Simulating climate warming scenarios with intentionally biased bootstrapping and its implications for precipitation
Abstract. The outputs from GCMs provide useful information about the rate and magnitude of future climate change. The temperature variable is the most reliable of the 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), that simulates the increase of the temperature variable by a certain level as ascertained from observed global warming data is proposed. In addition, precipitation data was resampled by employing a block-wise sampling technique associated with the temperature simulation. In summary, a warming temperature scenario is simulated and the corresponding precipitation values whose time indices are the same as the one 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 time scale. 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.