Climate change inspector with intentionally biased bootstrapping (CCIIBB ver. 1.0) - methodology development

Abstract

The outputs from general circulation models (GCMs) provide useful information about the rate and magnitude of future climate change. The temperature variable is more reliable than other variables in GCM outputs. However, hydrological variables (e.g., precipitation) from GCM outputs for future climate change possess an uncertainty that is too high for practical use. Therefore, a method called intentionally biased bootstrapping (IBB), which simulates the increase of the temperature variable by a certain level as ascertained from observed global warming data, is proposed. In addition, precipitation data were resampled by employing a block-wise sampling technique associated with the temperature simulation. In summary, a warming temperature scenario is simulated, along with the corresponding precipitation values whose time indices are the same as those of the simulated warming temperature scenario. The proposed method was validated with annual precipitation data by truncating the recent years of the record. The proposed model was also employed to assess the future changes in seasonal precipitation in South Korea within a global warming scenario as well as in weekly timescales. The results illustrate that the proposed method is a good alternative for assessing the variation of hydrological variables such as precipitation under the warming condition.