Time of unprecedented climate for extreme temperature in winter averaged over Korea

For extreme temperature, we used climate extreme indices provided by CLIVAR (Climate and Ocean-Variability, Predictability, and Change) ETCCDI (Expert Team on Climate Change Detection and Indices). ETCCDI has provided 27 climate extreme indices not only with global reanalysis datasets but with CMIP5 simulations. The indices data are available on-line and the results with CMIP5 simulations were summarized by Sillmann et al. [2013]. For our analysis, we downloaded a monthly minimum of daily minimum surface air temperature (TNn) and a monthly maximum of daily maximum temperature (TXx). Among the CMIP5, 27 model results available on their website, we used 23 model results containing both of the TNn and TXx for all of the historical, RCP 4.5 and 8.5 experiments. Since our focus is on boreal-winter extreme temperature, we selected the lowest TNn and highest TXx among the three months of December-January-February every year from 1861 to 2005 for the historical simulation and from 2006 to 2099 for the RCP 4.5 and RCP 8.5 scenario. Before the spatial averaging over the analysis domain (34°N-43°N in latitude and 124°E-131°E in longitude including the Korean Peninsula), we had remapped all of the simulation data onto a 1.5° x 1.5° horizontal resolution. The time of unprecedented climate (TUC) for extreme temperature is defined in this study as the beginning year when the extreme temperature projected for the future climate scenarios exceed a critical value in all subsequent years during the RCP scenario runs. In this study, the critical value for extreme temperatures is specified as a 50-year return level which is rather arbitrary but refers to a rough estimate for the social lifetime of a man. One may find the return level empirically from historical data, but this study estimates it using a Generalized Extreme Value distribution function as suggested by Kharin et al. [2007]. Based on the CMIP5 historical simulation data using R, we obtained three parameters determining a GEV distribution for each model, respectively for TNn and TXx. The GEV distribution for each model and variable has been verified using a Q-Q (quantile-quantile) plot if it adequately describes the CMIP5 historical data. All of the models showed the Q-Q plot within the 95% confidence range (Figure 1a for GFDL-ESM2G TXx for an instance). Then, we estimated the return level from the distribution and TUC from the RCP scenario runs for the wintertime TNn and TXx averaged over Korea.针对极端气温研究，我们采用了气候与海洋变率、可预报性与变化计划（CLIVAR）下属的气候变化检测与指数专家小组（ETCCDI）提供的气候极端指数。ETCCDI共提供了27项气候极端指数，其数据源不仅包含全球再分析数据集，还涵盖耦合模式比较计划第五阶段（CMIP5）的模拟结果。该指数数据集可在线获取，CMIP5模拟结果的相关总结由Sillmann等人于2013年完成。本研究选取了日最低地表气温月最小值（TNn）与日最高气温月最大值（TXx）两项指数进行分析。在该网站公开的27个CMIP5模式结果中，我们最终选取了同时包含TNn与TXx的23个模式结果，用于历史试验、典型浓度路径4.5（RCP4.5）及典型浓度路径8.5（RCP8.5）试验的分析。 鉴于本研究聚焦北半球冬季极端气温，我们针对历史模拟试验（时段为1861年至2005年）、RCP4.5与RCP8.5情景试验（时段为2006年至2099年），分别从每年12月—次年1月—2月这三个月中选取最低TNn与最高TXx。在对分析区域（纬度范围34°N至43°N，经度范围124°E至131°E，包含朝鲜半岛）进行空间平均前，我们已将所有模拟数据重映射至1.5°×1.5°的水平分辨率。 本研究将极端气温相关的前所未有的气候发生时间（Time of Unprecedented Climate, TUC）定义为：在RCP情景试验中，未来气候情景预估的极端气温在后续所有年份均超过临界值的起始年份。 本研究将极端气温的临界值设定为50年重现期水平，该设定虽带有一定主观性，但参考了人类社会生命周期的粗略估算。重现期水平可通过历史数据以经验方法求得，本研究则参照Kharin等人2007年的建议，采用广义极值分布（Generalized Extreme Value, GEV）函数进行估算。基于R语言处理的CMIP5历史模拟数据，我们分别为每个模式的TNn与TXx获取了确定GEV分布所需的三个参数。我们采用分位数-分位数图（quantile-quantile plot, Q-Q）对各模式、各变量对应的GEV分布能否合理拟合CMIP5历史模拟数据进行了验证。所有模式的Q-Q图均落在95%置信区间内（例如GFDL-ESM2G模式TXx的Q-Q图见图1a）。最终，我们基于该分布估算了重现期水平，并通过RCP情景试验得到了朝鲜半岛区域平均的冬季TNn与TXx对应的TUC。