoyancy index, mid-level warming index, and a trade-wind inversion index). These same nine variables were then extracted on a daily basis from four GCMs for the eastern Caribbean early rainfall season (April-July) between 2041-2060 and 2081-2100, and fed through the ANN. These data are the daily predicted values of wet (1) or dry (0) conditions for each of the four GCMs in the ensemble. Because ERS total precipitation at El Verde is strongly correlated with the percentage of ERS dry days (R2=0.95 for years with <10% missing data), the GCM predictions were used to estimate future ERS precipitation using the following formula:
ERS precipitation (mm) = 3373-37.6*(ERS dry-day percentage)
Applying this formula to each of the GCM dry-day projections yielded an ensemble mean ERS precipitation total of 771 mm by 2041-2060 and 974 mm by 2081-2100. See Ramseyer et al. (2019) for a complete description of the neural network and its predictions.
Ramseyer, C., P. Miller, and T. Mote, 2019: Future precipitation variability during the early rainfall season in the El Yunque National Forest. Science of The Total Environment, 661, 326-336.
Ramseyer, C.A., Mote, T.L. Atmospheric controls on Puerto Rico precipitation using artificial neural networks. Clim Dyn 47, 2515–2526 (2016). https://doi.org/10.1007/s00382-016-2980-3

Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support was provided by the USDA Forest Service International Institute of Tropical Forestry and the University of Puerto Rico.