In this study, we investigated spatial and temporal changes in precipitation over the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia domain, for present (1986–2005) and future (2031–2050) periods using the Regional Climate Model version 4 (RegCM4). Future meteorology produced by the Hadley Center Global Environmental Model version 2 coupled with the Atmosphere–Ocean (HadGEM2-AO) following global climate change scenarios (Representative Concentration Pathways (RCP) 4.5 and 8.5) was used as meteorological boundary conditions for the RegCM4. Six subregions (South Korea, North China, South China, Japan, Mongolia, and India) in the CORDEX East Asia domain were considered for analysis. The RegCM4 simulated spatial distributions of precipitation over East Asia with a correlation coefficient of 0.7 against Climate Research Unit data. The simulation skills of its temporal variability varied based on geographical regions and seasons, showing relatively poorer performance (underestimation in rainfall amount) in summer than in winter, in general. The future climate simulations by the RegCM4 presented that the East Asian continental regions will be warmer and more humid, leading to increased precipitation amounts, especially in the summer. The summer precipitation amount was projected to increase by about 5%, on average, over the East Asian domain, 5−15% in most subregions, and even higher (44% and 24%) in the South Korean region for the RCP 4.5 and 8.5 scenarios, respectively. It was also expected that heavy rainfall (> 50 mm/d) events may occur more frequently in the future possibly owing to meteorological changes that are favorable to convective heavy precipitation.

In this paper, the prediction skills of five ensemble methods for temperature and precipitation are discussed by considering 20 yr of simulation results (from 1989 to 2008) for four regional climate models (RCMs) driven by NCEP–Department of Energy and ECMWF Interim Re-Analysis (ERA-Interim) boundary conditions. The simulation domain is the Coordinated Regional Downscaling Experiment (CORDEX) for East Asia, and the number of grid points is 197 × 233 with a 50-km horizontal resolution. Three new performance-based ensemble averaging (PEA) methods are developed in this study using 1) bias, root-mean-square errors (RMSEs) and absolute correlation (PEA_BRC), RMSE and absolute correlation (PEA_RAC), and RMSE and original correlation (PEA_ROC). The other two ensemble methods are equal-weighted averaging (EWA) and multivariate linear regression (Mul_Reg). To derive the weighting coefficients and cross validate the prediction skills of the five ensemble methods, the authors considered 15-yr and 5-yr data, respectively, from the 20-yr simulation data. Among the five ensemble methods, the Mul_Reg (EWA) method shows the best (worst) skill during the training period. The PEA_RAC and PEA_ROC methods show skills that are similar to those of Mul_Reg during the training period. However, the skills and stabilities of Mul_Reg were drastically reduced when this method was applied to the prediction period. But, the skills and stabilities of PEA_RAC were only slightly reduced in this case. As a result, PEA_RAC shows the best skill, irrespective of the seasons and variables, during the prediction period. This result confirms that the new ensemble method developed in this study, PEA_RAC, can be used for the prediction of regional climate.

Surface urban heat island (SUHI) impacts control the exchange of sensible heat and latent heat between land and atmosphere and can worsen extreme climate events, such as heat waves. This study assessed SUHIs over three megacities (Seoul, Tokyo, Beijing) in East Asia using one-year (April 2011–March 2012) land surface temperature (LST) data retrieved from the Communication, Ocean and Meteorological Satellite (COMS). The spatio-temporal variations of SUHI and the relationship between SUHI and vegetation activity were analyzed using hourly cloud-free LST data. In general, the LST was higher in low latitudes, low altitudes, urban areas and dry regions compared to high latitudes, high altitudes, rural areas and vegetated areas. In particular, the LST over the three megacities was always higher than that in the surrounding rural areas. The SUHI showed a maximum intensity (10–13 °C) at noon during the summer, irrespective of the geographic location of the city, but weak intensities (4–7 °C) were observed during other times and seasons. In general, the SUHI intensity over the three megacities showed strong seasonal (diurnal) variations during the daytime (summer) and weak seasonal (diurnal) variations during the nighttime (other seasons). As a result, the temporal variation pattern of SUHIs was quite different from that of urban heat islands, and the SUHIs showed a distinct maximum at noon of the summer months and weak intensities during the nighttime of all seasons. The patterns of seasonal and diurnal variations of the SUHIs were clearly dependent on the geographic environment of cities. In addition, the intensity of SUHIs showed a strong negative relationship with vegetation activity during the daytime, but no such relationship was observed during the nighttime. This suggests that the SUHI intensity is mainly controlled by differences in evapotranspiration (or the Bowen ratio) between urban and rural areas during the daytime.

