Quantification of Uncertainty in Projections of Extreme Daily Precipitation

Abstract

Projections of extreme precipitation are of considerable interest in a range of design and management applications. These projections, however, can exhibit uncertainty that requires quantification to provide confidence to any application they are used in. This study assesses the uncertainty in projected extreme daily precipitation, separated into model, scenario, and ensemble components using the square root error variance (SREV) rationale. For this, 45 projections of daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used, consisting of multiple global circulation models and their ensemble members, for a range of Representative Concentration Pathways, allowing assessment across land-covered areas worldwide. It is found that the uncertainty in dry regions is significantly higher compared to wet regions, raising concerns regarding infrastructure design for the future in arid parts of the world. It is also found that the climate scenarios and initialization contribute significantly to the overall uncertainty, compared to contributions for more nonextreme precipitation simulations. This finding has implications in how design precipitation extremes ought to be projected into the future, with greater attention being paid on a broader selection of emission scenarios and initializations than is the case with projections of nonextreme precipitations. Plain Language Summary Projections of extreme precipitation are of considerable interest in a range of design and management problems. These projections, however, are subject to considerable uncertainty, which requires quantification before they can be put to use. This study quantifies the uncertainty in projected daily extreme precipitations worldwide and attributes this uncertainty into distinct dominant sources using a range of extreme daily precipitation simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The results indicate that extreme precipitation exhibits greater uncertainty in dry regions, such as North Africa and the Middle East, compared to wetter regions. Results also indicate that emission scenarios and model initializations strongly contribute to the uncertainty in the maximum extreme precipitation, more so than for nonextreme precipitation.