Quantifying climate internal variability using an hourly ensemble generator over South Korea

Identifying climate internal variability (CIV) generated by non-linear interactions and feedbacks among many components of the climate system is essential and challenging because of its irreducible and unpredictable characteristics. A range of studies have addressed this issue; however, these studies focused on the first order moments of few representative climate variables at relatively larger spatial and temporal scales. To investigate the magnitude and the spatial pattern of CIV relevant at finer spatial (point) and temporal (hourly) scales, CIV is assessed over a 30-year period in South Korea by analyzing 100-member ensemble generated using an hourly weather generator and a bootstrapping approach. Statistics addressing the first and second order moments, occurrences, and extremes are successfully verified at various temporal scales. The CIV is then estimated by the ‘detrended’ and ‘differenced’ methods for the four metrics proposed at different scales that signify rainfall volume, maxima, and occurrence. Consequently, the implications of this study are the following: (1) the estimation of CIV using bootstrapped ensembles often fails to represent the proper uncertainty range, resulting in high chances of underestimating extreme statistics, such as the maximum rainfall depth; (2) regardless of which of the two methods is used, no significant difference in the CIV estimation is observed; and (3) a temporal scale-dependency is observed for the proposed metrics used to identify the magnitude and the seasonal pattern of the CIV—the utility of an hourly time series and its associated extreme properties deserves significant attention. Ultimately, the spatial mapping and grouping of CIV will provide valuable information to identify which regions have high variability compared to climatological norms and thus are more vulnerable to extremes, and will serve as a guide for planning adaptation and mitigation measures against future extreme events.