Technical note: Including hydrologic impact definition in climate projection uncertainty partitioning: a case study of the Central American mid-summer drought

Abstract

The Central American mid-summer drought (MSD) is a defining precipitation pattern within the regional hydrologic system linked to water and food security. Past changes and future projections in the MSD show a strong sensitivity to how the MSD is defined. The question then arises as to whether multiple definitions should be considered to capture the uncertainty in projected impacts as climate warming continues and a need to understand the impacts on regional hydrology persists. This study uses an ensemble of climate models downscaled over Nicaragua using two methods, global warming levels up to +3 °C, and different definitions of the MSD to characterize the contributions to total uncertainty of each component. Results indicate that the MSD definition contributes the least to total uncertainty, explaining 5 %–9 % of the total. At the same time, evidence suggests a shift of the MSD to later in the year. As warming progresses, total uncertainty is increasingly dominated by variability among climate models. While not a dominant source of uncertainty, downscaling method adds approximately 8 %–18 % to total uncertainty. Future studies of this phenomenon should include an ensemble of climate models, taking advantage of archives of downscaled data to adequately capture uncertainty in hydrologic impacts. These findings provide critical guidance for future research aiming to inform water planning and adaptation efforts in the region: by identifying the dominant sources of uncertainty across warming levels, this framework helps prioritize where to focus modeling and monitoring efforts. In particular, water resource managers can use this information to design adaptive strategies that are robust to model spread and shifts in seasonal precipitation timing, rather than to definitional ambiguity. The projection uncertainty partitioning approach could serve as a template to quantify the relative importance of uncertainty for projections of other precipitation-driven phenomena in different geographic contexts.