Bayesian consideration of GCM simulations for rainfall quantile estimation: Uncertainty from GCMs and RCP scenarios

Global climate model (GCM) simulations are generally used in the estimation procedures regarding future rainfall quantiles. However, the use of GCM simulations brings several problems, such as a coarse spatial resolution and an intermodel difference in the simulation results. To overcome these problems, this study proposed a Bayesian method for using the GCM simulations as new information regarding the future. A total of seven GCMs that contained past and future simulations were considered in all four different representative concentration pathway (RCP) scenarios. The hourly rainfall data of Seoul, Korea, were used as the observed data. In the results, first, the rainfall quantiles estimated using the GCM simulations were found to be much smaller than those of the observed data, and they were intermixed, depending on the GCM and RCP scenarios. For example, the observed rainfall quantile of the observed data was estimated to be 318.3 mm for the return period of 50 years, but those for the GCM simulations were estimated to lie in a wide range from the minimum 79.2 mm (NorESM1-M for the RCP 6.0) to the maximum 293.5 mm (BCC-CSM1.1 for the RCP 8.5). Second, the difference became much smaller when the Bayesian method was applied. Although a consistent trend could not be found, all the estimated increase ratios of the future rainfall quantiles were found to lie in a reasonable range, -8.3 to + 16.95%. Third, the estimated increase ratio of the future rainfall quantiles showed that the increase or decrease patterns were all intermixed depending on the GCM and RCP scenarios. A stronger RCP scenario did not lead to an increase of the rainfall quantiles. In most GCMs, the RCP 4.5 scenario was found to lead to a greater increase of the rainfall quantiles than the other scenarios. Thus, a consideration of the greatest possible number of GCM simulations, along with all four of the RCP scenarios, could be a safe way to minimize the uncertainty potential of climate change considerations.