Added value of a priori bias correcting dynamically downscaled data for application to species distribution models - a case study for coastal British Columbia

Dipti Hingmire , Jennifer McHenry, Julia Velletta, Vanessa Valenti, Julia K. Baum 

Predicting changes in species distributions under climate change relies on high-quality
climate projections. In this case study of coastal British Columbia, we prepare and
evaluate two sets of climate data - a priori bias corrected and non bias corrected
dynamically downscaled historical projections of Community Earth System Model 2
simulations. We compare these datasets with downscaled ERA5 reanalysis focusing on
commonly used inputs to species distribution models (SDM), namely, bioclimatic
(BIOCLIM) variables and climate extreme indices. Our results show improvements for
mean BIOCLIM variables when a priori bias correction is applied. However, modest
improvements are observed in terms of variability and extreme indices. Overall, our
findings suggest that a priori bias corrected dynamically downscaled climate projections
provide more accurate input to SDMs, and thus can improve the reliability of these
important ecological models.