The impact of boundary conditions (BCs) on simulations of precipitation characteristics using the Regional Climate Model version 4 (RegCM4) driven by two BCs (ERA‐Interim: ERA, NCEP/DOE 2: R2) was investigated using 18 years (1989–2006) of simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia region. The RegCM4 adequately simulated spatial distribution of precipitation over the East Asia region and its temporal variations, irrespective of the BC used. It overestimated precipitation over the central region of the model domain except for India and South Korea and underestimated precipitation over equatorial ocean regions. The RegCM4's simulation skills were better in winter than in summer regardless of the BC, which can be related to the low spatial resolution (50 km) of the model, but detailed simulations of precipitation are significantly impacted by the BCs, although the impact varies with geographical location and season. Driven by ERA, the RegCM4 reproduces more precipitation in the central inland region of the model's domain and less precipitation in the southern coastal areas than when driven by R2. The differences occur because RegCM4 driven by ERA simulated the lower troposphere as being warmer and more humid and the upper troposphere as being cooler than that by R2. The transport of moisture into the central region of the model domain is also enhanced when the RegCM4 is driven by ERA. Although the RegCM4 accurately simulated the seasonal variations in precipitation, it failed to capture diurnal variations over South Korea, particularly when driven by R2, and overestimated and underestimated light precipitation ( 50 mm/day), respectively.

We developed land surface temperature (LST) retrieval algorithms based on the time of day and water vapor content using the Himawari-8/AHI (Advanced Himawari Imager) data, which is the Japanese next generation geostationary satellite. To develop the LST retrieval algorithms, we simulated the spectral radiance using the radiative transfer model (MODTRAN4) by applying the atmospheric profiles (SeeBor), diurnal variation of LST and air temperature, spectral emissivity of land surface, satellite viewing angle, and spectral response function of Himawari-8/AHI. To retrieve the LST from Himawari-8 data, a linear type of split-window method was used in this study. The Himawari-8 LST algorithms showed a high correlation coefficient (0.996), and a small bias (0.002 K) and root mean square error (RMSE) (1.083 K) between prescribed LSTs and estimated LSTs. However, the accuracy of LST algorithms showed a slightly large RMSE when the lapse rate was larger than 10 K, and the brightness temperature difference was greater than 6 K. The cross-validation of Himawari-8/AHI LST using the MODIS (Terra and Aqua Moderate Resolution Imaging Spectroradiometer) LST showed that annual mean correlation coefficient, bias, and RMSE were 0.94, +0.45 K, and 1.93 K, respectively. The performances of LST algorithms were slightly dependent on the season and time of day, generally better during the night (warm season) than during the day (cold season).

Bayesian changepoint analysis is applied to detect a change point in the 30-year (1976–2005) time series of the area-averaged annual maximum precipitation (A3MP) for the six accumulated time periods (1, 3, 6, 12, 24, and 48 h) over South Korea. Using noninformative priors, Bayesian model selection is performed by posterior probability through the Bayes factor, and the exact Bayes estimators of the parameters and unknown change point for the selected change model are obtained. To investigate the significance of the mean differences in the six A3MP between before and after the change point, posterior probability and 90% highest posterior density credible intervals are examined. The results show that a single change occurred around 1997 in the A3MP without regard to the accumulated time periods over South Korea. This is strongly consistent with the abrupt increases in the intensity and frequency of heavy precipitation after 1997. The A3MP after the change point (1997) significantly increased more than 15% compared with the A3MP before the change point. The intensification of A3MP resulted in a great increase of the annual total precipitation (about +18%), especially a greater increase of the heavy precipitation amount (+51%) and frequency (+48%) over South Korea.